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TRV-2026-1132Certified recordPeer-reviewed

Development 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…

Health · G Space — documented gain · certified 2026-09-18 · v1 · article view · machine-readable

Current reading — gain

A random survival forest clinicogenomic model integrating a 21-gene recurrence score, 14-gene immunoglobulin signature, and clinicopathologic features improved prognostic stratification in ER-positive/HER2-negative early breast cancer, identifying larger low-risk groups and improving discrimination in external datasets

What this doesn’t fix

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

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Truvace Impact Record TRV-2026-1132, v1: “Development and validation of a machine learning clinicogenomic model to improve prognostic stratification in ER-positive/HER2-negative early breast cancer.” Truvace, 2026-09-18. /record/TRV-2026-1132 (accessed at citation time). sha256 f6733a1526cc7415…

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