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Suggested Citation:"2 Review of NASA Model." National Research Council. 2012. Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation. Washington, DC: The National Academies Press. doi: 10.17226/13343.
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2

Review of NASA Model

INTRODUCTION

NASA’s proposed space radiation cancer risk assessment model for radiation-induced cancer in astronauts is described in the 2011 NASA report Space Radiation Cancer Risk Projections and Uncertainties—2010 (Cucinotta et al., 2011). That 2011 NASA report, as it is called hereafter, is divided into discussions of the various components of the proposed model, including the discussion of the key data sets that either were used to develop the component or are intended as inputs to calculations made by the model. It should be noted that the components of the proposed model are not, in every case, described in the 2011 NASA report. Instead they are incorporated by reference to earlier reports, such as the “BEIR VII Phase 2” report from the National Research Council’s (NRC’s) Committee on Biological Effects of Ionizing Radiation (BEIR) (NRC, 2006) and NCRP Report No. 132, Radiation Protection Guidance for Activities in Low-Earth Orbit (NCRP, 2000).

This chapter closely follows the organization of the 2011 NASA report (Cucinotta et al., 2011), with each of the individual model components first reviewed separately below, followed by a review of the integrated model in the chapter’s final text section entitled “Integration and Completeness of the Model.” This review of the integrated model summarizes the present report’s major conclusions and recommendations regarding the model, including those pertaining to the individual components discussed in the chapter. When recommending research that could help improve future versions of the model, the committee primarily considered research that could feasibly be carried out in the next 5 years, since the NASA model is generally updated every 5 years. While suggestions appear throughout the discussions in the report, the most important recommendations and conclusions are highlighted in bold in the text, and the majority of these appear in the final section of this chapter. This report does not attempt to duplicate the extensive background and descriptive material contained in the 2011 NASA report, but rather it refers the reader, as appropriate, either to specific sections of the NASA report or to the prior reports incorporated by NASA’s proposed model.

SPACE RADIATION ENVIRONMENTS AND TRANSPORT MODELS

The assessment of cancer risk due to space radiation begins with defining the external (or ambient) radiation environments. Data describing these environments are inputs to transport calculations to obtain the local radiation environment, modified by spacecraft and body shielding, at tissues of concern. Galactic cosmic rays (GCRs)

Suggested Citation:"2 Review of NASA Model." National Research Council. 2012. Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation. Washington, DC: The National Academies Press. doi: 10.17226/13343.
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and particles from solar particle events (SPEs) are two major components1 of the space radiation environment that pose radiation risk to astronauts during space missions away from the protective zone of Earth’s magnetic field. The GCR and SPE environments in the solar system have a strong correlation with the approximately 11-year solar cycle.

Galactic Cosmic Rays

Overview

Note that much of the material in this Overview section is contained in Heliophysics—Evolving Solar Activity and the Climates of Space and Earth, in the chapter by J.R. Jokipii (2010).2

Galactic cosmic rays constitute a major part of the space radiation environment near Earth. GCRs are very energetic charged particles (electrons and atomic nuclei) that are believed to be accelerated by vast, spheroidal blast waves from supernova explosions that propagate in the interstellar gas. The accelerated cosmic rays enter the heliosphere on their way to the inner solar system and Earth. In the process they are changed, and so understanding their transport is essential to understanding the space radiation environment.

The heliosphere is a vast spheroidal cavity in the local interstellar plasma, some 150 to 200 astronomical units (AU) in size, created by a supersonic, radial flow of plasma, called the solar wind, that flows outward from the Sun. The spatial scale of the heliosphere is determined by both the Sun and the back pressure of the surrounding interstellar plasma and magnetic field. Far from the Sun, the outward-flowing solar wind is spread over such a large volume that it can no longer continue out into the surrounding interstellar plasma. Because the wind is flowing supersonically (faster than waves can propagate), the supersonic flow ends at a spheroidal shock wave, which is called the heliospheric termination shock, where the flow changes suddenly to a subsonic (slower than the wave speed) outward flow.

The interstellar plasma is moving at about 26 km/sec relative to the heliosphere, pushing it in on one side. Beyond the termination shock, the solar plasma continues to flow outward, but it is deflected and eventually turns to flow in the same direction as the interstellar plasma, forming a large, trailing, heliospheric tail. The interstellar medium also contains neutral atoms, and these also play a role in the interaction of the heliosphere with the interstellar medium, although the effects are small and may be neglected.

Energetic particles including cosmic rays pervade the heliosphere, as they do all regions of low-enough density in the universe. The energetic particles are in four basic types: galactic cosmic rays, anomalous cosmic rays, interplanetary energetic particles, and solar energetic particles. This discussion concentrates primarily on galactic cosmic rays. They come from the galaxy, where they are thought to be accelerated by supernova blast waves. They envelop the heliosphere with a very nearly constant, isotropic bath. The particles are then partially excluded from the inner parts of the heliosphere. Therefore, their intensity reflects the varying properties of the heliosphere. GCRs have a typical energy of 1 GeV and are present continuously, but fluctuate on a variety of timescales. Solar cosmic rays are produced sporadically by the Sun, at considerably lower energies than those of the galactic particles (see Figure 2.1). Their spectrum is also a much more rapidly decreasing function of energy. The time-averaged intensity of these two types of cosmic rays, as a function of energy, is illustrated in Figure 2.1, where the solar particles are a solar-cycle average. The average spectrum over time is therefore dominated by the GCRs, although for short periods (hours to days) the solar particles can be quite intense.

The intensity of GCRs in the inner solar system is observed to vary with time over a wide variety of timescales. The time variations of galactic particles are due to variations in the solar wind and its entrained magnetic field, which are accessible to direct measurement. There exists a generally accepted physical model that can account quantitatively for these modulations.

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1Trapped-particle models are not covered here because they contribute very little to the organ dose for missions aboard the International Space Station or missions to the Moon or to Mars.

2J.R. Jokipii, The heliosphere and cosmic rays, Chapter 9 in Heliophysics—Evolving Solar Activity and the Climates of Space and Earth (C.J. Schrijver and G.L. Siscoe, eds.), Cambridge University Press, New York, 2010. Copyright © 2010 Cambridge University Press. Used with permission.

Suggested Citation:"2 Review of NASA Model." National Research Council. 2012. Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation. Washington, DC: The National Academies Press. doi: 10.17226/13343.
×

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FIGURE 2.1 The observed intensity of cosmic rays at Earth orbit at quiet times. Shown are galactic cosmic-ray protons and helium. The dashed line is a time average of solar energetic particles. SOURCE: J.R. Jokipii, The heliosphere and cosmic rays, Chapter 9 in Heliophysics—Evolving Solar Activity and the Climates of Space and Earth (C.J. Schrijver and G.L. Siscoe, eds.), Cambridge University Press, New York, 2010. Copyright © 2010 Cambridge University Press. Reprinted with permission.


It is reasonably certain that cosmic-ray variations on timescales of less than about 50,000 years must be caused by changes in the heliosphere resulting from changes in the Sun. Interstellar variations over longer time periods can be caused either by the motion of the solar system through the interstellar medium or by transient changes in the interstellar cosmic-ray intensity caused by changes in local interstellar conditions such as by a supernova blast wave. In addition, the heliospheric structure, and hence its effects on cosmic rays, can be affected by changes in the interstellar medium caused by, for example, interstellar clouds.

The largest observed periodic variation is the variation of GCRs in anti-phase with the 11-year sunspot cycle. The variation of the GCR intensity over the past five sunspot cycles is illustrated in Figure 2.2. Note the very obvious 11-year cyclic variation and the alternating shapes of successive cosmic-ray maxima. At this time it is not completely understood how solar activity changes the interplanetary medium to produce the observed temporal variations. There are four principal elements:

1. Co-rotating, high-speed streams produce nearly periodic variations at the solar rotation period of approximately 27 days.

2. Coronal mass ejections (CMEs) and high-speed solar wind streams combine to produce large-scale, long-lived structures (known as global merged interaction regions, or GMIRs) of enhanced magnetic field that propagate out to the termination shock and into the heliosheath. There is a strong correlation between the rate of CMEs and sunspot numbers that have been observed over periods of high and low solar activity. From this one may conclude that the almost 400 years of sunspot observations provide a useful tool for studying the levels of solar activity over that time.

Suggested Citation:"2 Review of NASA Model." National Research Council. 2012. Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation. Washington, DC: The National Academies Press. doi: 10.17226/13343.
×

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FIGURE 2.2 The modulation of galactic cosmic rays during five sunspot cycles. Top: The intensity of cosmic rays as measured by the Climax neutron monitor as a function of time since 1951. Bottom: The sunspot index as a function of time since 1950. Note the alternation of the cosmic-ray maxima between sharply peaked and more-rounded shapes. This corresponds to the change in the direction of the solar magnetic field. SOURCE: J.R. Jokipii, The heliosphere and cosmic rays, Chapter 9 in Heliophysics—Evolving Solar Activity and the Climates of Space and Earth (C.J. Schrijver and G.L. Siscoe, eds.), Cambridge University Press, New York, 2010. Copyright © 2010 Cambridge University Press. Reprinted with permission.


3. Changes in magnitude of the heliospheric magnetic field (HMF) occur over many scales (see Section 9.2 of Jokipii, 2010). The gyroradii of cosmic rays are inversely dependent on the strength of the HMF, and this, together with the observed turbulence of interplanetary plasma, produces changes in the cosmic-ray diffusion coefficients that are approximately inversely proportional to the magnetic field magnitude B (see Section 9.4 of Jokipii, 2010).

4. The changing inclination of the heliospheric current sheet that changes from a nearly flat configuration in the equatorial plane at solar minimum to a 90° inclination at solar maximum and then with decreasing solar activity returns to its near equatorial position at the next solar minimum (see Figure 14 of Jokipii, 2010). This is associated with a change in magnetic polarity, leading to a 22-year solar magnetic cycle.

Review of the NASA GCR Model

The GCR model used in NASA’s proposed model (see Cucinotta et al., 2011) is based on Badhwar-O’Neill (O’Neill, 2006). The model uses a simplified version of the currently well established paradigm of the effects of the heliosphere GCRs. Since the GCRs come from the interstellar medium, this complete model is quite complicated and detailed, involving the spatial and temporal variations of the solar wind, its entrained magnetic field, and the boundary conditions at the interface, which is at a distance from the Sun of some 130 AU. The outer parts of this model are still being debated. However, the basic picture for the inner heliosphere (out to several astronomical units) seems to be well grounded.

The simplified GCR model being used in NASA’s proposed model is often used by scientists in the field to categorize the observations of GCRs. The modulation at Earth as a function of time is represented by one parameter, Φ, called the modulation parameter. For convenience, it is expressed as an energy. This parameter, of course, is a

Suggested Citation:"2 Review of NASA Model." National Research Council. 2012. Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation. Washington, DC: The National Academies Press. doi: 10.17226/13343.
×

quite crude representation of the complicated physical situation, but it can be shown to account reasonably well for observations.

The observational data seem to be consistent and generally accepted. Research on how to incorporate more of the physics, such as the 22-year magnetic cycle, into the cancer risk assessment model would make the estimates of risk more accurate. It is not clear at this point, however, whether the extra effort, which would indeed make the uncertainties less, would make enough difference to reduce the overall risks significantly. It would, however, make the sources of uncertainty more certain. Also, incorporating the new understanding resulting from the recent (2009-2011) historically deep solar minimum will certainly decrease the uncertainties, but, again, it is not clear that the improvement is enough to warrant the effort. The primary uncertainty in the GCR environment calculation comes from a lack of understanding of the physical conditions of transport and the use of statistically based models. The committee also points out that the differences in the GCR intensity at Mars relative to Earth are less than other uncertainties, and so using Earth-based models to address doses at Mars introduces very little uncertainty. Having just passed through an unexpected historically deep solar minimum, it is clear that environmental uncertainties increase with time over years to decades.

The recent incorporation of the CREME96 (Tylka et al., 1997) and Nymmick (Nymmick et al., 1996) models adds more complication, but basically tweaks the Badhwar-O’Neill model to try to make more physical the variation of the modulation parameter Φ. However, there is no simple parameterization of the current models that yields the 22-year effects except in an ad hoc manner. The NASA GCR model could be improved by incorporating the 22-year-cycle variation.

Solar Particle Events

Overview

“Solar energetic particle” (SEP) and “solar particle event” (SPE) are names for a very energetic process and for the potentially damaging situation that occurs when very strong magnetic fields in the solar photosphere reach a critical instability. These are also called solar proton events as protons are the most abundant (>90 percent) species in SPEs. There are also substantial radio bursts, X-ray and gamma-ray emissions accompanying SPEs. A schematic illustrating the timescales for the different emissions from solar events is presented in Figure 2.3 (the 27-day period is the approximate rotation rate of the Sun as seen from Earth). SPEs are typically divided into two classes: (1) gradual events, which are typically the largest events and are associated with coronal mass ejection shocks and typically last for several days; and (2) impulsive events (often called solar flares), which are short, longitudinally localized events on the solar surface. There are usually fewer than 10 gradual events a year. The particle abundances in these events are similar to the composition of the solar corona. They have sharp rise times and decay slowly over hours to days and cover a large longitudinal extent. The impulsive events, associated with instabilities in the solar atmosphere, are rich in heavy ions and show a sharp peak in X rays and gamma rays. These two types of events are illustrated in Figure 2.4. SPEs are stochastic in nature. Their characteristics—composition, intensity, energy spectra, and temporal profile—are highly variable. Typical SPEs are known to pose a small health risk to astronauts and can be effectively attenuated by using relatively thin shield materials, although they can influence mission planning or interfere with mission activities such as extravehicular activities (EVAs). However, large SPEs can be lethal, although they are rare.

