Subtraction of Temporally Sequential Digital Mammograms: Enhancing the Detection and Classification of Malignant...

Background: This study evaluates the performance of an automated method for detecting and classifying breast masses as Breast Imaging Reporting and Data System (BI-RADS) benign or biopsy-confirmed malignant using subtraction of temporally sequential mammograms. Mammograms from 100 women across two screening rounds (400 images: 2 views × 2 rounds × 100 cases) were retrospectively collected. The prior mammographic views were subtracted from the most recent ones, 98 image features were extracted from regions of interest, and were ranked using 8 feature selection methods. Results: Machine learning reduced false positives and detected masses with 97.06% accuracy and 0.92 AUC. True masses were classified as benign or malignant with 94.82% accuracy and 0.95 AUC, a significant improvement compared with state-of-the-art methods reported in the literature (0.95 vs. 0.90 AUC). Conclusions: The proposed approach demonstrates that temporal subtraction can improve diagnostic accuracy by up to 5%, supporting earlier detection of malignancies and enabling more personalized treatment strategies.