Radiomics and Deep Learning Merge Breast Cancer Imaging with Tumor Genomics

A review outlines how radiomics and deep learning extract quantitative imaging features to predict breast cancer's molecular subtypes and genomic heterogeneity, enabling noninvasive radiogenomic profiling for personalized diagnosis and treatment planning.

• This is a narrative review, not a primary research study; it reports no new patient-level validation metrics.
• The synthesis covers the entire radiogenomic workflow: image acquisition, tumor segmentation, automated deep-learning feature extraction, feature selection, and multimodal data-fusion modeling.
• A key limitation is the lack of external validation or reporting of pooled effect sizes; the paper highlights methodology and existing gaps in clinical translation.

RadPigeon summaries are original and for information only. They are not clinical advice.