The intensity and number of very large solar events (CMEs) vary dramatically from solar cycle to solar cycle. The lack of correlation between sunspots and SPEs illustrates the difficulty in reliably predicting the level or frequency of activity for the future. One fact is clear, however: SPEs occur far more frequently during solar maximum (Figure 2.5). The energy spectra vary significantly from event to event, as illustrated in Figure 2.6. The energy spectrum of an SPE is an important consideration for accurate radiation risk assessment, but it is also not predictable at the present time. Feynman and Gabriel (1988) assume a radial dependence of r−3 for the flux inside 1 AU and a radial dependence of r−2 outside 1 AU. This implies that the fluence and dose will be trajectory-dependent for interplanetary missions such as exploration missions to near-Earth objects or to Mars. However, the validity of this scaling law is in question, because there are very few simultaneous measurements of SPEs at Earth and at

Suggested Citation:"2 Review of NASA Model." National Research Council. 2012. Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation. Washington, DC: The National Academies Press. doi: 10.17226/13343.
×

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FIGURE 2.3 Schematic plot of the relative variations in time of the amplitudes of the X-ray, radio noise, high-energy particle, and solar plasma fluxes for a “typical” large solar flare. NOTE: HF, high frequency; PCA, polar cap absorption. SOURCE: Reprinted from M.A. Shea and D.F. Smart, History of solar proton event observations, Nuclear Physics B (Proc. Suppl.) 39A: 16-25, 1995. Copyright 1996, with permission from Elsevier.

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FIGURE 2.4 Two types of solar proton events: (1) gradual events, which are typically the largest events and are associated with coronal mass ejection (CME) shocks and typically last for several days; and (2) impulsive events (often called solar flares), which are short, longitudinally localized events on the solar surface. SOURCE: J. Barth, NASA Goddard Space Flight Center, “Modeling Space Radiation Environments,” presentation at the IEEE Nuclear and Space Radiation Effects Conference Short Course: Applying Computer Simulation Tools to Radiation Effects Problems, July 21, 1997.

Suggested Citation:"2 Review of NASA Model." National Research Council. 2012. Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation. Washington, DC: The National Academies Press. doi: 10.17226/13343.
×

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FIGURE 2.5 Yearly event fluences for protons of energy >30 MeV versus year relative to sunspot maximum. SOURCE: J. Feynman, G. Spitale, and J. Wang, Interplanetary proton fluence model, Journal of Geophysical Research 98:13281-13294, 1993. Copyright 1993 American Geophysical Union. Reproduced by permission of American Geophysical Union.

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FIGURE 2.6 Energy spectra of several large solar energetic particle (SEP) events. SOURCE: R.A. Mewaldt, C.M.S. Cohen, A.W. Labrador, R.A. Leske, G.M. Mason, M.I. Desai, M.D. Looper, J.E. Mazur, R.S. Selesnick, and D.K. Haggerty, Proton, helium, and electron spectra during the large solar particle events of October-November 2003, Journal of Geophysical Research 110:A09S18, 2005. Copyright 2005 American Geophysical Union. Reproduced by permission of American Geophysical Union.

Suggested Citation:"2 Review of NASA Model." National Research Council. 2012. Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation. Washington, DC: The National Academies Press. doi: 10.17226/13343.
×

other locations in the heliosphere. Simply stated, there is no clear understanding of the radial scaling law of SPE flux at the present time.

In addition to a high flux of protons, solar flare events also typically are accompanied by small but variable amounts of heavy ions. However, previous estimates (Kim et al., 1999; NCRP, 1989) indicate that solar heavy ions do not contribute much to the SPE radiation hazard to astronauts.

Review of the NASA SPE Model

The SPE model used in NASA’s proposed space radiation cancer risk assessment model (Cucinotta et al., 2011) is described in Kim et al. (2009, 2011). This SPE model was developed using an extensive events list compiled from King (1974), Feynman et al. (1990), and Shea and Smart (1990) for Solar Cycles 19 to 21, and geostationary operational environmental satellite (GOES) data from 1986 to the present (Solar Cycles 22 and 23). Also, the database includes the SPE events estimated using impulsive nitrate enhancements in polar ice cores (McCracken et al., 2001). From this events list, NASA used non-homogeneous Poisson statistics (Parzen, 1967) for describing SPE frequency distribution and a hazard function from survival analysis to estimate the expected number of events in a given mission duration. Then, from the prediction of the mean number of SPEs with integral fluence (i.e., Φ30) exceeding given thresholds, a (e.g., a = 107, 108, and 2 × 109 cm−2), the cumulative probabilities of SPE frequencies and total proton fluences are defined at various percentiles during a given mission duration. As for the energy spectrum of the expected SPEs, the event-integrated fluence above an arbitrary energy level of E MeV from the 34 historically largest SPEs is fit with the Weibull function up to 1 GeV.

As described above, the new NASA SPE model uses a comprehensive collection of historical SPEs from an extensive database using rigorous mathematical analysis and formulation to determine the total proton fluence and energy spectrum for a given mission length. The new NASA SPE model is an advance over the past model, in which NASA used the fixed event fluence and spectrum from the 1972 August SPE. However, it should be noted that the new model is a statistical SPE model from a data set constructed from past measurements, although the new NASA SPE model is called a probabilistic risk assessment (PRA) model in the NASA report (Cucinotta et al., 2011). The model is not a predictive model for the future, and one must be cautious in extrapolating from the past and present conditions to the future. For both GCR and SPE, the radiation environment at the present time appears to be relatively mild, which is unusual from the historical perspective (NRC, 2006).

The new NASA SPE model can be considered to be a major step forward compared to the current NASA SPE model. Nonetheless there are a few minor areas that NASA could consider further addressing in the future, as follows:

•   The new NASA SPE model is constructed using a list of events from past measurements. However, the definition of an event is not provided in the 2011 NASA report. Depending on how an event is defined, the underlying distribution function of the events can be different from what is used in the NASA SPE model. There are three areas of SPE climatology research: how to count events that may be correlated, statistics of extreme events, and how SPE characterization varies with the solar cycle. The largest (most extreme) impacts are associated with “clusters” of high-speed CMEs called fast CMEs. Recent studies (Ruzmaikin et al., 2011a,b) indicate that extreme space weather events (i.e., large CMEs and SPEs) are not independent of one another. The most important SPEs for estimating radiation risk to astronauts are these extreme events. A new advanced statistical method (e.g., see Ruzmaikin et al., 2011b, and references therein) recently developed can be used to describe these extreme events. It may change the functional form of the distribution of extreme SPEs, which in turn can affect spacecraft design or mission planning. It will be useful if NASA evaluates the statistics of SPEs using different methods to define better the tail distribution of SPEs, that is, extreme SPEs. Furthermore, since SPE statistics vary dramatically from solar cycle to solar cycle, combining all SPE observations together and saying that they are representative of any given solar cycle introduces additional uncertainty. In this regard, it may be useful for NASA to further investigate the SPE frequency relationship to solar cycles. This may also affect the statistics of extreme events.

•   Extension of the energy spectrum of an SPE to 1 GeV using the data only up to approximately 100 MeV seems oversimplified, especially when it has been stressed multiple times that the shape of energy spectrum is an

Suggested Citation:"2 Review of NASA Model." National Research Council. 2012. Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation. Washington, DC: The National Academies Press. doi: 10.17226/13343.
×

important parameter when assessing radiation risk from SPEs. Additional measurements of high-energy protons, especially in the energy range of >100 to >500 MeV, will help better define the energy spectra of SPEs.

•   There is no mention in the 2011 NASA report of radial and longitudinal variations of the SPE fluence and energy spectrum (e.g., the SPE environments at Earth and at Mars are expected to be different when Earth and Mars are at different solar latitude and longitude). The SEPs preferentially move along the spiral interplanetary magnetic field, and this produces a characteristic coupled longitudinal and radial dependence of the intensity, which depends on the parameters and is difficult to precisely determine. The incorporation of these radial and longitudinal variations of SPE environment may be important for a future mission to Mars. In this regard, actual measurements of SPE environment at Mars would be very useful. The Mars Science Laboratory (MSL) Radiation Assessment Detector (RAD) can provide very valuable data in this sense, which will measure the surface radiation environment at Mars. Furthermore, simultaneous measurements of SPEs on orbit at Mars and at the surface are desired because they can be used to validate radiation transport models for the martian atmosphere and surface.

•   There is no mention of a heavy-ion component of SPEs in the 2011 NASA report. Although the committee understands that the SPE heavy-ion flux is low compared to the SPE proton flux, the biological effects of heavy ions is very uncertain at this stage, and it may be premature to exclude the SPE heavy-ion consideration entirely from the overall cancer risk assessment for astronauts. To have a complete SPE model, it is suggested that NASA add a heavy-ion component to the new SPE model.

There are other SPE models available in the community. However, in terms of data sources and model outputs, NASA’s proposed model is not much different from other models, although each uses a different mathematical approach: the Jet Propulsion Laboratory model uses a lognormal fit to the observed SEP event fluences (Feynman et al., 1993, 2002), and the emission of solar protons model uses the maximum entropy principle (Xapsos et al., 2004).

Transport Model

Overview

The external radiation environments described above change their properties (in terms of particle type and energy spectra) as they go through spacecraft materials and the body mass surrounding the internal organs. Nuclear interactions between the primary radiation and shielding materials can generate a score of secondary particles through spallation or fragmentation reactions, which include secondary neutrons. These interactions are typically modeled using radiation transport codes, which can employ deterministic or Monte Carlo methods.

Review of the NASA Shielding Transport Models

The transport model used in NASA’s proposed model is HZETRN, high charge and energy transport code (and BRYNTRN, a computational model of baryon transport) (Slaba et al., 2010a,b). The fluence for each particle type and energy at each tissue of interest (behind spacecraft and body shielding) is characterized by using radiation transport codes: HZETRN with a quantum multiple scattering fragmentation (QMSFRG) database for GCRs and baryon transport model (BRYNTRN) for SPEs. These radiation transport codes solve the Boltzmann transport equation with the straight-ahead approximation. This is a deterministic approach. Typically, deterministic codes are used for simple geometries for which the Boltzmann equation can be solved numerically—Cartesian, cylindrical, spherical, or toroidal geometries. For complex geometries (e.g., spacecraft and the human body) for which the Boltzmann equation cannot be solved numerically, a Monte Carlo approach is appropriate and would provide more accurate results. However, Monte Carlo simulations take prohibitively long computation time, especially for problems with complex geometry, to obtain the results with a good statistical accuracy. Furthermore, the level of accuracy from the Monte Carlo simulations tends to be negated anyway when a simplified geometry is used in the Monte Carlo simulations.

To deal with this situation, NASA uses the aforementioned deterministic codes with ray-tracing techniques to consider a very detailed geometry in a one-dimensional approach. This approach, along with the numerical techniques used in the proposed NASA transport model, is well accepted in the radiation transport community.

Suggested Citation:"2 Review of NASA Model." National Research Council. 2012. Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation. Washington, DC: The National Academies Press. doi: 10.17226/13343.
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Based on the presentations given by NASA to the committee during the first meeting and materials provided subsequently, it is the committee’s opinion that the NASA radiation transport codes have been verified and validated through thin and thick target experimental data and by means of intercomparisons among transport codes widely used in the radiation transport community (HETC-HEDS, FLUKA, PHITS, MANPX, and Geant4). Furthermore, the radiation transport codes used in NASA’s proposed space radiation cancer risk assessment model are shown to provide good agreement in most cases with spaceflight measurements from the International Space Station (ISS) and/or Space Transportation System (STS). In this regard, it is concluded that there have been reasonable advances in developing the predictive capability of radiation transport codes used in the NASA cancer risk assessment. However, it is noted that comparisons of HZETRN with spaceflight measurements still show greater than 20 percent difference for several occasions (see Tables 2.5 and 2.6a in the 2011 NASA report).

As discussed in the 2011 NASA report (Cucinotta et al., 2011), a minor source of discrepancy between transport code predictions and measurements is the mesons, electrons, and gamma rays that become important contributors to organ doses for very thick shielding—for example, >50 g/cm2 shielding. In this sense, the continuous collection of data for different shielding materials and different ion beams is recommended for further validation of transport code, especially for thick targets. The 2011 NASA report also states that the cross-section data are sparse for some projectile-target combinations, especially above 1,000 MeV/u, and improvements are required in how differential cross sections are represented in transports. The committee agrees with this point and suggests that NASA continue compiling experimental thick-target data for code validation.

Final Comments

In general, the committee agrees that the uncertainty associated with the space physics parameters (i.e., environments and transport models) is a minor contributor to the overall space radiation cancer risk assessment, within 15 percent for effective dose comparisons. The knowledge, or lack of it, about the biological effects and responses to space radiation is the single most important factor limiting the prediction of radiation risk associated with human space exploration. NASA’s proposed space radiation cancer risk assessment model assigns a slightly higher overall physics model uncertainty than the estimate of 15 percent. It is assumed in the proposed model that light ion (Z ≤ 4) fluence spectra computed at targets of interest would have a normal distribution with a mean shifted to higher value (M = 1.05) and the standard deviation of 0.33, compared to heavy ions (Z > 5) for which the mean and the standard deviation are assigned to be 1.0 and 0.25, respectively. However, it should be noted that the uncertainty of the space physics model is used independently from other uncertainties in the overall radiation risk assessment. Hence, in the Monte Carlo calculations in the overall risk assessment, tissue-specific particle spectra are being used simply as an input.

The radiation environment and transport models used in NASA’s proposed space radiation cancer risk assessment model are considered to be a major step forward compared to previous models used (especially the statistical SPE model). The models described in the 2011 NASA report have been developed making extensive use of available data and rigorous mathematical analyses. The uncertainties conservatively allocated to the space physics parameters are deemed adequate at this time, considering that the space physics uncertainty is only a minor contributor to the overall cancer risk assessment. Although further research in this area can reduce the uncertainty, it is not clear at this point whether the extra effort would make enough difference in the space physics uncertainty to reduce the overall risks.

CANCER RISK PROJECTION MODELS

Overview

The cancer risk projection component of NASA’s proposed model can be broadly defined as an amalgamation of approaches developed by the 2006 United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR, 2006) and the BEIR VII committee. These committees agree in many respects on how to project lifetime cancer risks following radiation exposure. They both use the latest Japanese atomic bomb survivors life

Suggested Citation:"2 Review of NASA Model." National Research Council. 2012. Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation. Washington, DC: The National Academies Press. doi: 10.17226/13343.
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span study (LSS) as the primary data source. Organ-specific doses are combined with organ-specified cancer risk models, and risks are estimated using both excess relative risk (ERR) and excess absolute risk (EAR) models, which are then combined using a weighted average. (The excess relative risk is the cancer rate in the exposed population divided by the rate in the unexposed population minus 1, whereas the excess absolute risk is the cancer rate in the exposed population minus the rate in the unexposed population.) There are, however, a number of areas in which BEIR VII and UNSCEAR differ in their approach, and these can have a non-negligible impact on risk estimates. The areas are these:

1. The functional form of the ERR and EAR risk models;

2. The estimation of cancer mortality risks;

3. The weights used in the weighted average of the ERR and EAR models, which is the approach used to transfer risk estimates from the Japanese to the U.S. population; and

4. Application of a dose and dose rate effectiveness factor (DDREF) to account for potential differences in cancer risk at low doses and low dose rates.

