An Chao, Zuo Mengxuan, Li Wang, Wu Peihong
Department of Minimal invasive intervention, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China.
Department of Interventional Ultrasound, Fifth Medical Center of Chinese PLA General Hospital, Beijing, China.
Int J Surg. 2025 Jul 2. doi: 10.1097/JS9.0000000000002719.
Currently, there is still a lack of noninvasive, automated, and accurate machine-learning(ML) model for prognostic risk stratification of intermediate-stage hepatocellular carcinoma(HCC) after transarterial chemoembolization(TACE) .
We aimed to develop an ML model for prognostic risk stratification of intermediate-stage HCC after TACE to assist physicians in decision-making.
Between April 2008 and October 2022, consecutive patients with intermediate-stage HCC undergoing initial conventional TACE(cTACE) were retrospectively enrolled from seven tertiary hospitals.A system utilizing natural language processing technology was used to extract clinical information from electronic medical records to develop the ML models.The primary outcomes were 2-year HCC-related death and cancer-related survival(CRS,defined as the interval from initial TACE to either HCC-related death or last follow-up).The ML models' performance and their comparison with various biomarkers were assessed.
A total of 4,426 eligible patients were included(3906 male,520 female; median age, 54 years ± 11[standard deviation];2,667 in the training cohort,667 in the internal test cohort,and 1,092 patients in the external test cohort).Six ML models were developed, with the XGBoost model demonstrating the best predictive performance. It achieved an AUC of 0.842 (95% CI, 0.827-0.857) in the training cohort, 0.815 (95% CI, 0.783-0.847) in the internal test cohort, and 0.798 (95% CI,0.771-0.824) in the external test cohort. Among high-risk patients stratified by the XGBoost model, those who received TACE combined with microwave ablation had significantly higher cumulative CRS rates than those treated with TACE alone.
We developed a noninvasive, automated, and accurate ML model, the XGBoost model, with robust performance in prognostic risk stratification for intermediate-stage HCC following TACE.
目前，对于经动脉化疗栓塞术（TACE）后中期肝细胞癌（HCC）的预后风险分层，仍缺乏无创、自动化且准确的机器学习（ML）模型。
我们旨在开发一种用于TACE后中期HCC预后风险分层的ML模型，以协助医生进行决策。
在2008年4月至2022年10月期间，从七家三级医院回顾性纳入连续接受初次传统TACE（cTACE）的中期HCC患者。利用自然语言处理技术的系统用于从电子病历中提取临床信息以开发ML模型。主要结局为2年HCC相关死亡和癌症相关生存（CRS，定义为从初次TACE到HCC相关死亡或最后一次随访的间隔时间）。评估了ML模型的性能及其与各种生物标志物的比较。
共纳入4426例符合条件的患者（男性3906例，女性520例；中位年龄54岁±11[标准差]；训练队列2667例，内部测试队列667例，外部测试队列1092例）。开发了六个ML模型，其中XGBoost模型表现出最佳预测性能。在训练队列中其AUC为0.842（95%CI，0.827 - 0.857），在内部测试队列中为0.815（95%CI，0.783 - 0.847），在外部测试队列中为0.798（95%CI，0.771 - 0.824）。在由XGBoost模型分层的高危患者中，接受TACE联合微波消融的患者累积CRS率显著高于单纯接受TACE治疗的患者。
我们开发了一种无创、自动化且准确的ML模型，即XGBoost模型，在TACE后中期HCC的预后风险分层中具有强大的性能。
