Design and validate a new test or new battery of tests to assess the competence of radiologists when screening (i.e., interpreting) mammograms for tumours.
Download and read the background information references below to gain an understanding of what is known about the expertise required to detect tumors in mammograms. Consider (1) what measures previous researchers have found to predict performance in this skill, and (2) what methods/ tests researchers have used to measure performance in the past. One option might be to develop a new simulation test where participants view lots of example mammograms and have to indicate whether a tumor is present or not. One way to assess the validity of such a measure might be to compare novice radiologists with expert radiologists (i.e., a group you would expect to be bad at detecting tumors vs. a group you would expect to be good at detecting tumors). If your new test could tell these groups apart (i.e., experts score significantly higher than novices), then this could be considered evidence for its validity.
In some of the suggested references below, you may find yourself unfamiliar with some of the performance analyses and terms. For example, many of the articles talk about ‘sensitivity’, ‘specificity’ and Receiver Operator Characteristic (ROC) curves. Don’t worry about these. We do not expect you to understand these concepts, nor use them in your test design (these issues will be covered toward the end of this course). The articles provided should still give you a start with the background literature, as well as ideas for methodology.
If you wish to discuss or measure performance in detecting objects in images, a good alternative to using unfamiliar terms/ methods such as sensitivity, specificity, and ROC curves is instead to talk about false alarm and correct detection rates to measure performance in this domain. This is essentially the sources from which sensitivity, specificity, and ROC curves are derived anyway. Using this alternative approach is perfectly acceptable to achieve a high Grade 7.
Background information references:
Barlow, W. E., Chi, C., Carney, P. A., Taplin, S. H., D’Orsi, C., Cutter, G., Hendrick, R. E., & Elmore, J. G. (2004). Accuracy of screening mammography interpretation by characteristics of radiologists. Journal of the National Cancer Institute, 96(24), 1840- 1850. https://doi.org/10.1093/jnci/djh333
Beam, C. A., Layde, P. M., & Sullivan, D. C. (1996). Variability in the interpretation of screening mammograms by US radiologists: Findings from a national sample. Archives of Internal Medicine, 156(2), 209-213. https://doi.org/10.1001/archinte.1996.00440020119016
Elmore, J. G., Jackson, S. L., Abraham, L., Miglioretti, D. L., Carney, P. A., Geller, B. M., Yankaskas, B. C., Kerlikowske, K., Onega, T., Rosenberg, R. D., Sickles, E. A., & Buist,
D. S. M. (2009). Variability in interpretive performance at screening mammography and radiologists’ characteristics associated with accuracy. Radiology, 253(3), 641-651. https://doi.org/10.1148/radiol.2533082308
Nodine, C. F., Kundel, H. L., Lauver, S. C., & Toto, L. C. (1996). Nature of expertise in
searching mammograms for breast masses. Academic Radiology, 3(12), 1000-1006. https://doi.org/10.1016/S1076-6332(96)80032-8
Other potentially interesting references:
Carney, P. A., Sickles, E. A., Monsees, B. S., Bassett, L. W., Brenner, R. J., Feig, S. A., Smith,
R. A., Rosenberg, R. D., Bogart, T. A., Browning, S., Barry, J. W., Kelly, M. M., Tran, K. A., & Miglioretti, D. L. (2010). Identifying minimally acceptable interpretive performance criteria for screening mammography. Radiology, 255(2), 354-361. https://doi.org/10.1148/radiol.10091636
Elmore, J. G., Wells, C. K., Lee, C. H., Howard, D. H., & Feinstein, A. R. (1994). Variability in radiologists interpretations of mammograms. New England Journal of Medicine, 331(22), 1493-1499. https://doi.org/10.1056/NEJM199412013312206
Goddard, C. C., Gilbert, R. J., Needham, G., & Deans, H. E. (1998). Routine receiver operating characteristic analysis in mammography as a measure of radiologists’ performance.
British Journal of Radiology, 71(850), 1012-1017. https://doi.org/10.1259/bjr.71.850.10211059
Miglioretti, D. L., Gard, C. C., Carney, P. A., Onega, T. L., Buist, D. S. M., Sickles, E. A., Kerlikowske, K., Rosenberg, R. D., Yankaskas, B. C., Geller, B. M., & Elmore, J. G. (2009). When radiologists perform best: The learning curve in screening mammogram interpretation. Radiology, 253(3), 632-640. https://doi.org/10.1148/radiol.2533090070
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