AI and Cancer Research ── Breast ── 2026-10-19
Breast cancer: treatment, trials and AI
- Abstract
Radiogenomics aims to use non-invasive medical images to provide information about the molecular characteristics of tumors. However, many radiogenomic studies focus mainly on prediction accuracy and provide limited explanation of which imaging features drive the predictions or how those features relate to tumor biology. We developed an explainable radiogenomic framework for breast cancer that (1) compares machine-learning models for predicting molecular subtype from MRI radiomic features, (2) uses bootstrap permutation importance to identify a compact and stable radiomic signature, and (3) examines correlations between the selected features and gene expression. We first evaluated the workflow in a synthetic paired radiomics-transcriptomics cohort of 300 samples. Twelve MRI radiomic features and ten gene-expression values were generated from shared estrogen-receptor, HER2, and proliferation activity scores, providing a known ground truth for evaluation. Five-fold cross-validation showed strong four-class discrimination, with logistic regression achieving a macro-average AUC of 0.963. Bootstrap analysis using 100 resamples and a 70% stability threshold identified six robust features: margin spiculation, enhancement heterogeneity, wash-out ratio, shape irregularity, wash-in slope, and peak enhancement. The six-feature signature preserved classification performance (AUC = 0.966). Enhancement heterogeneity and margin spiculation were positively correlated with proliferation and basal markers and negatively correlated with estrogen-pathway genes, whereas wash-out ratio showed its strongest correlations with ERBB2 and GRB7. The synthetic cohort provides a controlled proof of concept showing that the workflow can recover predefined imaging-genomic relationships when a shared biological signal is present. These associations should not be interpreted as newly discovered patient-level biological findings. Independent validation using real paired breast MRI and transcriptomic cohorts is required to establish generalizability and clinical utility.
Journal IF-equivalent: 0.1 (OpenAlex 2-year mean citedness, value as of 2026-10-18, retrieved 2026-10-19; not the official Clarivate IF)Reference: Thakar SB, Thakar PS, Sangle A. Explainable Radiogenomic Fusion Links Mri-derived Radiomic Phenotypes to Transcriptomic Molecular Subtypes in Breast Cancer. Res J Life Sci Bioinform Pharm Chem Sci. 2026 Oct 17;12(5). doi:10.26479/2026.1205.02.Checked: Abstract only