aceBuilding Machine Learning Models Based on Oxidative Stress Index Score to Predict Survival of Locally Advanced Rectal Cancer Receiving Different Neoadjuvant Therapy Patterns
Background Liver enzyme biomarkers are known to contribute to the onset and progression of colorectal cancer. Objective To develop a novel oxidative stress index score integrating γ-glutamyl transferase and total bilirubin, evaluate its prognostic value in locally advanced rectal cancer patients receiving neoadjuvant therapy, and construct and validate machine learning-based survival prediction models incorporating oxidative stress index score. Design A novel liver enzyme indicator - oxidative stress index score…
Random survival forests models incorporating oxidative stress index score improved prediction of disease-free and overall survival for locally advanced rectal cancer patients receiving neoadjuvant therapy, outperforming conventional ypStage and enabling risk stratification.
Findings are constrained by retrospective single-center design and lack of external multi-center validation, limiting generalizability of the predictive models.
Evidence
- Peer-reviewedDiseases of the Colon & Rectum2026-08-12
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Truvace Impact Record TRV-2026-0754, v1: “Building Machine Learning Models Based on Oxidative Stress Index Score to Predict Survival of Locally Advanced Rectal Cancer Receiving Different Neoadjuvant Therapy Patterns.” Truvace, 2026-08-14. /record/TRV-2026-0754 (accessed at citation time). sha256 d3b083f2c7ddbcd7…
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