关键词: 肝细胞癌 , 肝移植 , 机器学习 , 多模态数据

Abstract:

Objective To improve the accuracy and clinical interpretability of recurrence prediction after liver transplantation in patients with hepatocellular carcinoma（HCC），and to explore the potential application of explainable machine learning models in integrating multimodal data. Methods This study included data from 138 liver transplant patients with liver cancer at the Third Medical Center of the Chinese People's Liberation Army General Hospital from December 2018 to December 2021. Preoperative contrast-enhanced CT radiomics features and clinical variables were extracted. Predictive models were developed using four machine learning methods ：LASSO regression，random forest，support vector machine（SVM），and neural network. Importance of variate was employed to identify key predictive factors. Model performance was evaluated using area under the receiver operating characteristiccurve（AUC），Brier score，and calibration curves. A nomogram was ultimately constructed based on important variables. Results The AUCs for the random forest model in predicting recurrence at 1，2，and 3 years were 0.881，0.906，and 0.915，respectively，significantly outperforming other models. The importance analysis identified five key imaging features，and the nomogram model combining these with clinical variables demonstrated good consistency and predictive capability. Conclusion Explainable machine learning models based on multimodal data can effectively improve the accuracy and transparency of recurrence prediction following liver transplantation in HCC patients. These models have strong clinical applicability and provide valuable support for individualized preoperative risk assessmentand treatment decision-making. 

Key words:

Hepatocellular carcinoma, Liver transplantation, Machine learning, Multimodal data