 

NASA’s proposed model broadly follows UNSCEAR (2006) for the first area listed above, BEIR VII (NRC, 2006) for the second and third, and for the fourth it uses a distribution developed for the National Institutes of Health (NIH) radioepidemiological tables (NIH, 2003). The resulting risk of exposure-induced death (REID) estimates from NASA’s proposed model are quite different from those from the current NASA model (developed in 2005). It is difficult, however, to isolate the factors that drive this difference, because NASA’s proposed model combines different components of several existing approaches. The UNSCEAR and BEIR VII committees developed their approaches each following a unified philosophy to risk estimation. For example, BEIR VII developed functional forms for the risk models and a specific “LSS DDREF” approach to be combined with those models. Conversely, UNSCEAR evaluated linear-quadratic models for the LSS data as a direct approach to incorporating evidence of lower risks at lower doses. Furthermore, those reports had different emphases. The UNSCEAR report emphasized cancer mortality results, whereas the BEIR VII report emphasized cancer incidence. More effort, therefore, was put into developing the models and evaluating the models for cancer incidence in the BEIR VII report and vice versa for the UNSCEAR report. NASA’s proposed model combines the UNSCEAR incidence risk models with the BEIR VII mortality approach, which are probably the least developed aspects of the respective reports. The following of either one of these reports more directly by NASA and the providing of careful justification in its published model description for any minor deviations may result in a model that is more transparent and robust.

A detailed review of the key aspects of the proposed cancer risk projection component of NASA’s model is given below, including a comparison with the approach of NASA’s current model.

Cancer Mortality Risk Estimation

NASA limits an astronaut’s radiation exposures to amounts expected to result in no more than a 3 percent excess risk of exposure-induced death. Therefore, a risk projection model for cancer mortality—as opposed to cancer incidence—is required. The traditional approach to estimating risks of cancer mortality is to use risk coefficients estimated directly from the LSS cancer mortality data. A major proposed change in NASA’s proposed model is to use the “incidence-mortality” approach developed by BEIR VII (NRC, 2006) whereby risk coefficients from LSS cancer incidence data are used and then cancer mortality risks are estimated from these incidence risks. For the ERR models the incidence-ERR coefficients are combined directly with current U.S. cancer mortality rates, whereas for the EAR models the EAR cancer incidence coefficients are multiplied by the current ratio of cancer mortality to cancer incidence in the U.S. population. As mentioned earlier these are then combined to provide a single estimate from a weighted average (more details are provided below). This ratio is supposed to approximate the mortality probability for this cancer site.

The incidence-mortality approach is considered an improvement for site-specific cancer mortality estimation because LSS site-specific cancer incidence data are likely to be more accurate than are cancer mortality data (Ron et al., 1994a,b), which suffer from misclassification of causes on death certificates. However, some further

Suggested Citation:"2 Review of NASA Model." National Research Council. 2012. Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation. Washington, DC: The National Academies Press. doi: 10.17226/13343.
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TABLE 2.1 Comparison of Mortality:Incidence Ratio and Survival Probability Approaches to Estimating Probability of Death from Breast Cancer in Women

  Mortality Rate
(%)
Incidence Rate
(%)
Ratio 10-Year Survival
Ages 20-49     8.6   73.7 0.12 0.21
Ages 50-64   46.6 281.7 0.17 0.18
Ages 65-74   79.5 416.4 0.19 0.16
Ages 75+ 137.3 407.2 0.34 0.19

NOTE: Rates are per 100,000 for events occurring from 2003 through 2007 in the U.S. SEER 9 cancer registries. Ten-year survival probability from data from 1988 through 2007 also from SEER 9.

SOURCE: Data from Surveillance, Epidemiology and End Results (SEER) Program of the National Cancer Institute, available at http://seer.cancer.gov/.


exploration of the change in approach is warranted because it has a non-negligible impact on the REID estimates, especially with respect to the pattern with age at exposure. For example, an evaluation is required of the epidemiological biases that may be operating in the cancer mortality or cancer incidence data from the LSS that would result in a weaker relationship with age at exposure in the cancer incidence data. One possibility is that the level of misclassification in the cancer deaths may increase with increasing age at exposure. Before a major change is implemented, more research into these epidemiological questions is warranted.

In addition, one aspect of the incidence-mortality approach that requires additional evaluation is the use of the ratio of current cancer mortality to cancer incidence rates in the U.S. general population as an approximation to the probability of mortality. Differential period and cohort effects are likely in cancer incidence and mortality data, and these will be captured in this ratio. Current age-specific 10-year-survival probabilities may be a better approach, but this would need to be examined in more detail before being implemented. An example of the problem is shown in Table 2.1 for female breast cancer. There is a strong age dependence of the ratio of mortality to incidence rates for breast cancer, but this is not evident in the survival probabilities. The Environmental Protection Agency (EPA) committee also highlighted this problem in its recent report (EPA, 2011) and developed an alternative method for breast cancer mortality risk estimation. It is unlikely that this problem is limited to breast cancer, however, and the committee recommends further evaluation of other cancer sites.

Transfer from the Japanese to the U.S. Population

Because underlying cancer incidence rates for some cancer sites differ greatly between the Japanese and the U.S. populations, risk estimation based on an excess relative risk model can give very different REID estimates from those based on an excess absolute risk model. Risk projection committees have used a variety of approaches to transfer radiation-related cancer risk estimates from the life span study of the Japanese atomic bomb survivors to a non-Japanese population. A number of committees have recommended that a weighted average of the ERR and EAR models be used. The choice of weights is not straightforward, however, and the most appropriate weighting may well vary by cancer site. NASA’s current (2005) model assigned equal weights to both models for all cancer sites. Most recently the BEIR VII committee (NRC, 2006) assigned subjective weights to each cancer site, mostly weighting the ERR model more heavily than the EAR model. The UNSCEAR (2006) report, however, elected to present the results from the two models separately rather than estimating a weighted average. The proposed NASA approach follows BEIR VII in calculating a weighted average with uncertain weights and generally follows the recommended BEIR VII weights. There are some deviations, however, and the reasons for these deviations need to be justified and the implications of their choices described. For example, BEIR VII recommended a weight of 0.3 for the ERR model for lung cancer because of the evidence that the joint effect of smoking and radiation may be closer to additive than multiplicative (Pierce et al., 2003). In NASA’s proposed model, a weight of 0.5 was used for the ERR lung cancer model. For never-smokers (NSs) this choice will result in lower risk estimates than

Suggested Citation:"2 Review of NASA Model." National Research Council. 2012. Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation. Washington, DC: The National Academies Press. doi: 10.17226/13343.
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would the BEIR weight, and so this change needs to be carefully justified. See below for further comments on the never-smoker lung cancer model.

Dose and Dose Rate Effectiveness Factor

A dose and dose rate effectiveness factor is often applied to lifetime cancer risk projections estimated using data from the Japanese atomic bomb survivors because the cancer risk per unit dose from low doses or low-dose-rate protracted radiation exposures may be lower than the risk per unit dose from acute higher-dose exposures. There are considerable uncertainties, however, about whether such a factor is necessary, and the approach by different committees for estimating DDREF varies. The BEIR VII committee conducted an extensive review of the experimental data, analyzed the LSS data, and developed a Bayesian “LSS DDREF” with a central estimate of 1.5. BEIR VII recommended that this LSS DDREF be applied for all protracted doses and for acute doses below 100 mGy. The UNSCEAR (2006) reviewed the various approaches but did not make any recommendations about the application of a DDREF. The proposed NASA approach is to use a discrete uncertainty distribution for the DDREF that was developed for the NIH (2003) radioepidemiological tables, which essentially has a median DDREF of 1.75. This was a subjective uncertainty distribution that approximated continuous subjective distributions from three earlier publications (EPA, 1999; NCRP, 1997; Grogan et al., 2001). The more recent, BEIR VII approach, which is somewhat less subjective, may be preferable to this older approach. An additional consideration is that a number of publications produced since the NIH report are relevant to this issue: for example, the Mayak workers study (Shilnikova et al., 2003), the third analysis of the United Kingdom’s National Registry for Radiation Workers (Muirhead et al., 2009), and the 15-country nuclear workers study (Cardis et al., 2005), together with the review of these studies and comparison with the LSS by Jacob et al. (2009). These findings are obviously not reflected in the older, subjective uncertainty distributions referenced above.

Risk Models for Never-Smokers

The issue of the smoking status of the astronauts and the potential implications on risk projections for smoking-related cancers are also important and it is appropriate that the impact of taking smoking status into account should be investigated. Most astronauts are non-smokers, which would likely lower the risk projections for astronauts compared to estimates for the general population (a mixture of never- and ever-smokers). The proposed NASA approach for lung cancer does not take account of variation in lung cancer risk coefficients or competing causes of death with smoking status and should be developed further. The approach is to use the UNSCEAR lung cancer risk coefficients applied to lung cancer rates of those who are never-smokers (typically defined as having smoked fewer than 100 cigarettes ever). These risk coefficients, however, are not smoking-specific. A number of analyses of the joint effect of smoking and radiation on lung cancer risk have been conducted using the LSS data and risk coefficients from these models for never-smokers would be more appropriate (Pierce et al., 2003; Furukawa et al., 2010). Indeed these analyses demonstrate that the risk coefficients do vary by smoking history. There would still need to be consideration of how to transfer the never-smoker risk models from the Japanese to the U.S. population. It may not be as straightforward as just applying the LSS never-smoker ERR coefficients to U.S. never-smoker lung cancer incidence rates, as the pooled analysis by Thun et al. (2008) found that lung cancer rates in never-smokers are much higher in Asians than in Caucasians. NASA’s current (2005) model does not include an assessment of the risks for never-smokers.

A second aspect of the risk model for never-smokers that needs to be addressed is that NASA’s proposed model does not use competing risk adjustments that are specific to never-smokers in the REID calculation. As the life expectancy of never-smokers is about 10 years longer than that for current smokers, continuing use of the general-population survival probabilities for competing risks will result in underestimates of the REID for never-smokers. One relatively straightforward approach to competing risks for never-smokers is to assume that never-smokers have approximately a 5 years’ longer life expectancy than that of the general population (Doll et al., 1994). (The general population is a mixture of never-smokers, past smokers, and current smokers, and so 10 years would be an overestimate of how to adjust the general-population survival curves.)

Suggested Citation:"2 Review of NASA Model." National Research Council. 2012. Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation. Washington, DC: The National Academies Press. doi: 10.17226/13343.
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For the other smoking-related cancers, it is probably premature to estimate the REID for never-smokers. No studies have been conducted to evaluate the joint effect of smoking and radiation for these cancer sites, and reliable estimates of the cancer incidence rates for these sites for never-smokers are not available.

Additional Minor Issues

The UNSCEAR 2006 models included a latency term, but Preston et al. (2007) and BEIR VII did not. For the cancer risk estimates that were based on risk coefficients from those reports, a latency period needs to be added—for example, by assuming that risk is zero for the first 5 years after exposure. It is unclear whether this was done for the cancer risk estimates that were based on risk models from these sources. Secondly, the approach used for the age-centering of the coefficients for breast cancer in the BEIR VII report makes those coefficients inappropriate for use in NASA’s proposed model, and therefore coefficients should be taken instead from their original source (Preston et al., 2002). NASA does not consider non-Hodgkins’ lymphoma (NHL) as a radiogenic cancer for inclusion in its risk estimates. There are reported studies of the induction of NHL from occupational exposures; however, the issue remains unclear at this time (Preston et al., 1994). NASA may find it useful to follow the literature on this topic for possible future adjustments of its model.

UNCERTAINTIES IN LOW LINEAR ENERGY TRANSFER RISK MODEL

Overview

One of the components in NASA’s proposed model (described in Section 4 of the 2011 NASA report: Cucinotta et al., 2011) addresses risk estimates and their uncertainties associated with exposure to low linear energy transfer (LET) radiation. Such radiation includes high-energy gamma radiation found in space environments, the acute gamma radiation received by survivors of the atomic bombings of Hiroshima and Nagasaki in 1945, and medical X rays. Uncertainties are important because risk protection involves safety factors that, as described in the 2011 NASA report, are driven by upper 95 percent uncertainty limits on risk. For example, a space activity that is estimated to be associated with a lifetime excess mortality risk of “about 0.5 percent” would (and should) be viewed very differently depending on whether the upper 95 percent uncertainty limit or confidence bound obtained by NASA on that estimate is 0.6 percent, 1.5 percent, or 4 percent.

Conclusion: Uncertainty limits on radiation-related risk reflect information about anticipated environmental radiation dose levels and accumulated knowledge about the relationship between radiation dose and cancer risk. For the approach used by NASA, more information, if available, might reduce statistical uncertainty and, assuming that the new information did not increase the central risk estimate, lower the upper 95 percent uncertainty bound criterion used by NASA to evaluate the acceptability of activity-related mortality risk.

