Published online Nov 27, 2026. doi: 10.4240/wjgs.123786
Revised: July 22, 2026
Accepted: September 8, 2026
Published online: November 27, 2026
Processing time: 162 Days and 13.6 Hours
Residual stones after hepatectomy for hepatolithiasis are associated with recurrent cholangitis, repeat interventions, and long-term biliary morbidity. Current classification systems assist in disease characterization and operative planning but fail to provide individualized risk estimates in the early postoperative period.
To develop and internally validate an early postoperative model for predicting residual stones in patients with hepatolithiasis after hepatectomy.
This study included 300 consecutive patients who underwent hepatectomy between January 2019 and December 2022. Residual stones were identified by postoperative imaging within 1 year and classified as incomplete clearance rather than recurrence. The cohort was randomly split into a development dataset (70%) and a held-out internal validation dataset (30%), and 12 candidate prediction models were developed and compared. Within the development dataset, preprocessing, feature shrinkage, hyperparameter tuning, and algorithm selection were performed using repeated nested cross-validation (outer 5-fold cross-validation repeated three times; inner 5-fold cross-validation). The final model was then locked and evaluated in the held-out validation cohort. Model performance was evaluated based on discrimination, prediction error, calibration, threshold-based classification, and decision-curve analysis.
Residual stones were identified in 82 of 300 patients (27.3%). The development cohort included 211 patients (58 events), and the validation cohort included 89 patients (24 events). Based solely on the out-of-fold performance within the development cohort, least absolute shrinkage and selection operator-penalized logistic regression (LASSO-LR) was selected as the final model. This model achieved a patient-level out-of-fold area under the receiver operating characteristic curve (AUC) of 0.849 [95% confidence interval (CI): 0.794-0.904] and a Brier score of 0.138. In the validation cohort, the AUC was 0.877 (95%CI: 0.807-0.947) with a Brier score of 0.134, confirming stable discrimination and predictive accuracy. Using the development-derived cutoff of 0.270, the validation sensitivity, specificity, and negative predictive value were 0.875, 0.785, and 0.944, respectively. The final model retained four predictors: Operative time, bilateral stone distribution, T-tube drainage, and postoperative biliary stricture before outcome ascertainment. Calibration intercepts neared zero in both cohorts, and decision-curve analysis showed net benefit across threshold probabilities from 0.05 to 0.40.
The LASSO-LR model demonstrated favorable internally validated performance for early postoperative residual-stone risk stratification and may facilitate individualized surveillance after hepatectomy for hepatolithiasis.
Core Tip: Residual stones remain an important and clinically consequential challenge after hepatectomy for hepatolithiasis, yet individualized tools for early postoperative risk stratification remain limited. In this retrospective cohort of 300 consecutive patients, 12 complete prediction pipelines were rigorously compared using repeated nested cross-validation within the development cohort, and least absolute shrinkage and selection operator-penalized logistic regression was selected before evaluation in a strictly held-out internal validation cohort. The final model retained only four routinely available perioperative predictors: Operative time, bilateral stone distribution, T-tube drainage, and postoperative biliary stricture documented before outcome ascertainment, and achieved an internally validated area under the receiver operating characteristic curve of 0.877 with a Brier score of 0.134 and a negative predictive value of 0.944. Its combination of parsimony, interpretability, low data-acquisition burden, and stable internally validated performance represents a practical strength for postoperative risk stratification. The model may help identify patients who warrant intensified surveillance and earlier imaging assessment, while independent external validation remains necessary before routine clinical implementation.