Wu Linxia, Chen Lei, Zhang Lijie, Liu Yiming, Ouyang Die, Wu Wenlong, Lei Yu, Han Ping, Zhao Huangxuan, Zheng Chuansheng
Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, 430022, People's Republic of China.
Department of Interventional Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, 430022, People's Republic of China.
J Hepatocell Carcinoma. 2025 Jan 21;12:77-91. doi: 10.2147/JHC.S496481. eCollection 2025.
Type II diabetes mellitus (T2DM) has been found to increase the mortality of patients with hepatocellular carcinoma (HCC). Therefore, this study aimed to establish and validate a machine learning-based explainable prediction model of prognosis in patients with HCC and T2DM undergoing transarterial chemoembolization (TACE).
The prediction model was developed using data from the derivation cohort comprising patients from three medical centers, followed by external validation utilizing patient data extracted from another center. Further, five predictive models were employed to establish prognosis models for 1-, 2-, and 3-year survival, respectively. Prediction performance was assessed by the receiver operating characteristic (ROC), calibration, and decision curve analysis curves. Lastly, the SHapley Additive exPlanations (SHAP) method was used to interpret the final ML model.
A total of 636 patients were included. Thirteen variables were selected for the model development. The final random survival forest (RSF) model exhibited excellent performance in the internal validation cohort, with areas under the ROC curve (AUCs) of 0.824, 0.853, and 0.810 in the 1-, 2-, and 3-year survival groups, respectively. This model also demonstrated remarkable discrimination in the external validation cohort, achieving AUCs of 0.862, 0.815, and 0.798 in the 1-, 2-, and 3-year survival groups, respectively. SHAP summary plots were also created to interpret the RSF model.
An RSF model with good predictive performance was developed by incorporating simple parameters. This prognostic prediction model may assist physicians in early clinical intervention and improve prognosis outcomes in patients with HCC and comorbid T2DM after TACE.
已发现2型糖尿病（T2DM）会增加肝细胞癌（HCC）患者的死亡率。因此，本研究旨在建立并验证一种基于机器学习的可解释预测模型，用于预测接受经动脉化疗栓塞术（TACE）的HCC合并T2DM患者的预后。
使用来自三个医疗中心患者的推导队列数据开发预测模型，随后利用从另一个中心提取的患者数据进行外部验证。此外，采用五种预测模型分别建立1年、2年和3年生存的预后模型。通过受试者工作特征（ROC）、校准和决策曲线分析曲线评估预测性能。最后，使用SHapley加性解释（SHAP）方法解释最终的机器学习模型。
共纳入636例患者。为模型开发选择了13个变量。最终的随机生存森林（RSF）模型在内部验证队列中表现出优异的性能，1年、2年和3年生存组的ROC曲线下面积（AUC）分别为0.824、0.853和0.810。该模型在外部验证队列中也表现出显著的区分能力，1年、2年和3年生存组的AUC分别为0.862、0.815和0.798。还创建了SHAP总结图来解释RSF模型。
通过纳入简单参数开发了具有良好预测性能的RSF模型。这种预后预测模型可能有助于医生进行早期临床干预，并改善TACE术后HCC合并T2DM患者的预后结果。