Epidemiological Basis for Radiation-Related Cancer Risk Estimation

The uncertain relationships between radiation exposure and subsequent, organ-specific cancer risks are best understood for low-LET radiation and, in particular, for the high-energy gamma radiation that dominated the radiation doses received by the Japanese atomic bomb survivors. In contrast with earlier reports—for example, NCRP (1997) Report No. 126 and NCRP (2000) Report No. 132—estimated neutron doses from the bombs are now considerably lower, reflecting new appreciation of the neutron shielding provided by water vapor in the very humid August air of the two cities (Cullings et al., 2006; Preston et al., 2007). As a result, estimated neutron dose, even with a postulated 10-fold relative biological effectiveness (RBE) compared to gamma dose, is now considered to be a less important contributor to risk among atomic bomb survivors and, perhaps more important, it is a risk factor that is difficult to quantify based only on the atomic bomb survivor data (Preston et al., 2007). In NASA’s proposed model, the low-LET radiation, and its uncertainties, are based primarily on cancer incidence and mortality data from the LSS cohort of atomic bomb survivors, plus medically or occupationally irradiated populations, as

Suggested Citation:"2 Review of NASA Model." National Research Council. 2012. Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation. Washington, DC: The National Academies Press. doi: 10.17226/13343.
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interpreted in a number of committee reports from the International Commission on Radiological Protection (ICRP, 1991, 2006), NCRP (1997, 2000), NIH (2003), NRC (2006), and UNSCEAR (2008). The statistical uncertainty structure of the estimated parameters of this model is well characterized for the more important cancer sites. The low-LET model pertains directly to cancer risk from high-energy gamma radiation at high dose rates and requires a somewhat controversial, and uncertain, DDREF for such radiation that is fractionated or delivered at low dose rates, as discussed in the section above entitled “Cancer Risk Projection Models.” In contrast, cancer risks associated with different types of high-LET radiation characteristic of the radiation environment beyond low-Earth orbit, many of which are rarely if ever encountered at Earth’s surface, are more tenuous. It may be, however, that certain simplifying relationships apparent in dose-response models for low-LET radiation, such as common parameter values for attained age as a modifier of dose response (NRC, 2006, Table 12-2), may carry over to high-LET models. The low-LET risk model therefore is an obvious starting point for modeling risk from various types of high-LET radiation, which is discussed in Sections 5 and 6 of the 2011 NASA report (Cucinotta et al., 2011).

Uncertainty-Related Bias

Radiation doses to individual members of epidemiological study populations tend to be uncertain, and such uncertainties may result in biased estimates of risk. This has long been recognized (Gilbert, 1984; Armstrong, 1990), and in recent years and with improved computing power, the correction of biases associated with such uncertainties has increasingly become an essential part of the dose-response analysis (Pierce et al., 1990; Schafer and Gilbert, 2006; Hofer, 2007, 2008; Pierce et al., 2008). Such corrections, which are not covered by NCRP (1997) Report No. 126 or NCRP (2000) Report No. 132, tend to increase the estimated dose-response as well as its uncertainty, a factor that should be mentioned in future descriptions of the risk model.

Radiation Risk Protection

Because Sections 3 and 4 of the 2011 NASA report (Cucinotta et al., 2011) are limited to the consideration of low-LET radiation, inferences are substantially simpler, and better supported by epidemiological data, than are inferences in later sections that deal with the more complex radiation environment beyond low Earth orbit. The 2011 NASA report is concerned with radiation protection, which is intrinsically simpler than the attribution of existing cancer cases to past radiation exposures for compensation or other purposes (NIH, 1985, 2003; Wakeford et al., 1998; Kocher et al., 2008). NASA’s decision to consider the cancer site only insofar as it affects the relationship between the radiation dose and overall excess cancer mortality is a further simplification. The proposed model described in the 2011 NASA report continues to depend on the NCRP (1997) Report No. 126 and NCRP (2000) Report No.132 models, but its site-specific risk estimates are based primarily on LSS incidence, which is converted to mortality using U.S. cancer survival rates (NRC, 2006), rather than on mortality data.

Dependence on Atomic Bomb Survivor Data

The LSS data constitute by far the most important single information source for predicting radiation-related cancer risk in exposed or potentially exposed populations, as discussed above. The 2011 NASA report (Cucinotta et al., 2011) expressly relies on LSS incidence data obtained through the Japanese Radiation Effects Research Foundation (RERF) and on U.S. cancer survival statistics in preference to LSS mortality data. The incidence data were obtained through periodic analyses of data from the RERF Tumor Registry, which covers site-specific cancer incidence starting in 1958 and is adjusted for the migration of members of the LSS cohort to other parts of Japan and other places not covered by the registry. Other sources of information include site-specific cancer mortality data beginning from 1950 (Preston et al., 2003) and periodic refinements to the reconstructed radiation doses received by individual survivors (most recently, Pierce et al., 1990, 2008; Preston et al., 1994, 2004, 2007; Cullings et al., 2006; Thompson et al., 1994). The NASA analysis, which generally follows NCRP (2000) Report No. 132, also cites reports by the NRC (2006) and UNSCEAR (2008), and new models for lifetime risk and its uncertainty as employed in UNSCEAR (2006), Little et al. (2008), and an analysis of cancer risk in radiation workers (Jacob et

Suggested Citation:"2 Review of NASA Model." National Research Council. 2012. Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation. Washington, DC: The National Academies Press. doi: 10.17226/13343.
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al., 2009) with emphasis on DDREF. Of these reports, only Jacob et al. (2009) deals mainly with non-LSS data. Although the LSS data have many assets that make them uniquely valuable, they are not without some limitations. For example, they do not cover the entire LSS population but only those resident in Hiroshima and Nagasaki. In addition, the registries start 13 years after the initial exposure. These limitations add a level of uncertainty that is considered in the risk estimates developed by NASA.

The data used by NASA for low-LET risk models are those cited above from the LSS published by RERF, plus additional dose-response data from a pooled analysis of cancers of the thyroid gland by Ron et al. (1995), largely from the Israeli series on children treated by x-ray scalp depilation for tinea capitis, but also from studies of patients treated by radiation for enlarged thymus glands and by tonsil and nasopharyngeal irradiation. Strong evidence of radiation-related risk of female breast cancer is provided by the LSS data, two series of tuberculosis patients given lung collapse therapy monitored by frequent chest fluoroscopies, and a series of children treated in infancy for enlarged thymus. A joint analysis by Preston et al. (2002) found these four series to be consistent with one another. However, in the same joint analysis, data from women given radiation therapy for acute postpartum mastitis and for hemangioma demonstrated increased breast cancer risks, but with dose responses inconsistent with the other four studies and with each other, and so the final estimate was based on a combination of the LSS and tuberculosis and enlarged-thymus studies.

The 2011 NASA report states at the beginning of Section 6 (Cucinotta et al., 2011) that arithmetic weighting of multiplicative and additive risk-transfer models is used to transfer risk estimates to the U.S. population. This appears to be the only mention in the 2011 NASA report of this information, which is crucial, given that, with similar weights, geometric weighting always yields a lower value than arithmetic weighting unless the multiplicative and additive risk-transfer model estimates are identical. It would seem that the above statement in Section 6 of the 2011 NASA report (Cucinotta et al., 2011) might usefully be highlighted by inclusion as well in its Executive Summary.

Miscellaneous Comments

A minor flaw in Formula 4.1 of the 2011 NASA report (Cucinotta et al., 2011), easily repaired, is that the baseline mortality rate λ0 is written not only as a function of attained age a, but also as a function of E (energy) and aE (age at exposure). It would be better to write baseline rate λ0 as a function only of age and sex; it is the excess risk that also depends on energy and exposure age.

The empirical Bayes (EB) shrinkage approach of Pawel et al. (2008) is discussed briefly in Sections 3, 3.1, and 4.2 and in Table 4.1 of the 2011 NASA report (Cucinotta et al., 2011), but it is not mentioned in later sections. The EB approach arguably has advantages for a single-site estimate—for example, cancer of the urinary bladder—to the extent that information pertaining to additional cancer sites may also be relevant to radiation-related bladder cancer risk and may therefore modify that estimate and reduce its uncertainty. For radiation-related mortality from all cancers combined, however, the standard error of the estimate depends on the entire covariance matrix and should be about the same as it would be without shrinkage. The EB discussion is not particularly relevant to radiation-related mortality from all cancers combined, and it might be left out of the 2011 NASA report.

CANCER RISKS AND RADIATION QUALITY IN THE MODEL

Overview

Different types and energies of ionizing radiation (radiation qualities) do not all have the same biological effectiveness per unit absorbed dose. ICRP introduced radiation weighting factors, wR, for different types of particles to take account of the differences in biological effectiveness (ICRP, 1991). The radiation weighting factors are used to multiply organ- and tissue-absorbed doses in order to obtain equivalent doses for organs. In its current (2005) model, NASA uses quality factors, Q, for the cosmic radiation field instead of radiation weighting factors to obtain equivalent dose, following the recommendation by NCRP (2000) Report No. 132 and NCRP (2002) Report No. 142. In the ICRP system of quantities, Q is used to weight absorbed dose at a point to obtain dose equivalent used for the definition of operational quantities for radiation monitoring. The values

Suggested Citation:"2 Review of NASA Model." National Research Council. 2012. Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation. Washington, DC: The National Academies Press. doi: 10.17226/13343.
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of Q have been selected by ICRP and NCRP as a function of LET on the basis of published data on the RBE for different radiations and for appropriate or relevant biological effects. RBE is the ratio of the absorbed dose of a standard radiation, such as X rays or gamma rays, to the absorbed dose of a radiation in question, that both produce the same level of a given biological effect. Since RBE depends on numerous factors such as the level of effect and the biological effect chosen for comparison, values of Q have been chosen by ICRP as well as by NCRP to represent the factor for low doses of a given radiation most likely to be pertinent for carcinogenesis, the effect of primary concern for most radiation protection considerations, including those of NASA. This is an especially important consideration for the radiation environments in space, where astronauts are exposed to solar protons and heavy ions of high energy and high atomic number (HZE). The vast majority of data relating dose to biological effects in general and to cancer risk in particular, however, relate to sparsely ionizing radiations like X rays or gamma rays.

One property that has been extensively studied in an effort to link a physical description of energy deposition by different ionizing radiations is linear energy transfer, which gives some indication of the average density of ionizations along charged particle tracks. But LET alone does not fully predict RBE for a particular biological effect, and it has become increasingly clear that for radiations encountered in space, a better descriptor is needed. A major focus of the 2011 NASA report, in which NASA’s proposed model is described, is to utilize more realistic approaches to defining quality factors and to examine and suggest improved approaches in this regard. However, it should be noted that the 2011 NASA report proposes the replacement of the ICRP-NCRP-defined quality factor, Q (ICRP, 1991, 2007; NCRP, 2000), with a different definition (and different numerical values) of quality factor, QF, for application in the space radiation environment. The 2011 NASA report is not consistent in its usage of the terminology Q and QF, and the report’s glossary is unhelpful in this respect: it identifies both Q and QF simply as “quality factor,” without indicating a distinction between them. For clarity in reviewing NASA’s proposed model, the committee uses the term Q exclusively for the ICRP-NCRP-defined quality factor, and it uses QF exclusively for the space radiation quality factor as defined in the 2011 NASA report (Cucinotta et al., 2011) for NASA’s proposed model (see the glossary definitions in Appendix C in the present report). The committee suggests that NASA retain this clear distinction in terminology between ICRP-defined quantities and NASA-defined alternatives.

In NCRP (2000) Report No. 132 and ICRP (1991) Publication 60 and ICRP (2007) Publication 103, it is assumed that particles with the same LET but different spatial distributions of ionization and excitation events produce the same cancer risk even though the complexity of damage and spatial distribution of initial damage may be different. NCRP (2001) Report No. 137 did not consider particle track structure or extrapolation to low dose. Section 5 of the 2011 NASA report (Cucinotta et al., 2011) describing the proposed model builds the case for several major proposed changes to these approaches. First is the use of different values of QF for leukemia and for solid cancers, with substantially higher values for solid cancers than those for leukemia, based on the literature evidence described below. Second is the replacement of the dependence of Q on LET, as the main radiation quality descriptor, with functional dependence of QF on Z*2/β2, where Z* is the effective charge number of the particle and β its speed relative to the speed of light. Three empirical parameters, Σ0/αimage m, and κ, are introduced in order to define the proposed QF dependence on Z*2/β2, as discussed in more detail in the section below entitled “Integration and Completeness of the Model.” LET, by definition, is the average energy loss per unit path length of charged particles and does not provide a description of the microscopic distribution of energy deposition around the pathway of the particles including the production of delta rays of varying energies; however, Z*2/β2 to some degree takes account of energy deposition around the track of the particle. Furthermore, the proposed NASA approach uses a different dependence of QF on Z*2/β2 for light (Z ≤ 4) and for heavy (Z > 4) charged particles. Third, NCRP (2001) Report No. 137 previously explored the concept of using particle fluence instead of absorbed dose. In that approach, the absorbed dose and quality factor are replaced by particle fluence and a parameter, Σ, denoting risk cross section. In the NASA’s proposed model, NASA has proposed empirical functions for Σ guided by considerations of track structure and a biophysical model to extrapolate to HZE particles. Fourth is the consideration of non-targeted effects (NTEs) in the model. These effects, despite the extensive discussion in the NASA report of a possible NTE component, are not incorporated into the proposed NASA risk projection model but do provide a framework for the future incorporation of such extensions of the NASA model.

Suggested Citation:"2 Review of NASA Model." National Research Council. 2012. Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation. Washington, DC: The National Academies Press. doi: 10.17226/13343.
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Quality Factors for Leukemia and Solid Cancers

The epidemiology data available for human populations exposed to high-LET radiation are relatively sparse and have limitations that make them of limited utility for estimating risks. The available data sets together with a discussion of these limitations are provided in the 2011 NASA report in Section 5.1.1, entitled “Relative Biological Effectiveness from Human Epidemiology Studies” (Cucinotta et al., 2011). The committee agrees with NASA’s conclusion that most insight on the influences of radiation quality on carcinogenesis comes from limited animal experiments coupled with cellular studies and use of biophysical models. The animal studies available include the Harderian gland studies (Fry et al., 1985; Alpen et al., 1993, 1994), work on skin cancer (Burns et al., 1993, 1994), work on mammary gland carcinogenesis (Dicello et al., 2004), and the recent Weil et al. (2009) work on acute myeloid leukemia (AML) and hepatocellular carcinoma. The results from these animal studies have indicated a large dependence of RBE for heavy ions on tissue and induced cancer type. This was dramatically shown in the study by Weil et al. (2009), in which a very high RBE (in the range of 50) was reported for hepatocellular carcinomas, but low RBE for AML. It should be pointed out that the large RBE is derived from the observation that a barely significant increase in liver tumors was seen for gamma rays even after the highest doses, but a large increase was seen even at the lowest doses for 1 GeV/nucleon Fe ions. The 2011 NASA report also has derived an RBE of 0.48 +/−0.007 for Fe ions relative to gamma rays for induction of AML based on the data presented in Weil et al. (2009). The range of doses and the levels of effect, however, were quite different for the iron ions and gamma rays, and so the establishment of an RBE with this small level of uncertainty will require further work. Nevertheless, the available animal data, coupled with the small amount of human data from Thorotrast studies (Boice, 1993; Grogan et al., 2001), are consistent with the proposed use of low QF for leukemia and substantially higher QF for solid cancers in the NASA model. The committee therefore endorses the use of separate QFs for leukemia and solid tumors and recommends further research using animal models to increase confidence in the numerical values to be used for those QFs.

