Predicting 5-Year Breast Cancer Risk From Longitudinal Digital Breast Tomosynthesis: A Single-Center Retrospective Study.

Authors

Yanqi Xu, Laura Heacock, Jungkyu Park, Felicia L Pasadyn, Qi Lei, Alana Lewin, Krzysztof Geras, Linda Moy, Freya Schnabel, Yiqiu Shen

Published in

AJR. American journal of roentgenology. Aug 12, 2026. Epub Aug 12, 2026.

Abstract

Background: Imaging-based breast cancer risk prediction models primarily use full-field digital mammography (FFDM). Although digital breast tomosynthesis (DBT) has become a predominant screening modality in the United States, its potential for long-term breast cancer risk prediction remains underexplored. Objective: The aim of this study was to develop and evaluate a deep learning model that uses longitudinal DBT examinations to predict long-term breast cancer risk. Methods: This retrospective study included 313,335 DBT examinations from 161,077 women (mean age, 58.5 ± 11.7 years) between January 2016 and August 2020 at a single health institution. A DBT-based risk prediction (DRP) model was developed to estimate 2- to 5-year breast cancer risk using longitudinal DBT examinations, patient age, and breast density. Model performance was compared with a single-timepoint DBT model, the Mirai model using same-day FFDM, and the Tyrer-Cuzick model using the AUC, time-dependent concordance index, and integrated Brier score. Results: In an independent test set (n = 34,570), the longitudinal DRP model achieved a 5-year AUC of 0.721 (95% CI, 0.698-0.744), improving on the single-timepoint DRP model (AUC, 0.707; 95% CI, 0.683-0.730; p < .001) and the Mirai model (AUC, 0.687; 95% CI, 0.663-0.710; p < .001). In a matched case-control cohort (n = 432), the DRP model achieved a 5-year AUC of 0.676 (95% CI, 0.626-0.726), compared with 0.563 (95% CI, 0.509-0.619; p < .001) for the Tyrer-Cuzick model. Among examinations of women with extremely dense breasts, the model classified 39.7% (746/1877) as average risk, with an observed 5-year cancer incidence of 0.8% (6/746). Among examinations of women with fatty breasts, the model classified 14.8% (386/2605) as high risk, with an observed 5-year cancer incidence of 2.6% (10/386). Conclusion: A deep learning model using longitudinal DBT examinations improved long-term breast cancer risk prediction compared with FFDM-based and clinical risk models. Clinical Impact: Longitudinal DBT-based risk prediction has the potential to inform dynamic risk assessment using screening images and to support future personalized screening strategies.

PMID:
42584410
Bibliographic data and abstract were imported from PubMed on 12 Aug 2026.

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