Procedure-specific prediction of surgical difficulty in laparoscopic and robotic right hemicolectomy: Interpretable machine learning models.

BACKGROUND: Laparoscopic (L-RHC) and robotic (R-RHC) right hemicolectomy are standard treatments for colon cancer, but procedure-specific prediction of surgical difficulty remains limited. This study developed interpretable machine learning (ML) models to predict surgical difficulty for both procedures and to support individualized surgical approach selection. METHODS: Patients with right-sided colon adenocarcinoma who underwent L-RHC or R-RHC between Jan. 2019 and Dec. 2025 were retrospectively analyzed. The two procedures were treated as independent cohorts and randomly split into development and validation sets (7:3). Preoperative clinical variables and CT-derived anatomical metrics were collected. In each development set, LASSO regression was used for feature selection. Nine ML algorithms were trained with 10-fold cross-validation, and a weighted soft-voting ensemble of the five best-performing models was built for each cohort. SHAP was used for interpretation, and web-based calculators were developed. RESULTS: In the L-RHC cohort (n = 419), selected predictors included adiposity-related metrics, Henle's trunk type, presence of the right colonic artery, and high plasma triglycerides. In the R-RHC cohort (n = 215), selected predictors included prior abdominal surgery, adiposity-related metrics, and high plasma triglycerides. In internal validation, the ensemble models achieved AUCs of 0.919 for L-RHC and 0.901 for R-RHC. SHAP showed that adiposity-related metrics were key contributors in both cohorts, while vascular anatomy was more important in L-RHC and prior abdominal surgery in R-RHC. CONCLUSIONS: Procedure-specific ensemble ML models using routine clinical and CT-derived variables predicted surgical difficulty in L-RHC and R-RHC with good discrimination. The accompanying web-based calculators may support individualized risk assessment, preoperative planning, and surgical approach selection.