It is anticipated that human data relevant to the assessment of quality factors for some solid cancers and cardiovascular disease will become available in the near future. Proton irradiation as well as carbon-ion irradiation (with different probabilities of fragment tails for these two radiations) of patients will in the future be a resource for this type of information, especially at different Z-values. NASA will need to monitor developments in this area of study.

Delayed effects of irradiation are discussed in Section 5 of the 2011 NASA report (Cucinotta et al., 2011), principally cited are several reports of genomic instability that was measured by a delayed increase in rates of appearance of new chromosomal aberrations or micronuclei in cells several cell generations following irradiation. In these cases, radiations have been shown to trigger a delayed or ongoing instability, apparently due to a decreased capacity for genome maintenance. Radiation-induced instability is potentially very important in connection with current notions of multistep processes involved in carcinogenesis, especially regarding any dependencies on radiation quality that may apply. It would have been useful, for a balanced view on the subject, to include mention in the 2011 NASA report of numerous literature reports in which such instabilities have not been seen (e.g., Kodama et al., 2005; Whitehouse and Tawn, 2001; Dugan and Bedford, 2003). Also, interestingly, there are reports of observations on genetic variability among individuals in susceptibility to radiation-induced genomic instability (e.g., Kadhim, 2003) that could have been cited, as well as studies on the lack of a delayed instability in clones of lymphocytes in numerous atomic bomb survivors decades after irradiation. Hence, this topic is in a state in which it can only be suggested as a factor that may be important if a connection to health risk could eventually be established. The whole question of the genetic contribution to variation in individual susceptibilities to radiation-induced cancer would be a fruitful area of further investigation. This is especially pertinent in light of the well-known body of evidence for genetic sources governing the wide variation in carcinogenic radiosensitivity in mouse strains, and more recent evidence for such genetic variation in human populations (e.g., Best et al., 2011; Flint-Richter and Sadetzki, 2007).

To date, the vast majority of Earth-based studies on effects of heavy ions that may be encountered in space involve exposures lasting only seconds or minutes. In light of the fact that exposures received by astronauts will be protracted over weeks, months, or even years in the case of Mars missions, the modification of effects per unit dose resulting from low dose rate exposures received over long periods of time for HZE radiations are briefly dis-

Suggested Citation:"2 Review of NASA Model." National Research Council. 2012. Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation. Washington, DC: The National Academies Press. doi: 10.17226/13343.
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cussed in the 2011 NASA report. The issue of dose and dose rate, or DDREF, is mentioned above. At the cellular level, given the flux of HZE particles in space, the frequency at which cell nuclei are traversed by a particle track will be so low that the issue becomes one of how many cells in a tissue are traversed by a particle track, each of which would deliver a given dose to the cell traversed. As far as dose protraction over an extended time period is concerned, a further question would be whether some forms of time-dependent interactions, as yet unknown, occur among cells or involve other factors that could modify the overall response at the tissue or organ level. Such issues have not been studied adequately, and the fact remains that, with the few exceptions noted below, most Earth-based HZE studies have examined effects for exposures lasting only a few minutes.

A few literature reports (e.g., Burns et al., 1994; Alpen et al., 1994) cited in the 2011 NASA report show that protraction or fractionation of radiation exposures over time has resulted in increased, or in some cases decreased, incidences of induced cancers in animals relative to single exposures of short duration. To emphasize the broad scope and relevance of the issue, the 2011 NASA report mentions many factors aside from cellular and molecular repair processes that can contribute to the modification of carcinogenic or other biological effects, depending on fluence rate or dose fractionation. As mentioned above, further changes in the radiosensitivity of cells or tissues during prolonged exposure, and differences in the way that these changes may depend on radiation quality, greatly increase the uncertainties involved in use of quality factors that are based on data from single, short exposures. Hence, the 2011 NASA report (Cucinotta et al., 2011) correctly points out that there is more to fractionation and dose-rate effects than DNA repair processes might predict for both high- and low-LET radiations. The analysis in Cucinotta et al. (2011) thus summarizes why further studies should be undertaken to account for these several factors, which argue against the indiscriminant application of DDREF in risk estimation. It also emphasizes the need for further studies for HZE radiations.

Replacement of LET-Based Quality Factors with Track-Structure-Based Quality Factors

Cellular studies with end points of chromosome aberrations and neoplastic transformation are presented in the 2011 NASA report as examples of the common observation that RBE depends on LET, with a peak in RBE around 100 to 200 keV/mm. The description of cellular studies of chromosomal aberrations and mutations in the 2011 NASA report could be strengthened considerably by citing other relevant publications from research groups outside of NASA. A few relevant papers from these outside groups are Dugan and Bedford (2003); Cox et al. (1977a,b); Goodhead et al. (1979, 1980); Thacker et al. (1980); Bedford and Goodhead (1989); Anderson et al. (2000, 2005); Goodwin et al. (1996); Loucas and Cornforth (2001); Loucas et al. (2004); Elkind and Whitmore (1967); and Lloyd and Edwards (1983). The number of other relevant publications is limited somewhat because NASA’s concerns focus on heavy-ion effects, but even so, there are many references not cited that agree with and strengthen the general conclusions based on those that are cited by NASA.

Importantly, the data (e.g., George and Cucinotta, 2007) also show that the LET value of the RBE peak increases with Z and that RBE increases with decreasing E. As already mentioned, LET is known to provide a poor description of track structure and energy deposition in biomolecules, cells, or tissues due to the different spatial distribution of ionization and excitation events for particles that have the same LET values (as illustrated in Figure 5.4 in the 2011 NASA report [Cucinotta et al., 2011] using data from Plante and Cucinotta [2008]). In part, this reflects the relative contributions of delta rays for different particles of similar LET. NASA’s proposed model uses an approach for evaluating energy deposition changes with particle charge and energy that is based on biophysical models that include a description of radial dose (Goodhead, 1989).

The 2011 NASA report describes Monte Carlo methods that relate the stochastic aspects of radiation tracks to energy deposition patterns in nanometer volumes which include secondary electron (delta-ray) tracks. Thus predictions of the energy deposited in nanometer target sizes can be made for particles found in space radiation (Cucinotta et al., 2000). The results of these approaches indicate that the parameter Z*2/β2 gives a better description of the energy deposition in small nanometer volumes than does LET. Biological data from the literature chosen by NASA for use in its report show that the observed levels of biological effects (survival, mutations) differ significantly for different particles with the same LET (Thacker et al., 1979; Belli et al., 1992, 1993; Schafer et al., 1994;

Suggested Citation:"2 Review of NASA Model." National Research Council. 2012. Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation. Washington, DC: The National Academies Press. doi: 10.17226/13343.
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Baltschukat and Horneck, 1991; Kiefer et al., 1994; Kranert et al., 1990), whereas the fits using the parameter Z*2/β2 gave better correspondence for particles with the same LET (Cucinotta et al., 1996, 1997).

The data presented in the 2011 NASA report support NASA’s rationale for replacing the use of LET as a descriptor of track structure and energy deposition events in biomolecules, cells, or tissues for particle radiation, although the use of LET is not questioned by the radiation protection community for the development of risk estimates for terrestrial radiations. It is acknowledged by this committee that DNA strand breaks, cell death, mutations, and chromosomal aberrations may not be direct measures of cancer risk; however, they may be valuable markers assuming that cancer risk shows similar relative changes with radiation quality. Hence, an increasing amount of data, theory, and literature support is consistent with the idea that at low particle fluences, as present in space, the dependence of biological effects on radiation quality is not well described by LET alone and that instead, both Z and E must be considered in order to give an adequate description of three-dimensional track structure, including both the primary particle and delta rays.

Risk Cross Sections and Quality Factors

The development of risk cross sections, previously discussed in NCRP (2001) Report No. 137, has now been extended using Equations 5.19 through 5.21 in the 2011 NASA report (Cucinotta et al., 2011) to arrive at a QF function (called QNASA in Equation 5.21 but not elsewhere in the 2011 NASA report). This extension of risk cross sections allows consideration of track structure based on Z*2/β2 and biophysical models, enabling the extrapolation of experimental data to other particle types and the extrapolation of responses from acute exposures to chronic exposures. The assumption is made without discussion in Equation 5.19 in the 2011 NASA report (Cucinotta et al., 2011) that the risk cross section can be arithmetically partitioned into two independent components describing low- and high-LET-like behavior, respectively. The three variable parameters, Σ0/αimage m, and κ, are estimated from the RBE of HZE particles for various radiobiological end points on Z*2/β2. Biological end points in mammalian cells for dicentrics, mutations involving the hypoxanthine-guanine phosphoribosyltransferase gene and neoplastic transformations (Figure 5.12 in the 2011 NASA report [Cucinotta et al., 2011]) have been chosen to show the dependence of risk cross sections on Z*2/β2 since data on solid cancer risk for HZE particles are scarce. The ranges of uncertainty for these three parameters are only estimated from radiobiological data and require more studies using HZE particles of different energy and Z to identify the most effective energy or value of Z*2/β2 to increase confidence in the use of risk cross sections.

Inspection of the risk cross sections for various biological end points as shown in Figure 5.12 of the 2011 NASA report (Cucinotta et al., 2011) highlights the paucity of data on cancer induction for different HZE particles. The uncertainties in the empirical model parameters, Σ0/αimage m, and κ, estimated from the RBE of HZE particles for various radiobiological end points on Z*2/β2, and hence the uncertainties in QF, are responsible for the largest component of uncertainty in REID as calculated by NASA’s proposed model (see the section below entitled “Integration and Completeness of the Model”). For similar reasons, Q is responsible for the largest component of uncertainty using NASA’s current (2005) model. This weakness in both NASA’s current and proposed models will continue to be an issue in the absence either of more data relating to cancer risk from HZE particles, or of more data using sufficiently reliable surrogate end points for cancer risk from HZE particles. A limitation of using Z*2/β2 is the lack of distinguishing track widths for different particles but with identical Z*2/β2values. Differences in the biological effectiveness of delta rays, especially for energies less than 10 keV (Goodhead and Nikjoo, 1989), have not been considered explicitly, since tracks of some particles—for example, low-energy hydrogen or helium—contain a higher fraction of delta rays at these lower energies. The use of LET, as in NASA’s current (2005) model, also suffers from limitations regarding track width and delta-ray energies, as well as from additional limitations as discussed previously. Accordingly, the committee concludes that, given current knowledge, NASA’s proposal to use Z*2/β2, rather than LET, and risk cross sections based on Z*2/β2 is reasonable and, hence, the committee judges that this change to the model is appropriate.

Suggested Citation:"2 Review of NASA Model." National Research Council. 2012. Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation. Washington, DC: The National Academies Press. doi: 10.17226/13343.
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Non-Targeted Effects in the NASA Model

Section 5 of the 2011 NASA report (Cucinotta et al., 2011) also discusses the possibility of modifying the NASA model by adding a term for non-targeted effects, considered to be biological responses in cells that are not directly irradiated. The existence of such effects in cell culture systems has been widely documented for both high- and low-LET radiations (e.g., reviewed in NRC, 2006; UNSCEAR, 2008; Held, 2009), and the potential relevance of NTEs to space radiation biology has been discussed (Cucinotta and Durante, 2006; Held, 2009). However, there are also published studies in which the effect could not be demonstrated (e.g., Fournier et al., 2009). The existence of NTEs in vivo is less well documented, and the relevance to cancer risk is not known with any degree of certainty. Section 5 of the NASA report presents a clear discussion of the manner in which NTEs could be incorporated mathematically into the proposed risk model, and based on current knowledge, the approach seems reasonable. Furthermore, the section summarizes a recent study (Cucinotta and Chappell, 2010) in which the NTE-containing model, when compared with the so-called targeted effect (TE) model, was shown to improve the fit to data on cancer induction in the Harderian gland (data of Alpen et al., 1993) by a range of heavy charged particles. However, the NTE component is not incorporated into the final version of the proposed NASA risk model because the NASA report authors believe that insufficient data are available to lead to an understanding of the importance of NTEs in cancer risks at this time. The committee endorses the decision that an NTE component not be included in the NASA model at this time.

However, the committee points out that, although it is appreciated that the inclusion of considerations of NTEs in the 2011 NASA report was done to indicate potential future enhancements of NASA’s proposed model, such an extensive discussion in the 2011 NASA report seems distracting for an element that is then not incorporated into the final version of the proposed model.

Another issue to consider relates to NTEs with HZE, since delta rays may crossfire into neighboring cells and need to be taken into consideration when defining bystander cells, especially as the energy deposition events due to the delta rays may extend several hundred microns or more. The committee believes that continued research on NTEs and their potential impact on cancer risk projections is highly warranted.

Qualitative Differences Between Low-LET Radiation and Heavy-Ion Biological Effects

The underlying assumption when using quality factors to scale from low- to high-LET risks, and the experimental RBEs on which the factors are based, is that the types of cell damage and response are the same with all radiation qualities. However, substantial evidence suggests that this assumption is not correct. The deposition of energy events for low-LET radiation is more sparsely distributed in cells and tissues, in contrast to the greater localization of energy along the particle track of heavy ions. These different properties result in qualitative differences at various levels of biological complexity, from DNA damage through to cellular and tissue responses. Starting at the DNA level, the distribution of lesions shows an increasing clustering and/or correlation of damage sites with the increasing ionization density of HZE particles, and hence there is an increasingly compromised ability of cells to repair the damage accurately. This can be recognized as increased mutability and changes in the spectrum of mutations (e.g., Zhu et al., 1996; Thacker et al., 1979) and at the chromosome level as increasingly complex chromosome aberrations with high-LET radiations (Anderson et al., 2000, 2005; Loucas and Cornforth, 2001; Loucas et al., 2004; Hande et al., 2003). At the cellular level these differences are reflected in the size of DNA repair-related protein foci and their slower resolution (Costes et al., 2006), differences in gene expression, and increased cellular inactivation.

