Concordant and Discordant Breast Density Patterns by Different approaches for Assessing Breast Density and Breast Cancer Risk

Abstract

Purpose To examine the discrepancy in breast density assessments by radiologists, LIBRA software, and AI algorithm and their association with breast cancer risk. Methods Among 74,610 Korean women aged ≥ 34 years, who underwent screening mammography, density estimates obtained from both LIBRA and the AI algorithm were compared to radiologists using BI-RADS density categories (A–D, designating C and D as dense breasts). The breast cancer risks were compared according to concordant or discordant dense breasts identified by radiologists, LIBRA, and AI. Cox-proportional hazards models were used to determine adjusted hazard ratios (aHRs) [95% confidence intervals (CIs)]. Results During a median follow-up of 9.9 years, 479 breast cancer cases developed. Compared to the reference non-dense breast group, the aHRs (95% CIs) for breast cancer were 2.37 (1.68–3.36) for radiologist-classified dense breasts, 1.30 (1.05–1.62) for LIBRA, and 2.55 (1.84–3.56) for AI. For different combinations of breast density assessment, aHRs (95% CI) for breast cancer were 2.40 (1.69–3.41) for radiologist-dense/LIBRA-non-dense, 11.99 (1.64–87.62) for radiologist-non-dense/LIBRA-dense, and 2.99 (1.99–4.50) for both dense breasts, compared to concordant non-dense breasts. Similar trends were observed with radiologists/AI classification: the aHRs (95% CI) were 1.79 (1.02–3.12) for radiologist-dense/AI-non-dense, 2.43 (1.24–4.78) for radiologist-non-dense/AI-dense, and 3.23 (2.15–4.86) for both dense breasts. Conclusion The risk of breast cancer was highest in concordant dense breasts. Discordant dense breast cases also had a significantly higher risk of breast cancer, especially when identified as dense by either AI or LIBRA, but not radiologists, compared to concordant non-dense breast cases.