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

作者：Rongqiang Liu, Shi‐Nan Wu, Hao Yu, Kaining Zeng, Zhixing Liang, Siqi Li, Yongwei Hu, Yang Yang, Linsen Ye · 发表于：Heliyon · 年份：2023 · DOI：10.1016/j.heliyon.2023.e22458 · 被引用次数：17 · 研究领域：Hepatocellular Carcinoma Treatment and Prognosis、Inflammatory Biomarkers in Disease Prognosis、Radiomics and Machine Learning in Medical Imaging

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 (A...