Although the data are extremely limited, the animal carcinogenesis data are consistent with a different spectrum of cancer types (Weil et al., 2009) and reduced latency of cancer appearance (Burns et al., 1994; Dicello et al., 2004) after high-LET irradiation, suggesting underlying differences in the spectrum of damage types from low- and high-LET radiations.

Despite the limited data, the problems associated with applying universal quality factors based on a ratio such as RBEmax, which implies common biochemical and biological responses with low- and high-LET radiation, are clear. While this raises the question of whether universal LET-scaled (or Z*2/β2-scaled) quality factors are even

Suggested Citation:"2 Review of NASA Model." National Research Council. 2012. Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation. Washington, DC: The National Academies Press. doi: 10.17226/13343.
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appropriate to use, at this time there does not appear to be a better approach. Further, the current position taken by NASA in the proposed model is largely overly conservative, or overprotective rather than underprotective. Hence, this is the most reasonable approach that can be taken at the present time. Thus the committee concludes that the use of the “simplistic” RBE ratios and quality factors in predictive modeling is justified at this time, but an investigation of the qualitative differences in damage and cell or tissue responses dependent on LET and other track structure features is an area in great need of research, with a goal of developing a more mechanistic-based understanding of biological processes for high-LET radiations.

Data or Research That Could Improve This Portion of NASA’s Proposed Model

Although use of Z*2/β2 rather than LET has advantages and is a commendable improvement in NASA’s proposed model, there are still deficiencies that include, for example, the lack of allowance for differences in track width between two particles with identical Z*2/β2, and the absence of a description in the 2011 NASA report of differences in biological effects of delta rays with different energies. To date, the data used have been at an intermediate LET value. To test the validity of the incorporation of Z*2/β2 instead of LET into NASA’s proposed model, research is required to compare the biological effects by using either different particles of the same LET or individual particles at different energies and, hence, different LET values. Secondly, biological data with emphasis on cellular end points of relevance for cancer induction are needed for different particle types and energies, with different energy spreads of delta rays and track widths. The biological data should be obtained under conditions of low particle fluences (less than 1 particle traversal per cell). The biological effectiveness, derived from the data, needs to be interpreted in terms of stochastic track structure models. Additionally, studies are needed to demonstrate whether the end points related to DNA damage that have been widely used in studies of heavy particles are reasonable surrogates for cancer processes.

Due to the scarcity of data on cancer induction by particles relevant to space, research is required to obtain rates of cancer induction and/or a surrogate for cancer induction in animals by particles of different LET or Z*2/β2.

Studies at low dose rates or with fractionated doses, especially at the low fluences found in space, are needed with high-LET radiations. Recognizing the substantial difficulties, large numbers of animals needed, and the considerable expense in performing such studies in animals, new in vivo models are needed that accurately reflect cancer induction processes relevant to humans.

Recent data are consistent with the idea that the qualitative nature of damage and cell or tissue responses to the damage is different with high- versus low-LET radiations (e.g., reviewed in Held, 2009), as well as at high versus low radiation doses (e.g., Ding et al., 2005; Mezentsev and Amundson, 2011). Studies are needed to evaluate these differences in a systematic fashion and to provide insight on how, or whether, quality factors can appropriately be used if the nature of the damage is different with different radiation qualities.

Increasing evidence indicates that at low particle fluences (doses), dose-response curves may be non-linear due to NTEs. However, it is far from clear that NTEs such as bystander effects are necessarily detrimental, but may, at least in some cases, be beneficial due to removal of damaged cells (Prise et al., 2005; Portess et al., 2007; Schollnberger et al., 2007). Before an NTE component could be added to the risk model described in the 2011 NASA report, additional data are needed on NTEs at low particle fluences, for a range of particle types and energies, using cancer-relevant end points.

INTEGRATION AND COMPLETENESS OF THE MODEL

Background

General

As noted above, NASA’s proposed space radiation cancer risk assessment model follows essentially the same overall methodology as that of the current NASA model, developed in 2005, which is based on recommendations by the NCRP (2000) in Report No. 132 and is summarized in Chapter 7 of NCRP (2006) Report No. 153. NASA’s proposed model integrates a number of new findings and methods into its components, particularly by taking into

Suggested Citation:"2 Review of NASA Model." National Research Council. 2012. Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation. Washington, DC: The National Academies Press. doi: 10.17226/13343.
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account newer epidemiological data and analyses, new radiobiological data, and an improved method for specifying quality factors in terms of track structure concepts rather than LET. The newer epidemiological information includes the life span study incidence data, transferred to the U.S. population and converted to mortality from U.S. population statistics. In addition, the model provides an alternative analysis applicable to lifetime never-smokers (NSs). Details of the uncertainty analysis have also been improved.

Key Elements of Model

The key input components of NASA’s proposed model are summarized in Figure 2.7.

Each key element of NASA’s proposed model is discussed in an earlier section of this chapter and is further evaluated below. The key elements are as follows: (1) the LSS cancer incidence rates, which provide tissue-, age-, and gender-specific coefficients of excess relative risk and excess absolute risk per unit dose of low-LET radiation; (2) cancer incidence and mortality and all-cause mortality in the U.S. population and in never-smokers, which enable the dose coefficients to be converted from incidence to mortality risk in the U.S. population or in never-smokers; (3) a dose and dose rate effectiveness factor, to adjust the low-LET risks to low-dose-rate protracted exposures; (4) radiation quality factor (QF) in terms of track structure risk cross sections (representing risk from individual charged particles), with separate specifications for solid tumors and leukemia, to adjust the low-LET coefficients to coefficients for the particle radiations in the space environment; and (5) tissue-specific particle spectra, F(E,Z), and organ doses in the space-exposure radiation environment for which the risk of exposure-induced death is to be evaluated. NASA’s proposed model calculates, as its main output, age- and gender-specific REID for the estimation of mission- and astronaut-specific cancer risk and also the associated uncertainties in REID. In addition, the tissue particle spectra and fluences are used together with corresponding quality factors and NASA-chosen tissue weighting factors representative of the space radiation environment to obtain, for that particular environment, a summary quantity that NASA calls effective dose, but which is only partially related to the effective dose defined by ICRP. This summary quantity is for use as an operational measure.

image

FIGURE 2.7 Flow chart of inputs and computation of risk of exposure-induced death (REID) and risk of exposure-induced cancer (REIC) in NASA’s proposed model. The diagram is reproduced from the 2011 NASA report describing the proposed model. NOTE: Acronyms are defined in Appendix C of the present report. SOURCE: F.A. Cucinotta, M.-H.Y. Kim, and L.J. Chappell. 2011. Space Radiation Cancer Risk Projections and Uncertainties—2010. NASA/TP-2011-216155. NASA Johnson Space Center, Houston, Tex. July.

Suggested Citation:"2 Review of NASA Model." National Research Council. 2012. Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation. Washington, DC: The National Academies Press. doi: 10.17226/13343.
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As noted in Figure 2.7, the list of major underlying assumptions in NASA’s proposed model, and also in NASA’s current (2005) model, includes assumptions that (1) cancer risks from space radiation particles can be scaled from risks from low-LET gamma rays, (2) risks from protracted radiation are linear with dose and additive over the components of mixed high- and low-LET radiations, and (3) individual sensitivity is ignored. These are standard simplifying assumptions widely used in radiation protection.

Principal NASA Changes to the Model

The main elements that have been revised or brought up to date in NASA’s proposed model compared to its current (2005) model are highlighted in the Executive Summary of the NASA report (Cucinotta et al., 2011). The main elements are as follows:

1. Using the most recent tissue-specific cancer incidence risk coefficients derived from the LSS cohort data in Japan, the risks are transferred to an average U.S. population to estimate the probability of risk of exposure-induced cancer (REIC). Next the risk of exposure-induced death (REID) for cancer is obtained by applying estimated tissue-specific cancer survival rates in the United States and then summing up these individually estimated site-specific cancer mortality estimates.

2. An analysis is made of lung cancer and of other smoking-attributable cancer risks for never-smokers that shows significantly reduced lung cancer risks as well as reduced overall cancer risks compared to risks estimated for the average U.S. population. The lung cancer radiation risks for non-smokers are derived by applying lung cancer rates for never-smokers to the standard LSS risk coefficients.

3. Derivation is made of track-structure-based empirical radiation quality factors that depend on charge number, Z, and kinetic energy, E, in place of a dependence on LET alone.

4. There is specification of a smaller estimated maximum for the quality factor for leukemia than for solid cancers when estimating cancer site-specific risks for high-LET exposures.

5. The use of the ICRP tissue weights is shown to overestimate cancer risks from solar particle events by a factor of two or more. Summing cancer risks for each tissue is proposed in NASA’s proposed model as a more accurate approach to the estimating of SPE cancer risks. However, to define a summary metric of space radiation exposures under realistic space environments, which NASA calls effective dose, it is proposed to choose gender-specific tissue weights to represent that environment. This quantity is only partially analogous to the protection quantity that ICRP defines as effective dose.

6. The uncertainty distributions for all model coefficients in the proposed NASA model (physics, low-LET risk coefficients, DDREF, and quality factors) have been updated and revised.

7. A statistical risk assessment model for SPEs is included.

Integration

The elements listed above are combined into an integrated model, which is available for application on a laptop by means of a graphical user interface screen. The low-LET risk coefficients, adjusted to protracted exposures by the DDREF, are folded with the quality factors and the tissue-specific particle fluence spectra so as to obtain age- and tissue-specific risks under the particular exposure conditions. The risks are then summed across attained age, with adjustment for competing causes of death, to obtain REID. Alternative outputs include REIC, so that it is also available for future use if required, and also a summary quantity that NASA calls effective dose for a particular radiation environment to use for operational purposes. Tables 6.2.a (females) and 6.2.b (males) in the 2011 NASA report (Cucinotta et al., 2011) provide a comparison of effective dose limits on 1-year missions for a 3 percent REID. In these comparisons, effective dose limits from NASA’s current model, from BEIR VII (NRC, 2006), and from NASA’s proposed model are provided for ages 30, 40, 50, and 60 years. In addition, dose limits in NASA’s proposed model are given separately for the average U.S. population and for never-smokers. Of particular note for practical purposes, there are several differences between the values in NASA’s current (2005)

Suggested Citation:"2 Review of NASA Model." National Research Council. 2012. Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation. Washington, DC: The National Academies Press. doi: 10.17226/13343.
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model and those for NASA’s proposed model’s never-smokers that will impact the number of safe days in deep space (see Table 6.9 in the 2011 NASA report [Cucinotta et al., 2011]).

The tissue-specific cancer mortality risk equation in its track structure-based form for heavy-ion exposures in NASA’s proposed model is described in the 2011 NASA report as

image

where λimageM (aE, a) is the tissue-specific mortality coefficient for low-LET radiation, DT(Z,E) is the tissue-absorbed dose from particles of charge Z and energy E, and FT(Z,E) is the corresponding tissue fluence of these particles. The term in curly brackets partitions the risk from each heavy ion into a low-LET component and a high-LET component with relative proportions (1 - P(Z,E)) and P(Z,E), respectively. P(Z,E) is dependent on the quality of the radiation and is given by

image

where Z* is the effective charge number of the particle, β is its speed relative to the speed of light, and κ, m, and Σ0/αimage are empirical parameters, the values of which have been chosen to give what is deemed to be a suitable QF relationship.

Uncertainties

Table 6.5 of the 2011 NASA report summarizes the components of uncertainty that are quantified in NASA’s proposed model. The model expresses uncertainty associated with low-LET radiation dose in terms of site-specific, radiation-related excess absolute risk and excess relative risk treated separately, multiplying them by independent random variables, as follows: XS, here denoting statistical error, is assumed to be normally distributed with mean 1.0 and standard deviation (SD) 0.15, and XB, here representing uncertain bias in incidence data, is normal with mean 1.0 and SD 0.05, as in NCRP (1997) Report No. 126. XD, here representing dosimetry errors, is treated as lognormal with geometric mean (GM) 0.9 and geometric standard deviation (GSD)1.3. For low doses, and for higher doses delivered at low dose rates, the product of XS, XB, and XD is divided by XDDREF which is assumed to be equal to 1.75 times a left-truncated (at 0.75) lognormal random variable with GM 1.0 and GSD 1.4 before truncation. For transfer of LSS-based risk estimates to non-smoker U.S. populations, the site-specific LSS EAR estimates were used for breast cancer and the site-specific ERR estimates, times baseline, were used for thyroid cancer; for digestive cancers, differential additive weights of 0.3 for EAR and 0.7 for ERR times baseline were used, whereas for leukemia, cancers of the lung, and other cancer sites, equal additive weights were used (i.e., 0.5 for EAR and 0.5 for ERR times baseline).

Table 6.5 of the 2011 NASA report (Cucinotta et al., 2011) also includes uncertainties related to the quality and doses (or fluences) of space radiation. The quality factors (QFs) are defined as functions of Z*2/β2 of the particles in terms of three empirical parameters: (1) Σ0/αimage times a lognormal random uncertainty factor with GM = 0.9 and GSD = 1.4; (2) κ, multiplied by an uncertainty factor distributed as N(1,0.2); and (3) m, taking discrete values 1.5, 2, 2.5, 3, 3.5, and 4 with probabilities 0.05, 0.1, 0.2, 0.4, 0.2, and 0.05, respectively. The space environment physics-related uncertainties for high-energy charged particles including protons, neutrons, and heavier atomic nuclei are expressed in table 6.5 by a random multiplier F(Z*2/β2) distributed as N(1.05, 0.333) for Z < 5 and distributed as N(1.0, 0.25) for Z ≥ 5.

The components of uncertainty are combined in NASA’s proposed model by Monte Carlo methods, so as to obtain the probability distribution for REID.

Model Completeness, Improvements, and Recommendations

Each of the key elements of NASA’s proposed model is discussed in detail in previous sections of this report. In addition, those sections describe how NASA’s proposed model differs from its current (2005) model and how

Suggested Citation:"2 Review of NASA Model." National Research Council. 2012. Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation. Washington, DC: The National Academies Press. doi: 10.17226/13343.
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adequately these changes have been incorporated into the proposed model. In this section, the committee provides an evaluation of the completeness of each key element of the model and, in particular, how these elements are integrated to produce NASA’s final proposed space radiation cancer risk assessment model. The approach that the committee used is to assess the completeness of each element as presented by NASA and, based on this evaluation, to discuss possible improvements to the model together with recommendations and suggestions for addressing the gaps addressed by the possible improvements. In some cases, additional research might be needed to enhance NASA’s proposed model.

