Artificial intelligence-based integration of imaging, exposome, and multi-omics data for immune-related biomarker discovery and precision prevention in breast cancer

Abstract

Breast cancer management still depends on early detection, risk assessment, and individualized treatment. Imaging is used throughout screening, diagnosis, treatment evaluation, and follow-up. For this reason, artificial intelligence (AI) has become an active area in breast cancer imaging, especially in mammography, ultrasound, MRI, and radiomics. These methods may help describe tumor phenotype, estimate clinical risk, and predict treatment response. In daily practice, however, similar imaging findings do not always mean the same biology. Patients with similar images may have different molecular features, immune status, exposure histories, and treatment outcomes. Imaging models alone are therefore not enough to explain the heterogeneity of breast cancer. Environmental and lifestyle exposures, metabolic status, and immune regulation may also influence tumor development and prognosis. This review discusses imaging AI as a practical starting point for biomarker discovery in breast cancer. It also considers how exposome and multi-omics data may improve the biological interpretation of imaging features, and how immune dysregulation may connect external exposure, molecular change, and imaging phenotype. Current barriers include fragmented data, limited external validation, weak interpretability, fairness concerns, and difficulty in clinical implementation. Future work should focus less on building larger models alone, and more on developing transparent and validated tools that clinicians can use for risk stratification, biopsy planning, treatment-response prediction, and precision prevention. This highlights the need to integrate imaging features with multi-omics data to better interpret tumor biology and immune status.