European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology · 2025
Combining traditional analysis and machine learning to predict early, middle, and long-term recurrence of intrahepatic cholangiocarcinoma.
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Zhang Ruoyu, Wang Zengshuai, Yang Min, Chen Bo, Liu Mei, Zheng Minhua, Liu Peter Xiaoping, Wang Liming
Аннотация
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.
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.