On the basis of its review, the committee notes that the general approach to estimating cancer risks from exposure to low-LET radiation follows the approach utilized by ICRP, NCRP, EPA, and BEIR VII, and as such that it is state of the art. The specific data incorporated into NASA’s proposed model are generally appropriate, with some exceptions, noted below, relating to new data that have become available since the development of the model or additional data sets that were already available and not selected for use by NASA. In addition, the committee concludes that for the estimation of REID from space radiation, NASA has identified the major issues and, as discussed below, has suitably addressed these, although in a few cases the approach has raised some questions and concerns. These comments are made within the framework that NASA needs for assessing individual risks and providing a summary metric for operational purposes. The following sections discuss the key elements of the proposed NASA model.

Key Element: Cancer Risk Projection Model (Low-LET Radiation)

Model Completeness

The cancer risk projection component of NASA’s proposed model is as complete as our current understanding allows, and the general approach follows standard methods also used by national and international radiation protection committees. However, the committee has a number of recommendations for improvements to the model inputs and for modification of aspects of the approach. The key recommendations and conclusions are summarized below. The details are provided in the section above entitled “Cancer Risk Projection Models.”

Model Improvements and Recommendations

Cancer Mortality Risk Estimation. A major proposed change in NASA’s proposed model is to use the “incidence-mortality” approach developed by BEIR VII (NRC, 2006) whereby risk coefficients from LSS cancer incidence models are used, and then cancer mortality risks are estimated from these incidence risks. The approach results in considerable changes in the REID estimates, particularly in the pattern with age at exposure. The committee considers the approach to be an improvement for site-specific cancer mortality estimation because LSS site-specific cancer incidence data are likely to be more accurate than are cancer mortality data, which suffer from misclassification of causes on death certificates.

Recommendation: Before NASA implements its proposed major change to the “incidence-mortality” approach, the committee recommends that NASA conduct more research into the specific patterns of the underlying epidemiological biases that drive these changes. The committee also highlights a specific problem with the method of estimating the mortality probability from the ratio of cancer mortality to incidence as developed by the BEIR VII report published by the National Research Council in 2006 and proposed for use by NASA. In response, the committee recommends that NASA consider alternative methods for improved estimation of mortality probabilities for each cancer site. For example, as presented in its 2011 report EPA Radiogenic Cancer Risk Models and Projections for the U.S. Population, the Environmental Protection Agency has developed an alternative approach for breast cancer mortality estimation, and this could serve as a suitable approach to be applied by NASA.

Transfer of Risk Estimates from the Japanese to the U.S. Population. Because underlying cancer incidence rates for some cancer sites differ greatly between the Japanese and the U.S. populations, risk estimates based on

Suggested Citation:"2 Review of NASA Model." National Research Council. 2012. Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation. Washington, DC: The National Academies Press. doi: 10.17226/13343.
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an excess relative risk model can give very different REID values from those based on an excess absolute risk model. A number of organizations and committees have recommended that a weighted average of the ERR and EAR models be used. The proposed NASA approach follows BEIR VII (NRC, 2006) in calculating a weighted average with uncertain weights and generally follows the recommended BEIR VII weights.

Recommendation: Because there are some deviations in NASA’s proposed model from the weights recommended by BEIR VII for the excess relative risk and excess absolute risk models, the committee recommends that NASA provide additional justification for these alternative weights. (See additional comments below on never smokers.)

Dose and Dose Rate Effectiveness Factor. A DDREF value is applied to adjust the LSS-based cancer risk coefficients to apply to low-dose-rate protracted exposures, following conventional practice. In NASA’s proposed model, it is assumed that the DDREF applies only to gamma rays and that there is no dose-rate dependence of risks from space radiation. Differences between space radiation charged particles and gamma rays at low dose-rate are encompassed entirely within the quality factor (QF) discussed below. This is a standard assumption for high-LET radiations in the field of radiation protection. NASA’s proposed model uses an uncertain DDREF with a median value of 1.75, which was based on subjective uncertainty distributions developed for the NIH radioepidemiological tables (NIH, 2003). A number of publications produced since the NIH report are relevant to this issue.

Conclusion: Although the proposed NASA approach for estimating a DDREF describes a number of limitations in these newer epidemiological studies and in the BEIR VII DDREF methodology, the justification given for preferring the older NIH approach is that it is close to the average of various recommended values of slightly less than 2. The use of this average value is somewhat problematic given that the recommended values that are used to derive this average are not independent and thus applying equal weights to these is not justifiable.

Recommendation: The committee agrees with the use of an uncertainty approach for estimating DDREF, but it recommends that NASA use a central value and distribution that better accounts for the recent epidemiological and laboratory animal data.

Risk Models for Never-Smokers. The issue of the smoking status of the astronauts and the potential implications on risk projections for smoking-related cancers are important, and it is appropriate that this should be investigated. Most astronauts are non-smokers, which would likely lower the risk projections for astronauts compared to estimates for the general population (a mixture of never- and ever-smokers).

Recommendation: The proposed NASA approach for estimating lung cancer risks for astronauts who are never-smokers is limited and does not consider competing risks. Thus, the committee recommends that the NASA approach be developed further, given the important impact that it has on reducing estimated risk. The revised approach should use survival probabilities for competing risks that are specific to never-smokers. Further, the committee recommends that NASA make no changes at this time in the proposed model to include other smoking-related cancers. The data are not sufficiently robust for use in the modification of the REID estimate.

Key Element: Radiation Quality and Track Structure Risk Cross Section

Model Completeness

As in the current NASA model, quality factors in NASA’s proposed model are applied to convert the low-dose low-LET cancer risk coefficients to risk coefficients for the variety of charged particles from space radiation. The underlying assumption is that the age- and tissue-specific risks can be simply scaled from low-LET radiation to HZE particles. Although there are known to be qualitative differences between the biological damage inflicted by

Suggested Citation:"2 Review of NASA Model." National Research Council. 2012. Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation. Washington, DC: The National Academies Press. doi: 10.17226/13343.
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high-LET and low-LET radiations at the molecular and higher levels and there are possibly also different spectra of cancer types and latency, the committee agrees that simple scaling of risks is reasonable, given the current limited state of knowledge of qualitative differences in health effects (see also the section below entitled “Key Element: Other Issues”).

The main radiation quality parameter is Z*2/β2, and this largely takes the place of LET, although three additional parameters (κ, Σ0/αimage and m) are also introduced by Equation 2.2 above to define P(Z,E) in the quality factor relationships for risk as a function of Z*2/β2. According to Equation 5.21 of the 2011 NASA report (Cucinotta et al., 2011):

image

Thus, QFs are created for NASA’s proposed model on the basis of a number of empirical relationships and parameters that have been judged to give a reasonable description of the RBEs for a variety of available radiobiological data for heavy ions. These available radiobiological data are from animal and cellular systems but are quite limited in scope and in their relationship to carcinogenesis, as described in the section above entitled “Cancer Risks and Radiation Quality in the Model.” The parametric forms of the QF relationships have been guided by aspects of track structure and by aspects of a specific biophysical model, but it is stated that the interpretation of the parameters is not tied to any particular model. These empirical relationships replace the Q(L) relationship used in the current NASA risk model. The committee considers NASA’s proposed definition of QF to be reasonable and agrees that the parametric forms should be regarded as essentially empirical and not as having been formally derived from biophysical first principles.

Conclusion: In the proposed model, different maximum values of quality factor, QF, are assumed for leukemia (maximum 10) and for solid tumors (maximum 40). This is a change from the current NASA risk model. The committee agrees that it is reasonable to make such a distinction on the basis of the limited animal and human data available.

The concepts underlying this new parameterization of QF and the rationale for their use are discussed in the section above entitled “Cancer Risks and Radiation Quality in the Model.” Overall, the committee considers that the new parameterization, although more complicated, provides an improvement over the previous LET descriptions, particularly for the wide diversity of HZE and lower-energy charged particles to which astronauts are exposed in space. Not only are the new QF relationships likely to be more accurate for the proposed risk model, but they are also more amenable to uncertainty analysis and can guide future research aimed at reducing the parameter uncertainties and improving the form of parameterization.

At a purely empirical level, plotting the relative biological effectiveness of charged particles against Z*2/β2 does tend to bring the RBE data closer toward a single curve (for a given biological system) compared to the plotting of RBE against LET. However, as is noted in the 2011 NASA report, the uncertainties related to the quality factor are still the largest contributor to the overall uncertainty of REID.

Model Improvements and Recommendations

For NASA’s proposed model, values for the three parameters that define QF have been selected by comparison with experimentally observed variations in biological effectiveness with radiation type for some cellular biological effects and by considering the few available data on the induction of cancer by high-LET radiation. However, it is not clear from the 2011 NASA report how the particular values were decided, including the ad hoc selection of different values for parameters κ and Σ0/αimage for ions of Z ≤ 4 compared to all ions of higher charge, and what analyses were carried out.

Suggested Citation:"2 Review of NASA Model." National Research Council. 2012. Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation. Washington, DC: The National Academies Press. doi: 10.17226/13343.
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Recommendation: The committee recommends that NASA make a detailed comparison of the relative biological effectiveness versus Z*2/β2 dependence of the experimental data with the proposed form and parameters of the quality factor, QF, equation in order to improve the transparency of the basis for the selection of the proposed parameter values for the model and to provide guidance for future research to test, validate, modify, and/or extend the parameterization. This analysis needs to include the defined selection of different values for parameters κ and Σ0/αimage for ions of Z ≤ 4 compared to all ions of higher charge.

The risk equation of NASA’s proposed model (see Equation 2.1 above) follows directly from the proposed risk cross-section Equation 5.19 of the 2011 NASA report (Cucinotta et al., 2011), namely:

image

This equation partitions the risk from a single heavy ion into two components, one of which behaves exclusively as the low-LET component of energy deposition and the other exclusively as the high-LET component, with no radiobiological interaction between the two components. This important assumption is introduced in the 2011 NASA report as Equation 5.19 (Cucinotta et al., 2011), but without any clear attempt to justify it or to present a rationale for its use. The committee recommends that work be carried out to validate this assumption.

Key Element: Effective Dose

Model Completeness

The ICRP defines the quantity “effective dose” for use in radiation protection and states that it is intended mainly for use in “prospective dose assessment for planning and optimisation in radiological protection, and demonstration of compliance with dose limits for regulatory purposes” (ICRP, 2007, p. 13). Its computation includes tissue weighting factors (wT), the specific normalized values of which are defined by ICRP for the individual tissues of the body as an approximate gender-averaged representation of the relative contribution of each tissue to the radiation detriment of stochastic effects from uniform whole-body low-LET irradiation.

The 2011 NASA report defines its own quantity, which it also calls effective dose, and it states that this quantity is strictly for internal NASA use. This NASA quantity is analogous to ICRP effective dose but is based on NASA’s own gender-specific sets of relative tissue weights (wT) for the space radiation environment and NASA’s definition of quality factor for the space radiation environment. Values for wT are chosen in the 2011 NASA report to match the estimated tissue-specific components of REID from the various tissues in representative space radiation environments, including the high-LET radiation components. Values of wT are calculated by NASA’s proposed model. These NASA wT values can differ substantially from the ICRP values for a variety of reasons, particularly due to gender-specificity, inclusion of only radiation-induced death from cancer and not other forms of health detriment, the low penetration of some components of space radiation (such as the lower-energy SPE protons), and different QFs for leukemia compared to solid cancers. The committee considers NASA’s proposed model to be an appropriate tool for calculating this summary metric.

It should be noted that the partly substantial changes in weighting factors that NASA uses for evaluating its effective dose in comparison to the ICRP effective dose do not lead to major differences under many space radiation environments, as illustrated in Figures 6.4 and 6.5 in the 2011 NASA report (Cucinotta et al., 2011). In circumstances in which there are large differences, such as from SPE exposures, it is explained in the report that these are due to the relatively much larger doses to superficial organs compared to the more cancer-prone deep-seated organs. It would be useful to make clear that the main driver of these differences in effective dose is the chest/breast, which alone is responsible for about 40 percent of the total ICRP effective dose in the case of the SPE

Suggested Citation:"2 Review of NASA Model." National Research Council. 2012. Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation. Washington, DC: The National Academies Press. doi: 10.17226/13343.
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example given in Table 2.7 of the 2011 NASA report (Cucinotta et al., 2011), due to the large superficial dose to this tissue and to its large ICRP tissue weighting factor (0.12).

Model Improvements and Recommendations

As described above, NASA proposes to use its own summary quantity for mission operational purposes, and in the 2011 NASA report this quantity is simply called effective dose. However, “effective dose” is, strictly speaking, a quantity defined by ICRP, including ICRP-defined specification of numerical values for weighting factors and gender-averaging. If different tissue weighting factors and radiation quality specifications are used and effective dose is evaluated without gender-averaging, it is problematical for the resulting quantity still to be called effective dose and the unit sievert given to its numerical values. The committee believes that the NASA description of the proposed model would be improved by the use of terminology and notation that distinguish NASA-defined quantities (especially effective dose) from those defined by ICRP. Wide dissemination of the definition of “effective dose” within NASA and the research community would reduce confusion about what exactly is being measured or limited.

It is not clear from the 2011 NASA report in what ways the NASA-defined effective dose will be implemented as a summary variable for mission operations. The provision of further information would be useful, including what quantity or quantities are used for the calibration of area and personal monitors in space vehicles and how these calibration quantities compare with the NASA versions of effective dose.

Key Element: Tissue-Specific Particle Spectra, F(E,Z)

Model Completeness

The assessment of cancer risk due to space radiation begins with defining the external (or ambient) radiation environments, which in turn are inputs to transport-shielding calculations to obtain the local radiation environment, modified by spacecraft and body shielding, at tissues of concern. GCR and particles from SPE are two major components3 of the space radiation environment. Hence, tissue-specific particle fluence spectra, denoted by F(E,Z), are obtained as input to the cancer risk calculation, together with all of the components described above. The committee concludes that NASA’s proposed model uses standard and well-studied methods for the specification of the space radiation environment and computation of the tissue-specific particle spectra.

