Key result
Ensemble machine learning model predicts 1-year adverse outcomes in early-onset rectal cancer with 0.83 AUC.
Why the study?
There is currently no validated postoperative risk-stratification tool for early adverse outcomes within a year after rectal resection in early-onset rectal cancer patients.
Does a stacking ensemble machine learning model accurately predict early adverse outcomes in patients with early-onset rectal cancer?
Cohort (n=750)
Yes
Does a stacking ensemble machine learning model accurately predict early adverse outcomes in patients with early-onset rectal cancer?
An interpretable stacking ensemble machine learning model accurately predicts 1-year early adverse outcomes after surgery for early-onset rectal cancer, providing a tool to guide postoperative surveillance and adjuvant therapy.
May support postoperative risk stratification in early-onset rectal cancer; leaves open need for prospective trials before clinical adoption.
Patients often present at advanced stages of early-onset rectal cancer (EORC), which is becoming more common. Within a year following rectal resection, some patients experience early adverse outcomes (EAO), such as severe complications, recurrence, or death; however, there is currently no validated postoperative risk-stratification tool for this population. Current prognostic models concentrate on individual endpoints rather than the composite adversity spectrum that is pertinent to younger patients because they were mostly established in older cohorts. This work sought to create and externally validate a model for detecting EORC patients at greater risk of EAO using a stacking ensemble machine learning approach with SHAP interpretability in order to guide individualized postoperative surveillance and adjuvant therapy decisions. In this two-center retrospective cohort study, 648 EORC patients from Center A (2020–2025) constituted the development cohort and 102 patients from Center B (2024–2025) the external validation cohort. Eleven individual machine learning models and a two-layer stacking ensemble (combining three base learners with logistic regression meta-learner) were trained. Model performance was assessed by area under the receiver operating characteristic curve (AUC), calibration, and decision curve analysis. Boruta feature selection identified eleven important predictors, with pathological T stage (pT), lymphovascular invasion (LVI), and pathological TNM stage (pTNM) contributing most to model predictions. The stacking ensemble outperformed all 11 individual models, achieving AUCs of 0.91 (95% CI 0.88–0.93) in the training set, 0.89 (95% CI 0.85–0.94) in the internal validation set, and 0.83 (95% CI 0.76–0.91) in the external validation cohort, with a Brier score of 0.1104 and sustained net benefit on decision curve analysis. An interactive web calculator was deployed for individualized risk assessment. An interpretable stacking ensemble model integrating SHAP analysis and a web-based calculator showed promising predictive performance for EAO following EORC surgery. Designed for postoperative use once pathology data are available, the model provides a comprehensive risk estimate to guide surveillance intensity and interdisciplinary adjuvant therapy decision-making.
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Yue et al. (2026) conducted a cohort in Early-onset rectal cancer (EORC) (n=750). Stacking ensemble machine learning model vs. Individual machine learning models was evaluated on Early adverse outcomes (EAO) within 1 year (Clavien-Dindo grade ≥III complications, locoregional recurrence/distant metastasis, or all-cause death) (AUC 0.83, 95% CI 0.76-0.91). A stacking ensemble machine learning model incorporating pathological, clinical, and laboratory data predicted early adverse outcomes within one year after surgery for early-onset rectal cancer with an AUC of 0.83 in an external validation cohort.
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