Dual-modality CAD for breast cancer screening: dealing with discordant diagnosis between mammography and tomography

Full-field digital mammography (FFDM) is the standard for breast cancer screening. Digital breast tomosynthesis (DBT), compared to FFDM, enhances cancer detection and reduces unnecessary biopsies. Despite DBT's adoption, critical questions remain-higher radiation, time, cost, and clinical benefits, particularly for systematic breast screening. In the era of AI computer-aided detection/diagnosis (CAD) for breast screening, one unresolved question is the role of bimodal algorithms in predicting cancer and offering guidance when opinions differ, and we aim to understand this.

We retrospectively assembled an enriched screening cohort of 1,816 women who underwent both FFDM and DBT at two Hologic sites. Analyses requiring paired CAD scores were performed on a lesion-level subset for which both FFDM and DBT CAD scores were available (low suspicion = 1; high suspicion = 10) and reference standard outcomes were known, comprising 1,071 lesions from 657 examinations. From the joint distribution, we defined areas of "perpendicular scoring" (PS) as the areas of highly discordant scoring. We estimated the inter-modality agreement using the three classes (low, indeterminate, and high suspicious) with Cohen's kappa index. We evaluated the potential of systematic, lossless, and AI-powered reclassifications of PS both for tumoral masses and calcifications and in considering breast density as a risk factor for PS.

We observed a moderate inter-modality agreement, indicated by a kappa of 0.49 (95% CI: 0.46-0.52). PS scoring was present in 32.7% (95% CI: 29.7-35.8) of tumoral masses (soft tissue lesion) cases and 38.6% (95% CI: 30.1-47.6) of calcification cases. Breast density was a risk factor of PS for masses (odd, 0.66 [95% CI: 0.48-0.91]). AI-powered and lossless models were found effective for reclassifying 82.2% and 67.3% of PS of masses and calcification, respectively.

When processed by CAD, FFDM and DBT provided complementary information at the expense of unavoidable discordant diagnosis. Post-processing has the potential of reclassifying part of the discordant diagnosis in improving the overall performance of the CAD. Therefore, exploring alternative reclassification methods is essential.