Concordant and discordant breast density patterns by different approaches for assessing breast density and breast cancer risk.

Autor: Cho Y; Center for Cohort Studies, Total Healthcare Center, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.; Department of Family Medicine, Chung-Ang University Gwangmyeong Hospital, Chung-Ang University College of Medicine, Gwangmyeong, South Korea., Park EK; Lunit, Seoul, Republic of Korea.; Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea., Chang Y; Center for Cohort Studies, Total Healthcare Center, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea. yoosoo.chang@gmail.com.; Department of Occupational and Environmental Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Samsung Main Building B2, 250, Taepyung-ro 2Ga, Jung-gu, Seoul, 04514, Republic of Korea. yoosoo.chang@gmail.com.; Department of Clinical Research Design & Evaluation, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea. yoosoo.chang@gmail.com., Kwon MR; Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea., Kim EY; Department of Surgery, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea., Kim M; Lunit, Seoul, Republic of Korea.; Department of Statistics, Ewha Womans University, Seoul, Republic of Korea., Park B; Department of Preventive Medicine, Hanyang University College of Medicine, Seoul, Republic of Korea.; Hanyang Institute of Bioscience and Biotechnology, Hanyang University, Seoul, Republic of Korea., Lee S; Lunit, Seoul, Republic of Korea., Jeong HE; Lunit, Seoul, Republic of Korea., Kim KH; Lunit, Seoul, Republic of Korea., Kim TS; Lunit, Seoul, Republic of Korea., Lee H; Lunit, Seoul, Republic of Korea., Kwon R; Center for Cohort Studies, Total Healthcare Center, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.; Institute of Medical Research, Sungkyunkwan University School of Medicine, Suwon, Republic of Korea., Lim GY; Center for Cohort Studies, Total Healthcare Center, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.; Institute of Medical Research, Sungkyunkwan University School of Medicine, Suwon, Republic of Korea., Choi J; School of Mechanical Engineering, Sungkyunkwan University, Seoul, Republic of Korea., Kook SH; Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea., Ryu S; Center for Cohort Studies, Total Healthcare Center, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea. sh703.yoo@gmail.com.; Department of Occupational and Environmental Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Samsung Main Building B2, 250, Taepyung-ro 2Ga, Jung-gu, Seoul, 04514, Republic of Korea. sh703.yoo@gmail.com.; Department of Clinical Research Design & Evaluation, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea. sh703.yoo@gmail.com.
Jazyk: angličtina
Zdroj: Breast cancer research and treatment [Breast Cancer Res Treat] 2024 Nov 01. Date of Electronic Publication: 2024 Nov 01.
DOI: 10.1007/s10549-024-07541-1
Abstrakt: 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.
(© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)
Databáze: MEDLINE
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