Work

Development and internal validation of a machine learning model for predicting acute chemotherapy-induced nausea and vomiting after intra-arterial interventional therapy in patients with liver cancer

Year: 2026

Type: article

Abstract: Objective To develop and internally validate an interpretable machine learning model for predicting acute chemotherapy-induced nausea and vomiting (ACINV) after intra-arterial interventional therapy (IAIT) in patients with primary liver cancer. Methods This retrospective cohort included 1,062 patients who underwent IAIT in China. ACINV was defined as CTCAE grade ≥2 nausea or vomiting within 24 hours. Patients were allocated to a training set ( n = 638) and a held-out internal validation set ( n = 424). Seven algorithms were compared using nested stratified five-fold cross-validation. Outer folds generated out-of-fold (OOF) predictions, whereas inner folds supported tuning and threshold selection. Imputation, encoding, standardization, and LASSO selection were fitted only to training observations in each partition. Preprocessing was re-estimated within folds used for LASSO penalty selection. SMOTE was applied only within model-fitting partitions after predictor selection. Algorithms were ranked by pooled OOF AUC, with the Brier score resolving ties. The selected pipeline was refitted on the complete training set and evaluated in internal validation. Results ACINV occurred in 380 patients (35.8%). The final refit retained 11 predictors. Random forest achieved the highest pooled OOF AUC (0.941; 95% CI, 0.923–0.958) and a Brier score of 0.092. In internal validation, its AUC was 0.962 (95% CI, 0.946–0.978), Brier score was 0.073, calibration intercept was −0.090, and calibration slope was 1.056. SHAP analysis supported model interpretation, and the locked model was incorporated into a prototype calculator. Conclusions The model showed promising internal performance. External validation and prospective impact evaluation are required before clinical implementation. Trial registration This study was registered in Chinese clinical trial registry (ChiCTR-2500110585).

Source: Asia-Pacific Journal of Oncology Nursing