Saving Women's Lives Strategies for Improving Breast Cancer Detection and Diagnosis (2005) / Chapter Skim
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Appendix C: ROC Analysis: Key Statistical Tool for Evaluating Detection Technologies
Appendix C: ROC Analysis: Key Statistical Tool for Evaluating Detection Technologies
Pages 314-321
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From page 314... ...
For instance, each radiologist has his or her own visual clues guiding them to a clinical decision as whether the pattern variation of a mammogram indicates tissue abnormalities or just normal variation. The varying decisions make up a range of decision thresholds.
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In the case of interpreting mammograms, different radiologists with different decision thresholds can affect the clinical outcome in the assessment of nonobvious mammograms. ROC CURVES ARE NECESSARY TO CHARACTERIZE DIAGNOSTIC PERFORMANCE The ROC curve maps the effects of varying decision thresholds, accounting for all possible combinations of various correct and incorrect decisions.4 A ROC curve is a graph of the relationship between the truepositive rate (sensitivity)
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FIGURE C-1 Receiver operating characteristic (ROC) graph of a varying decision threshold compared with a "useless test." The three decision thresholds discussed in the previous section are represented on this graph.
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From page 317... ...
The ROC approach can be used to accurately compare breast cancer diagnostic tests. The ROC plot provides a visual representation of the accuracy of a detection test, incorporating not only the intrinsic features of the test, but also reader variability.
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The greater area under the ROC curve in the example indicates that computer-assisted mammography increases a radiologist's ability to correctly identify neoplastic breast tissue, as well as to avoid false alarms. FIGURE C-3 Comparison of two diagnostic modalities without ROC curves.
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However, clinical studies that incorporate more case variation to measure the diagnostic accuracy of a modality will have significantly more clinical value than studies based on little case variation. Variability in cancer detection tests has two main components, reader variability and case-sample variability.
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The MRMC study design has several advantages over the collection of single-reader ROC analyses because MRMC analysis provides a quantitative measure of the performance of a diagnostic test across a population of readers with varying degrees of skill. Even though having more than one reader increases variability in the measurement, MRMC studies can be designed so that the statistical power of differences between competing modalities will be greater than if only one reader's interpretation is used.1 When using MRMC methodology, statistical models can be used to account for both case variability and reader variability.
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From page 321... ...
6. Wagner RF, Beiden SV, Campbell G, Metz CE, Sacks WM.
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