A Deep Learning Framework for Early Breast Cancer Detection Among U.S. Women: Integrating Mammography and Clinical EHR Data

Abstract

Breast cancer is the most commonly diagnosed malignancy and a leading cause of cancer-related mortality among women in the United States (American Cancer Society, 2024). Although screening mammography has reduced mortality, its performance is limited by lower sensitivity in women with dense breasts, inter-reader variability, and frequent false-positive recalls (Skaane, 2019). At the same time, widespread adoption of electronic health records (EHRs) has created an opportunity to combine imaging with rich clinical information for more accurate, individualized risk assessment. This study proposes a deep learning framework for early breast cancer detection among U.S. women that integrates digital mammography with structured EHR variables. Mammography images are obtained from a curated public dataset such as the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM), which provides biopsy-verified benign and malignant cases (Lee et al., 2017). EHR-style data are organized following the structure of large U.S. clinical datasets, for example MIMIC-IV, and include demographics, breast density, reproductive and hormonal factors, comorbidities, family history, and prior screening history (Johnson et al., 2023). A convolutional neural network extracts high-level image features, which are fused with gradient-boosted clinical embeddings for malignancy prediction and compared with image-only and EHR-only baselines. In this practice framework, the multimodal model demonstrates superior discrimination and a better balance between missed cancers and false positives, particularly in women with dense breasts, highlighting the potential of integrating mammography and EHR data to support earlier detection and more informed, risk-adapted clinical decision-making.