Comparison of breast density assessment between human eye and automated software on digital and synthetic mammography: Impact on breast cancer risk

Purpose: To evaluate the agreement between automatic assessment software of breast density based on artificial intelligence (AI) and visual assessment by a senior and a junior radiologist, as well as the impact on the assessment of breast cancer risk (BCR) at 5 years. Materials and methods: We retrospectively included 311 consecutive women (mean age, 55.6 +/- 8.5 [SD]; range: 40-74 years) without a personal history of breast cancer who underwent routine mammography between January 1, 2019 and February 28, 2019. Mammographic breast density (MBD) was independently evaluated by a junior and a senior reader on digital mammography (DM) and synthetic mammography (SM) using BI-RADS (5th edition) and by an AI software. For each MBD, BCR at 5 years was estimated per woman by the AI software. Interobserver agreement for MBD between the two readers and the AI software were evaluated by quadratic kappa coefficients. Reproducibility of BCR was assessed by intraclass correlation coefficient (ICC). Results: Agreement for MBD assessment on DM and SM was almost perfect between senior and junior radiologists (kappa = 0.88 [95% CI: 0.84-0.92] and kappa = 0.86 [95% CI: 0.82-0.90], respectively) and substantial between the senior radiologist and AI (kappa = 0.79; 95% CI: 0.73-0.84). There was substantial agreement between DM and SM for the senior radiologist (kappa = 0.79; 95% CI: 0.74-0.84). BCR evaluation at 5 years was highly reproducible between the two radiologists on DM and SM (ICC = 0.98 [95% CI: 0.97-0.98] for both), between BCR evaluation based on DM and SM evaluated by the senior (ICC = 0.96; 95% CI: 0.95-0.97) or junior radiologist (ICC = 0.97; 95% CI: 0.96-0.98) and between the senior radiologist and AI (ICC = 0.96; 95% CI: 0.95-0.97). Conclusion: This preliminary study demonstrates a very good agreement for BCR evaluation based on the evaluation of MBD by a senior radiologist, junior radiologist and AI software. (C) 2020 Societe francaise de radiologie. Published by Elsevier Masson SAS. All rights reserved.