Development and validation of machine learning-based model for predicting early recurrence for patients with
Zi-Chen Yu1, Zhe-Jin Shi2, Zheng-Kang Fang1
1General Surgery, Cancer Center, Department of Hepatobiliary & Pancreatic Surgery and Minimally Invasive Surgery, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China; Department of Postgraduate Training Base Alliance of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Aims:
Early recurrence (ER) is strongly associated with poor long-term survival in patients with hepatocellular carcinoma (HCC). This study aimed to explore a prediction model based on machine learning (ML).
Methods:
Six ML algorithms were constructed and compared. The top-performing model was further compared with a conventional logistic regression model. Model interpretability was assessed using SHapley Additive exPlanations (SHAP) values at both global and individual levels.
Results:
Among 903 patients included, 351 (38.9 %) experienced ER within two years. As a result, the random forest (RF) model was selected and demonstrated superior discrimination (AUC 0.917) compared with the logistic regression model (AUC 0.853). SHAP analysis identified multiple tumors, microvascular invasion, and tumor size >5 cm as the key contributors to ER. An online calculator based on the RF model is accessible at: https://doctoryu.shinyapps.io/HCCEarlyRecurrencePredictor/.
Conclusion:
The RF model offers a clinically interpretable and deployable tool to support individualized postoperative surveillance strategies.
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