AI and Cancer Research ── Breast ── 2026-11-05
Breast cancer: treatment, trials and AI
- Abstract
PURPOSE: Accurate assessment of low levels of human epidermal growth factor receptor 2 (HER2) expression has become increasingly important with the expansion of HER2-targeted therapies to patients with HER2-low and HER2-ultralow breast cancer (BC). Following the introduction of revised College of American Pathologists (CAP) guidelines incorporating the HER2-ultralow (0+) category, we conducted a nationwide multireader study to evaluate interobserver variation in HER2 interpretation and to investigate the impact of artificial intelligence (AI)-assisted image analysis. MATERIALS AND METHODS: A total of 168 BC cases stained using the VENTANA HER2 (4B5) assay were digitized as whole-slide images (WSIs). Thirty-four board-certified breast pathologists independently reviewed all cases without AI assistance and repeated the assessment after a four-week washout period using AI support. Interobserver agreement, classification performance, and reading time were compared between the two phases. RESULTS: AI assistance significantly improved interobserver agreement (Krippendorff's alpha, 0.7415 to 0.8849; Fleiss' kappa, 0.4492 to 0.7524; p<0.001). The largest gains in diagnostic performance were observed in HER2-ultralow and HER2 IHC 1+ BCs, with sensitivities improving from 48.5% to 73.9% and from 64.9% to 87.7%, respectively (p<0.001). Although overall reading time was not reduced in the unadjusted analysis, AI assistance shortened interpretation time in HER2-ultralow BCs by 3.12 seconds (5.1% reduction). CONCLUSION: AI-assisted assessment improved the reproducibility and diagnostic performance of HER2 interpretation, with the greatest benefit observed in clinically relevant HER2 low-expression categories. The reduced interpretation time in HER2-ultralow BCs further suggests that AI can improve efficiency in diagnostically challenging cases encountered in routine practice.
Journal IF-equivalent: 3.6 (OpenAlex 2-year mean citedness, value as of 2026-11-03, retrieved 2026-11-04; not the official Clarivate IF)Reference: Lee H, Kim GY, Cho YA, Gong G, Lee HJ, Jeong BK, et al. A Nationwide Study of Interobserver Agreement and Accuracy in HER2 Immunohistochemistry and the Impact of Artificial Intelligence on HER2 Interpretation. Cancer Res Treat. 2026 Nov 2 [Epub ahead of print]. doi:10.4143/crt.2026.0805. PMID: 42971167.Checked: Abstract only - Abstract
BACKGROUND: The inherent biological heterogeneity of tumors continues to pose a challenge for achieving accurate survival status prediction in breast cancer. To address this, the current study seeks to improve the prognostic accuracy by developing an integrated framework that synthesizes clinical parameters with specific genetic variables derived from the comprehensive METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) dataset. METHODS: Several machine learning (ML) models were applied under overall survival month (OSM)-dropped scenarios to prevent information leakage and develop clinically applicable predictive models. A diverse array of feature selection strategies, including SelectKBest and an integrated SHapley Additive exPlanations-XGBoost (SHAP-XGB) framework, were implemented. The limma package was used to identify differentially expressed genes (DEGs) with thresholds of |logFC|≥ 0.25 and adjusted p-value < 0.05. High-contribution genes associated with survival were further determined using a Leave-One-Out AUC (LOO-AUC) reduction approach. Gene Ontology (GO) enrichment analysis was performed to investigate biological pathways associated with the identified genes. Monte Carlo permutation testing, Kaplan-Meier survival analysis, multivariate Cox proportional hazards regression, and external validation using the TCGA-BRCA cohort were implemented to validate consensus biomarkers. RESULTS: Integration of clinical data with a 10-gene subset selected by the SHAP-XGB model achieved the highest predictive accuracy of 0.7133 and a balanced accuracy of 0.7217 by the Random Forest model. Integrated analyses of SHAP-XGB/RF, SelectKBest, DEG, and high-contribution gene results consistently identified six prognostic genes: JAK1, JAK2, CASP8, KIT, STAT5A, and GSK3B. GO enrichment analysis revealed significant immune-inflammatory pathways, particularly those related to cytokine production and regulation. CONCLUSION: This study presents a biologically interpretable framework that integrates explainable machine learning, transcriptomic analyses, and survival modeling for breast cancer prognosis. The identified six-gene consensus signature was consistently validated through multiple independent analytical approaches, external cohort validation, and survival analyses, highlighting its potential as a promising prognostic biomarker panel for breast cancer risk stratification.
Journal IF-equivalent: 3.3 (OpenAlex 2-year mean citedness, value as of 2026-11-03, retrieved 2026-11-04; not the official Clarivate IF)Reference: Oguz O, Aydın Z, Gungor BB, Goksuluk D. Explainable machine learning and multi-layer transcriptomic analysis identify and validate prognostic biomarkers for breast cancer survival. BMC Med Genomics. 2026 Aug 1;19(1):166. doi:10.1186/s12920-026-02430-6; PMCID: PMC13726301.Checked: Full text checked