Mammo-AGE: deep learning estimation of breast age from mammograms.
Authors
Affiliations (11)
Affiliations (11)
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
- GROW School, Maastricht University, Maastricht, The Netherlands.
- Faculty of Applied Sciences, Macao Polytechnic University, Macao, SAR, China. [email protected].
- Department of Medical Imaging, Radboud University Medical Centre, Nijmegen, The Netherlands. [email protected].
- Department of Medical Imaging, Radboud University Medical Centre, Nijmegen, The Netherlands.
- School of Biomedical Engineering, Tsinghua University, Beijing, China.
- Department of Biomedical Sciences and Morphologic and Functional Imaging, Policlinico Universitario "G. Martino", University of Messina, Policlinico "G. Martino", Messina, Italy.
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
- Department of Surgical Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.
Abstract
Biological age is an important indicator of organ functions and health. Although mammograms are widely used in breast cancer screening, the potential of mammogram-based biological age predictors remains underexplored. Here, we propose a deep learning model to estimate the biological age of the breast using healthy mammograms. The model is developed on three large datasets and externally validated on two additional datasets, encompassing 95,826 mammograms from 44,497 women aged 18 to 98 years. It demonstrates accurate age estimation (mean absolute error: 4.2 - 6.1 years) with strong correlation to chronological age. Predicted breast age stratifies breast cancer risk similarly to chronological age. Occlusion analysis, employed for model interpretation, reveals the aging-related pattern of the breast. The breast age gap (the difference between system-bias-corrected breast age and chronological age) may reflect breast health status. Breast cancer patients show higher breast age gaps than the healthy population. In two longitudinal datasets, larger breast age gaps are associated with increased future breast cancer risk, with hazard ratios of 1.013 - 1.022. Furthermore, we finetune the model specifically for downstream breast cancer diagnosis and risk prediction. Our approach outperforms other comparative methods, showing its potential for supporting both early detection and personalized screening strategies.