The learning curve in prostate MRI interpretation: Self-directed learning versus continual reader feedback

OBJECTIVE. The purpose of this study is to evaluate the roles of self-directed learning and continual feedback in the learning curve for tumor detection by novice readers of prostate MRI. MATERIALS AND METHODS. A total of 124 prostate MRI examinations classified as positive (n = 52; single Prostate Imaging Reporting and Data System [PI-RADS] category 3 or higher lesion showing Gleason score ≥ 7 tumor at MRI-targeted biopsy) or negative (n = 72; PI-RADS category 2 or lower and negative biopsy) for detectable tumor were included. These were divided into four equal-sized batches, each with matching numbers of positive and negative examinations. Six second-year radiology residents reviewed examinations to localize tumors. Three of the six readers received feedback after each examination showing the preceding case's solution. The learning curve, plotting accuracy over time, was assessed by the Akaike information criterion (AIC). Logistic regression and mixed-model ANOVA were performed. RESULTS. For readers with and without feedback, the learning curve exhibited an initial rapid improvement that slowed after 40 examinations (change in AIC > 0.2%). Accuracy improved from 58.1% (batch 1) to 71.0-75.3% (batches 2-4) without feedback and from 58.1% to 72.0-77.4% with feedback (p = 0.027-0.046), without a difference in the extent of improvement (p = 0.800). Specificity improved from 53.7% to 68.5-81.5% without feedback and from 55.6% to 74.1-81.5% with feedback (p = 0.006-0.010), without a difference in the extent of improvement (p = 0.891). Sensitivity improved from 59.0-61.5% (batches 1-2) to 71.8-76.9% (batches 3-4) with feedback (p = 0.052), though did not improve without feedback (p = 0.602). Sensitivity for transition zone tumors exhibited larger changes (p = 0.024) with feedback than without feedback. Sensitivity for peripheral zone tumors did not improve in either group (p > 0.3). Reader confidence increased only with feedback (p > 0.001). CONCLUSION. The learning curve in prostate tumor detection largely reflected selfdirected learning. Continual feedback had a lesser effect. Clinical prostate MRI interpretation by novice radiologists warrants caution.