{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T06:57:04Z","timestamp":1792393024949,"version":"4.4.0"},"reference-count":22,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2014,2,26]],"date-time":"2014-02-26T00:00:00Z","timestamp":1393372800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2015,9,1]],"date-time":"2015-09-01T00:00:00Z","timestamp":1441065600000},"content-version":"tdm","delay-in-days":552,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["aip.org"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2014,3]]},"abstract":"<jats:sec><jats:title><jats:bold>Purpose:<\/jats:bold><\/jats:title><jats:p>The purpose of this study is to explore Breast Imaging\u2010Reporting and Data System (BI\u2010RADS) features as predictors of individual errors made by trainees when detecting masses in mammograms.<\/jats:p><\/jats:sec><jats:sec><jats:title><jats:bold>Methods:<\/jats:bold><\/jats:title><jats:p>Ten radiology trainees and three expert breast imagers reviewed 100 mammograms comprised of bilateral medial lateral oblique and craniocaudal views on a research workstation. The cases consisted of normal and biopsy proven benign and malignant masses. For cases with actionable abnormalities, the experts recorded breast (density and axillary lymph nodes) and mass (shape, margin, and density) features according to the BI\u2010RADS lexicon, as well as the abnormality location (depth and clock face). For each trainee, a user\u2010specific multivariate model was constructed to predict the trainee\u02c8s likelihood of error based on BI\u2010RADS features. The performance of the models was assessed using area under the receive operating characteristic curves (AUC).<\/jats:p><\/jats:sec><jats:sec><jats:title><jats:bold>Results:<\/jats:bold><\/jats:title><jats:p>Despite the variability in errors between different trainees, the individual models were able to predict the likelihood of error for the trainees with a mean AUC of 0.611 (range: 0.502\u20130.739, 95% Confidence Interval: 0.543\u20130.680,<jats:italic>p<\/jats:italic> &lt; 0.002).<\/jats:p><\/jats:sec><jats:sec><jats:title><jats:bold>Conclusions:<\/jats:bold><\/jats:title><jats:p>Patterns in detection errors for mammographic masses made by radiology trainees can be modeled using BI\u2010RADS features. These findings may have potential implications for the development of future educational materials that are personalized to individual trainees.<\/jats:p><\/jats:sec>","DOI":"10.1118\/1.4866379","type":"journal-article","created":{"date-parts":[[2014,2,27]],"date-time":"2014-02-27T13:24:28Z","timestamp":1393507468000},"update-policy":"https:\/\/doi.org\/10.1063\/aip-crossmark-policy-page","source":"Crossref","is-referenced-by-count":12,"title":["Predicting error in detecting mammographic masses among radiology trainees using statistical models based on BI\u2010RADS features"],"prefix":"10.1002","volume":"41","author":[{"given":"Lars J.","family":"Grimm","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sujata V.","family":"Ghate","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sora 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