Clinical and CT machine learning model for predicting acute liver function deterioration in hepatocellular carcinoma patients after the first time TACE
10.13929/j.issn.1672-8475.2025.03.001
- VernacularTitle:临床及CT机器学习模型预测肝细胞癌患者首次TACE后急性肝功能恶化
- Author:
Yongnian REN
1
;
Changqian TANG
;
Xingbo WEI
;
Dongxiao LI
;
Liancai WANG
;
Deyu LI
Author Information
1. 郑州大学人民医院肝胆胰腺外科,河南 郑州 450003
- Publication Type:Journal Article
- Keywords:
carcinoma,hepatocellular;
chemoembolization,therapeutic;
machine learning;
acute liver function deterioration
- From:
Chinese Journal of Interventional Imaging and Therapy
2025;22(3):153-158
- CountryChina
- Language:Chinese
-
Abstract:
Objective To observe the value of machine learning(ML)models constructed based on pre-treatment clinical and CT features for predicting acute liver function deterioration(ALFD)in hepatocellular carcinoma(HCC)patients after the first time TACE.Methods Totally 320 HCC patients who underwent the first TACE were retrospectively enrolled and divided into training set(n=256)and test set(n=64)at the ratio of 4∶1.ALFD was evaluated according to clinical,laboratory and image findings within 2 weeks after TACE.Univariate analysis was performed to compare clinical baseline data and diameter of HCC on pre-TACE CT in training set,and parameters being statistical different between patients with and without ALFD were used to construct ML models using 9 different ML algorithms.The efficacy of each model for predicting ALFD in test set was evaluated,and the optimal model was selected.The calibration degree and clinical value of the optimal model were assessed in test set,and the contribution of each parameter was analyzed using SHAP method.Results In training set,76 cases were ALFD and 180 cases were non-ALFD,while in test set,18 cases were ALFD and 46 cases were non-ALFD.Among 9 ML models,the sensitivity,specificity,accuracy,area under the curve,F1 value and Kappa value of extreme gradient boosting(XGBoost)model in test set was 85.12%,89.34%,88.08%,0.927,0.811 and 0.725,respectively.XGBoost model was considered as the optimal one,with predicted probability in test set in good agreement with actual probability and high clinical net benefit.The contribution of patients'age,lesion diameter on pre-TACE CT,glutamic-pyruvic transaminase,glutamic-oxaloacetic transaminase and TACE time were all great for XGBoost model.Conclusion XGBoost model based on pre-treatment clinical and CT features could be used to effectively predict ALFD in HCC patients after the first time TACE.
