aceDevelopment and validation of a machine learning clinicogenomic model to improve prognostic stratification in ER-positive/HER2-negative early breast cancer
Background Several gene expression signatures (GESs) are used for risk stratification in estrogen receptor-positive/human epidermal growth factor receptor 2-negative (ER-positive/HER2-negative) early breast cancer. Recent integrative approaches combine tumour proliferation, estrogen receptor signalling, immune activity, and clinicopathologic features, yielding gains in prognostic accuracy. We therefore developed a machine-learning-based clinicogenomic prognostic model integrating a research-grade implementation…
In three external retrospective datasets, an AI model combining gene scores and clinical features identified low-risk groups 33.8% to 111.8% larger than the standalone 21-gene score while keeping similar or higher survival estimates.
Findings are based on retrospective analysis of microarray and RNA-sequencing data using a research-grade implementation of the 21-gene recurrence score, not prospective clinical deployment
Evidence
- Peer-reviewedBreast Cancer Research2026-09-16
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Truvace Impact Record TRV-2026-1132, v2: “Development and validation of a machine learning clinicogenomic model to improve prognostic stratification in ER-positive/HER2-negative early breast cancer.” Truvace, 2026-11-04. /record/TRV-2026-1132 (accessed at citation time). sha256 0ce1c069443a8360…
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