AI and Cancer Research (Top Journals) ── Breast ── 2026-09-30
New papers on cancer and AI from 26 medical, oncology and general journals with an impact factor of 10 or more, collected from the journals' own websites and selected by Jev (an AI that makes language judgements): is it original research, is AI central, which of six themes, and does it concern treatment or trials. Summaries are written from each abstract only and make no claim about efficacy or safety.
Breast cancer: treatment, trials and AI
CATALINA independently validated two locked AI pipelines producing computational TIL scores against pathologist-scored stromal TILs, using long-term outcome data pooled from seven randomised trials. Among 1,356 evaluable patients with early triple-negative breast cancer, correlation with pathologist scores was modest (r 0.375-0.473), and both score types were independently associated with disease-free and overall survival after adjustment.
This secondary analysis of the phase 3 APHINITY trial compared manual, automated digital and AI-based stromal TIL quantification, plus two AI spatial features, in 4,262 H&E images. Manual scoring showed high interobserver reproducibility (ICC 0.84) with modest concordance to automated methods, and higher TIL levels were associated with better invasive disease-free survival across methods (HRs 0.41-0.93).
The authors extend pre-training of histopathology foundation models on invasive tumour tissue, evaluate the biological concepts encoded in their representations, and identify recurrent tumour archetypes with consistent morphological and molecular identities across patients. RNA splicing-associated archetypes were consistently associated with poorer outcomes in the cohorts analysed, including HER2-positive and triple-negative breast cancer.