Model Improvements and Recommendations

The radiation environment and shielding transport models in NASA’s proposed model are considered by the committee to be a major step forward compared to previous models used (especially the introduction of the statistical SPE model to NASA’s proposed model in place of the current SPE model). The proposed SPE model is not actually a probabilistic risk assessment (PRA), as is suggested in the Executive Summary of the 2011 NASA report (Cucinotta et al., 2011), but rather a statistical model developed using a data set from past measurements.

The committee agrees that the proposed radiation environment and shielding transport models have been developed with the extensive use of available data and rigorous mathematical analyses. The uncertainties conservatively allocated to the space physics parameters (i.e., environment and transport models) are deemed adequate at this time, considering that the space physics uncertainty is only a minor contributor to the overall cancer risk assessment. The currently used paradigm for both galactic cosmic rays and solar energetic particles is based entirely on the statistics obtained from past measurements. The committee agrees that the specification of the space physics parameters is done as well as it can be with this approach, and it may well be adequate for NASA’s purpose given other uncertainties in REID. However, the committee suggests that estimates could be improved by adding physics-based studies of particle transport using the current picture of the heliosphere and its electric and magnetic fields. Particle transport in the interplanetary medium is determined by the electric and magnetic fields

image

3As stated in footnote 1 at the beginning of this chapter, trapped-particle models are not covered here because they contribute very little to the organ dose for missions aboard the International Space Station or missions to the Moon or Mars.

Suggested Citation:"2 Review of NASA Model." National Research Council. 2012. Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation. Washington, DC: The National Academies Press. doi: 10.17226/13343.
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in the solar wind. Theoretical and numerical studies of particle trajectories should certainly result in improved transport models and smaller uncertainties in the environmental estimates. However, this would involve a major effort and change in modeling approach and may not be warranted in view of the relatively minor contribution of space physics uncertainties.

Key Element: Uncertainties

Model Completeness

The committee considers that the handling and combining of uncertainties in the NASA’s proposed organ-site-specific models, as presented in Table 6.5 of the 2011 NASA report (Cucinotta et al., 2011), are logical and appropriate as applied to statistical errors, bias in cancer incidence data, dosimetry errors, transfer model weights, and DDREF for low-LET radiation, and for radiation quality factors and risk cross sections for space radiation, as well as for the physics uncertainties of space radiation.

It is not clear, however, that the use of empirical Bayes estimates for the calculation of cancer risks for multiple organ sites, as discussed in Sections 3, 3.1, and 4.2 and in Table 4.1 of the 2011 NASA report, offers any advantage for estimating cancer mortality risk for all organ sites combined, as discussed in the following section.

Model Improvements and Recommendations

NASA’s proposed model does not include an upward bias correction for dosimetric uncertainty in the underlying life span study data (Pierce et al., 2008); however, the correction would be small at the dose levels anticipated for astronauts in the near future. In the 2011 NASA report, the discussion of the use of a maximum likelihood estimate (MLE) and empirical Bayes (EB) estimate of site-specific ERR per sievert in Section 4.2 and Table 4.1 is somewhat confusing. For example, as shown in the table, the site-specific EB estimate of ERR per sievert for kidney cancer (0.40) would be similar to the MLE (also 0.40 for this particular organ site), with a lower estimated standard error (0.19) compared to the MLE standard error of 0.32. This difference is due to the fact that the site-specific EB estimate uses additional risk information from organ sites other than the kidney, and the MLE estimate does not. However, the variance (and therefore the standard error) of the summed site-specific estimates of ERR per sievert over all cancer site groupings in the table should be similar for the MLE and EB approaches, because the EB error calculation would include information from the (mostly positive) off-diagonal elements of the covariance matrix as well as the estimated variances. It is not clear whether the EB approach has been used in NASA’s proposed model.

Recommendation: On the assumption that the empirical Bayes approach has been used in NASA’s proposed model, the committee recommends that the authors ensure that the off-diagonal covariance information has been taken into account. If the EB approach has not been used, either this fact should be stated in the text of the 2011 NASA report (Cucinotta et al., 2011) or the references to the EB approach should be removed from the text.

The uncertainty analysis in NASA’s proposed model reveals that the value of QF is the largest contributor to the uncertainty of REID, alone introducing about 3.4-fold uncertainty in risk. Additionally, it is found that this component is reduced to 2.8-fold uncertainty if two of the track structure parameters are constrained to a fixed algebraic relationship to one another (such that the Z*2/β2-position of the maximum value of QF is held fixed).

Conclusion: NASA’s proposed model discusses the observation that the use of a fixed relationship between two track structure parameters reduces the uncertainty as being a potentially valuable finding that may provide a method to reduce uncertainty in estimations of the REID. However, little indication is given in the 2011 NASA report as to why such a fixed position might be justified or expected. The

Suggested Citation:"2 Review of NASA Model." National Research Council. 2012. Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation. Washington, DC: The National Academies Press. doi: 10.17226/13343.
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committee suggests that further investigations into the validity and usefulness of this approach would be worthwhile.

Key Element: Other Issues

Non-Cancer Effects (Tissue Reactions)

In its proposed approach to estimating the safe days in deep space, NASA has used a 3 percent REID for fatal cancer as the limit. In its current (2005) model, NASA also considers dose limits for non-cancer effects—lens, skin, blood-forming organs, heart and central nervous system. For example, “career limits for the heart are intended to limit the REID for heart disease to be below approximately 3 to 5 percent, and are expected to be largely age and sex independent” (NASA, 2005, p. 65). It was further assumed by NASA that the limits established would restrict mortality values for these non-cancer effects to less than the risk level for cancer mortality. The cancer and non-cancer risks were not combined into a single REID. More recent data have led ICRP to reconsider the threshold dose values, particularly for the cardiovascular system (and cataracts) (see ICRP, 2011). It is concluded by ICRP (2011) that a threshold absorbed dose of 0.5 Gy should be considered for cardiovascular disease (and cataracts) for acute and for fractionated/protracted exposures. It is appreciated by ICRP that these values have a degree of uncertainty associated with them. For example, several reports suggest that effects on the cardiovascular system occur only at much higher dose levels (Mulrooney et al., 2009; Davis et al., 1989).

Conclusion: The revised value for the threshold dose value proposed by ICRP suggests that NASA may need to consider how it might account for cardiovascular disease in their calculations of dose limits. However, it is noted that to date there is very little information on RBE for non-cancer effects that is needed for risk estimates for space radiation exposures.

By continuing to monitor developments in the area of potential cardiovascular effects at low doses of radiation, NASA would have the opportunity to determine if there is a need to modify the proposed estimates of REID.

Delayed Effects

Delayed effects pertinent to the assessment of risk principally relate to observations whereby radiation-induced genomic instabilities have been reported, as measured by the appearance of a delayed increase in the rates of new chromosomal aberrations, mutations, or micronuclei in cells several cell generations after irradiation. Such effects could have important implications for radiation protection in view of current notions of the multistep mutational processes involved in carcinogenesis. An early induced change in subsequent and ongoing mutation rates in irradiated somatic cells could accelerate this process.

Conclusion: There are conflicting reports on the generality of the phenomenon of radiation-induced delayed genomic instability and some question about variation in susceptibilities of cells from different individuals with regard to this effect. Thus, the committee concludes that it is appropriate that genomic instability not be incorporated into the model, in agreement with the proposed NASA approach. However, the committee considers that further investigation of the phenomenon is certainly warranted.

Non-Targeted Effects

Non-targeted effects largely refer to the so-called bystander effects, by which responses can be produced in an unirradiated cell as a result of the transfer of a signal from an irradiated cell. For HZE radiations, doses that may be received by astronauts are very non-uniform in the sense that some cells will be traversed by the primary particle itself, whereas other cells will not be traversed; thus, an NTE is also a phenomenon that is of considerable interest.

Suggested Citation:"2 Review of NASA Model." National Research Council. 2012. Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation. Washington, DC: The National Academies Press. doi: 10.17226/13343.
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Conclusion: While the 2011 NASA report contains an extended discussion on non-targeted effects (NTEs) and their potential impact on risk estimates, they appropriately chose not to include these NTEs in their proposed model at this time. Little is known in qualitative or quantitative terms of the contribution of these NTEs directly related to radiation-induced carcinogenesis, but the committee believes that studies to elucidate any such relevance should be encouraged.

Qualitative Differences

It is recognized that there are qualitative differences in the nature of the initial energy depositions and hence in initial chemical, biochemical, and biological damages from different types of ionizing radiation. Differences are particularly great between low-LET gamma rays and the wide variety of high-LET heavy ions in space radiation. This may lead to observed differences in responses of cells, tissues or organisms such as different spectra of mutations and chromosome aberrations, altered gene-expression patterns, and different types and latencies of cancer. There is experimental evidence for qualitative differences at each of the above levels of biological effect. As a result, it may not be entirely appropriate to apply universal QFs as quantitative scaling factors, based on quantities such as RBE that assume similar underlying biological processes. This is an area in which experiments quantifying types, frequencies, and latencies of various cancers—for example, lung, colon, and breast cancer—as well as the studies of leukemia and liver cancer mentioned above, are sorely needed for radiations of varying LET, especially at low particle fluences such as in space. Furthermore, the tumor studies would need to be coupled with appropriate mechanistic investigations to provide understanding of the underlying carcinogenic processes.

Probabilistic Risk Assessment

The performance of a probabilistic risk assessment of spaceflight cancer risk implies the consideration of a comprehensive set of radiation exposure scenarios involving an array of radiation hazards over a long period of time and an assessment of the vulnerability of a complex engineered system under a variety of threats. Although it was not the intent of NASA’s proposed model to be comprehensive in terms of the risk scenarios, as the focus was limited to the health effects component of a total system risk model, it is important to recognize the limitations of the model in reference to best practices in total-system PRA. Details were lacking in the 2011 NASA report regarding the PRA context of NASA’s current (2005) model and the planned steps toward an eventual total-system cancer risk model.

Eventual movement to a total-system cancer risk model would require the development of scenario sets that include not only the quantification of the health effects but the details of the dynamics of the radiation source term and consideration of the “what can go wrong” scenarios associated with specific missions. Examples of such scenarios are unexpected solar particle events and a failure of radiation protection systems. The extent to which NASA’s current (2005) model accommodates multiple scenarios is not clear. Experience suggests that several exposure and shielding scenarios will have to be considered should the decision be made to perform comprehensive, mission-specific risk assessments.

The 2011 NASA report (Cucinotta et al., 2011) does make clear that there are only two radiation sources of interest, GCR and SPE. The report includes data suggesting that the GCR radiation environment is well characterized and nearly constant over time during solar-cycle activity minima. The report also includes data suggesting that SPEs may not contribute significantly to risk during solar-cycle activity minima and may be mitigated by shielding. This may be the case with respect to the radiation source term, although it is doubtful, but it is certainly not the case with respect to quantifying “what can go wrong” scenarios. It is possible that a single radiation hazard scenario is sufficient for a comprehensive PRA, but it would be a unique circumstance in the practice of PRA, and there is a lack of evidence to support such a condition.

It is always possible that many “what can go wrong” scenarios can be recovered from during a mission, but if that is part of the process, then recovery activities, such as the possible need for extravehicular activity, also have to be evaluated for their contribution to the overall risk. Evidence was not presented to indicate that such scenarios were actually considered or, that if they were, how they entered into the risk assessment.

Suggested Citation:"2 Review of NASA Model." National Research Council. 2012. Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation. Washington, DC: The National Academies Press. doi: 10.17226/13343.
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A major strength of comprehensive PRA is the quantification of the uncertainties and the importance ranking of the contributors to risk. The absence of the importance ranking of the contributors, including their uncertainties, compromises the comprehensiveness and possibly the capability of NASA’s proposed model. To be sure, the NASA methodology for characterizing uncertainty in the health effects model is comprehensive, but the point is that there is much more to a comprehensive risk assessment of the cancer risk of a space mission than the health effects model. In order to perform total-system and comprehensive risk assessments, much more attention to detail is required, particularly with respect to quantifying other factors contributing to risk, such as the engineering driven “what can go wrong” scenarios that experience indicates to be major contributors to risk.

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Suggested Citation:"2 Review of NASA Model." National Research Council. 2012. Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation. Washington, DC: The National Academies Press. doi: 10.17226/13343.
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Page 49
Suggested Citation:"2 Review of NASA Model." National Research Council. 2012. Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation. Washington, DC: The National Academies Press. doi: 10.17226/13343.
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Page 50
Suggested Citation:"2 Review of NASA Model." National Research Council. 2012. Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation. Washington, DC: The National Academies Press. doi: 10.17226/13343.
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Page 51
Suggested Citation:"2 Review of NASA Model." National Research Council. 2012. Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation. Washington, DC: The National Academies Press. doi: 10.17226/13343.
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Page 52
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 Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation
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NASA's current missions to the International Space Station (ISS) and potential future exploration missions involving extended stays by astronauts on the lunar surface, as well as the possibility of near- Earth object (NEO) or Mars missions, present challenges in protecting astronauts from radiation risks. These risks arise from a number of sources, including solar particle events (SPEs), galactic cosmic rays (GCRs), secondary radiation from surface impacts, and even the nuclear isotope power sources transported with the astronauts. The serious early and late radiation health effects potentially posed by these exposures are equally varied, ranging from early signs of radiation sickness to cancer induction. Other possible effects include central nervous system damage, cataracts, cardiovascular damage, heritable effects, impaired wound healing, and infertility.

Recent research, much of which has been sponsored by NASA, has focused on understanding and quantifying the radiation health risks posed by space radiation environments. Although many aspects of the space radiation environments are now relatively well characterized, important uncertainties still exist regarding biological effects and thus regarding the level and types of risks faced by astronauts.

This report presents an evaluation of NASA's proposed space radiation cancer risk assessment model, which is described in the 2011 NASA report, Space Radiation Cancer Risk Projections and Uncertainties—2010. The evaluation in Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation considers the model components, input data (for the radiation types, estimated doses, and epidemiology), and the associated uncertainties. This report also identifies gaps in NASA's current research strategy for reducing the uncertainties in cancer induction risks.

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