Prediction of Early Recurrence in Intrahepatic Cholangiocarcinoma by Interpretable Machine Learning Model: A
Tingfeng Huang1,2, Qizhu Lin1,2, Kun Yu2
1Department of Hepatobiliary Pancreatic Surgery, The First Affiliated Hospital of Fujian Medical University.
Background:
Early recurrence of intrahepatic cholangiocarcinoma (ICC) is difficult to predict. Traditional machine learning prediction models, characterized by their black-box nature, may be biases or ethical risks.
Methods:
The XGBoost algorithm develops the machine learning prediction model. The area under the receiver operating characteristic curve (AUC) served for evaluating model performance. The SHAP algorithm conducts interpretability analysis.
Results:
A total of 503 patients with 323 in the training cohort and 180 in the validation cohort. Tumor size, lymph node metastasis, microvascular invasion (MVI), and CA19-9 levels were identified as independent predictors of ICC early recurrence. The predictive model demonstrated the highest discriminative power in both training and validation cohorts (AUC 0.76 vs. 0.72, respectively). SHAP analysis demonstrates the decision-making process of the machine learning model.
Conclusions:
The XGBoost model for predicting early recurrence of ICC demonstrates accuracy and reliability. Explainable machine learning models, which balance transparency and accuracy.
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