Prediction model for hepatocellular carcinoma recurrence after hepatectomy: Machine learning-based development and interpretation study

Rongqiang Liu; Shinan Wu; Hao Yuan Yu; Kaining Zeng; Zhixing Liang; Siqi Li; Yongwei Hu; Yang Yang; Linsen Ye

doi:10.1016/j.heliyon.2023.e22458

Prediction model for hepatocellular carcinoma recurrence after hepatectomy: Machine learning-based development and interpretation study

Heliyon. 2023 Nov 19;9(11):e22458. doi: 10.1016/j.heliyon.2023.e22458. eCollection 2023 Nov.

Authors

Rongqiang Liu^{1

2}, Shinan Wu^{1

3}, Hao Yuan Yu¹, Kaining Zeng¹, Zhixing Liang¹, Siqi Li¹, Yongwei Hu¹, Yang Yang¹, Linsen Ye¹

Affiliations

¹ Department of Hepatic Surgery and Liver Transplantation Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
² Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China.
³ Institute of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China.

Abstract

Background: Identifying patients with hepatocellular carcinoma (HCC) at high risk of recurrence after hepatectomy can help to implement timely interventional treatment. This study aimed to develop a machine learning (ML) model to predict the recurrence risk of HCC patients after hepatectomy.

Methods: We retrospectively collected 315 HCC patients who underwent radical hepatectomy at the Third Affiliated Hospital of Sun Yat-sen University from April 2013 to October 2017, and randomly divided them into the training and validation sets at a ratio of 7:3. According to the postoperative recurrence of HCC patients, the patients were divided into recurrence group and non-recurrence group, and univariate and multivariate logistic regression were performed for the two groups. We applied six machine learning algorithms to construct the prediction models and performed internal validation by 10-fold cross-validation. Shapley additive explanations (SHAP) method was applied to interpret the machine learning model. We also built a web calculator based on the best machine learning model to personalize the assessment of the recurrence risk of HCC patients after hepatectomy.

Results: A total of 13 variables were included in the machine learning models. The multilayer perceptron (MLP) machine learning model was proved to achieve optimal predictive value in test set (AUC = 0.680). The SHAP method displayed that γ-glutamyl transpeptidase (GGT), fibrinogen, neutrophil, aspartate aminotransferase (AST) and total bilirubin (TB) were the top 5 important factors for recurrence risk of HCC patients after hepatectomy. In addition, we further demonstrated the reliability of the model by analyzing two patients. Finally, we successfully constructed an online web prediction calculator based on the MLP machine learning model.

Conclusion: MLP was an optimal machine learning model for predicting the recurrence risk of HCC patients after hepatectomy. This predictive model can help identify HCC patients at high recurrence risk after hepatectomy to provide early and personalized treatment.

Keywords: Hepatocellular carcinoma; Machine learning; Multilayer perceptron; Risk factor; Shapley additive explanations.