Application of a Commercial Artificial Intelligence Software in Unilateral Mammography: Simulating Total Mastectomy Scenarios.

Abstract

This study was to evaluate the performance of commercially available artificial intelligence (AI) software in unilateral mammograms simulating postmastectomy surveillance compared with AI software used in bilateral mammograms from the same women serving as controls. A retrospective database search identified consecutive women who underwent breast cancer surgery between January 2021 and December 2021. AI software was applied to the mammogram immediately preceding breast cancer diagnosis in two modes: bilateral (the standard bilateral mammography dataset) and unilateral analyses (each breast's craniocaudal and mediolateral oblique views), and their outputs were reviewed. The sensitivity, specificity, and number of marks per breast were compared between the bilateral and unilateral analyses with -5% non-inferiority margin for the difference in sensitivity and specificity between the two modes. A total of 694 women (mean age, 55.2 ± 10.8 years) with unilateral or bilateral breast cancer contributed mammograms for analysis; each breast was then separately evaluated in the unilateral postmastectomy simulation (n = 1388), of which 730 had breast cancer (52.6%) (mean invasive size = 1.5 cm) and compared with bilateral mammography analysis. The sensitivity of unilateral analysis was not inferior to that of bilateral analysis (78.6% vs. 76.7%), with a difference of 1.9%. The specificity of unilateral analysis was inferior to that in the bilateral analysis (81.5% vs. 91.9%), with a difference of -10.5% being lower than the non-inferiority margin. The average number of AI marks per breast was 0.94 (unilateral [1298/1388] and bilateral analyses [1306/1388], respectively). AI software performance in simulated unilateral mammography analysis demonstrated non-inferior sensitivity and inferior specificity compared to bilateral mammography.