ArticleAnnals of surgical oncology2026
Development and Validation of Time-to-Event Machine Learning Models for Predicting Disease-Free Survival in Patients with Locally Advanced Colorectal Cancer: A Multicenter Cohort Study.
Article in Annals of surgical oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- CT-Based Nested Habitats Analysis for Early Recurrence Prediction and Risk Stratification in Hepatocellular Carcinoma: Development and Multicenter Validation Across Four Cohorts.Annals of surgical oncology · 2026Article
- Topologically distinct 2D and 3D intratumoral heterogeneity scores for preoperatively predicting invasiveness in stage I lung adenocarcinoma: A multicenter study.PLOS digital health · 2026Article
- ASO Author Reflections: Bridging Oxidative Stress and Inflammation-A Novel Prognostic Tool for Locally Advanced Gastric Cancer Patients Undergoing Neoadjuvant Immunochemotherapy.Annals of surgical oncology · 2026Article
- 3D intratumoral heterogeneity-based quantitative score from chest CT for preoperative prediction of visceral pleural invasion in lung adenocarcinoma: a multicenter study.Frontiers in oncology · 2026Article
- CT-derived topological intratumoral heterogeneity predicts major pathological response to neoadjuvant immunochemotherapy in resectable non-small-cell lung cancer: a two-center study.Frontiers in immunology · 2026Article
- Article
- Machine learning based on body composition radiomics for predicting early recurrence in colorectal cancer: a multicenter study.Frontiers in nutrition · 2026Article
- Decoding Intratumoral Heterogeneity in Breast Cancer: The Evolution From Radiomics to Topological Quantification for Predicting Lymphovascular Invasion.Cancer control : journal of the Moffitt Cancer CenterReview
- Time-to-Event Machine Learning Model Incorporating MRI-Derived Intratumoral Heterogeneity Score for Predicting Invasive Breast Cancer Recurrence: A Dual-Center Study.Technology in cancer research & treatmentArticle
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Authors and funding
10 authors.
Funding
Abstract
backgroundThe postoperative prognosis of locally advanced colorectal cancer (LACRC) exhibits significant heterogeneity. However, conventional models for predicting disease-free survival (DFS) often lack the necessary precision. Therefore, we aim to develop and validate time-to-event machine learning (ML) models for predicting DFS in patients with LACRC, ultimately improving prognostic accuracy. PATIENTS AND
methodsThis multicenter cohort study enrolled 456 patients with LACRC from three medical centers. A training cohort consisting of 350 patients was formed from centers 1 and 2, while an external validation cohort comprising 106 patients was sourced from center 3. Preoperative computed tomography (CT) images were segmented to extract radiomics features, and a radiomics score (radscore) was calculated through feature engineering. In addition, intratumor heterogeneity (ITH) scores were derived by integrating clustered mask regions with global pixel distribution patterns. To predict DFS, five time-to-event ML models were trained: Cox proportional hazards, FastKernelSurvivalSVM, GradientBoostingSurvival (GB-Survival), RandomSurvivalForest, and ExtraSurvivalTrees. Model performance was assessed using the concordance index (C-index), and Survival SHapley Additive exPlanations over time (SurvSHAP (t)) analysis was conducted for model interpretation.
resultsAmong the models tested, GB-Survival demonstrated the highest predictive performance for DFS, achieving a C-index of 0.7823. SurvSHAP (t) analysis revealed that the key prognostic factors included the ITH score, pathological TNM stage, lymphovascular invasion, radscore, and the prognostic nutritional index.
conclusionsThe GB-Survival model that integrates multimodal data outperforms other time-to-event ML models in predicting DFS for LACRC. This approach may facilitate the development of data-driven treatment strategies and personalized risk stratification for patients with LACRC.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.