Ordering mammograms for improved mammography screening performance

We aim to investigate if ordering mammograms based on texture features promotes visual adaptation, allowing observers to more correctly and/or rapidly detect abnormalities in screening mammograms, thereby improving performance. A fully-crossed, multi-reader multi-case evaluation with 150 screening mammograms (1:1, positive:negative) and 10 screening radiologists was performed to test three different orders of mammograms. The mammograms were either randomly ordered, ordered by Volpara density (low to high), or ordered by a self-supervised learning (SSL) encoding. Level of suspicion (0–100) scores and recall decisions were given per examination by each radiologist. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were compared between ordering conditions using the open-access iMRMC software. Median reading times were compared with the Wilcoxon signed rank test. The radiologist-averaged AUC was higher when interpreting screening mammograms from low to high density than when interpreting mammograms in a random order (0.924 vs 0.936, P=0.013). The radiologist-averaged specificity for the mammograms ordered by density tended to increase (87.3% vs 91.2%, P=0.047) at similar sensitivities (79.9% vs 80.4%, P=0.846) with reduced reading time (29.3 seconds vs 25.1 seconds, P<0.001). For the SSL order no significant difference in screening performance (AUC: 0.924 vs 0.914, P=0.381) and reading time (both 29.3 seconds, P=0.221) with the random order was found. In conclusion, this study suggests that ordering screening mammograms from low to high density enables radiologists to improve their screening performance. Studies within a screening setting are needed to confirm these findings.