Combining traditional analysis and machine learning to predict early, middle, and long-term recurrence of intrahepatic cholangiocarcinoma

Abstract

Introduction

Intrahepatic cholangiocarcinoma (ICC) is a rare and highly aggressive cancer. Few patients are eligible for radical surgery, and most face the high risk of recurrence.

Methods

We developed early-, middle- and long-term (1-, 2-, and 3-year) ICC disease-free survival (DFS) prediction models using traditional Logistic analysis combined with machine learning (ML) and systematically compared the performance of traditional analysis and MLs.

Results

275, 256, and 238 ICC patients under radical surgery were included in the 1-, 2-, and 3-year DFS groups respectively. Five-fold cross-validation results demonstrated that both traditional Logistics and ML models exhibited remarkable robustness. MLs outperformed traditional Logistic models for DFS prediction across the AUC, accuracy and F1-scores. Specifically, the average AUC of training cohorts for the ML models were 0.878, 0.897 and 0.917 in 3 groups, compared to 0.657 (P < 0.001), 0.817 (P = 0.05), and 0.798 (P = 0.005) in traditional models. The average AUCs of testing cohorts for ML models were 0.831, 0.768, 0.803 in ML models in 3 groups, compared to 0.619 (P < 0.001), 0.719 (P = 0.008), 0.698 (P < 0.001) in traditional models. SHAP analysis identified lymph node metastasis played significant role in all-round recurrence, T stage and neural invasion had strong correction with middle and long-term recurrence in ICC patients.

Conclusion

Models with high predictive efficiency across early, middle, and long-term recurrence have been successfully built. ML models outperformed Logistic models for DFS prediction in ICC patients. This study suggests new possibilities for advancing statistical analysis software, such as SPSS and Stata, through ML integration.