机器学习预测乐伐替尼联合经动脉化疗栓塞术治疗不可切除肝细胞癌的疗效

Machine Learning to Predict the Response to Lenvatinib Combined with Transarterial Chemoembolization for Unresectable Hepatocellular Carcinoma.

作者信息

Ma Jun, Bo Zhiyuan, Zhao Zhengxiao, Yang Jinhuan, Yang Yan, Li Haoqi, Yang Yi, Wang Jingxian, Su Qing, Wang Juejin, Chen Kaiyu, Yu Zhengping, Wang Yi, Chen Gang

机构信息

Department of Epidemiology and Biostatistics, School of Public Health and Management, Wenzhou Medical University, Wenzhou 325035, China.

Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325035, China.

出版信息

Cancers (Basel). 2023 Jan 19;15(3):625. doi: 10.3390/cancers15030625.

DOI:10.3390/cancers15030625

PMID:36765583

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9913670/

Abstract

BACKGROUND

Lenvatinib and transarterial chemoembolization (TACE) are first-line treatments for unresectable hepatocellular carcinoma (HCC), but the objective response rate (ORR) is not satisfactory. We aimed to predict the response to lenvatinib combined with TACE before treatment for unresectable HCC using machine learning (ML) algorithms based on clinical data.

METHODS

Patients with unresectable HCC receiving the combination therapy of lenvatinib combined with TACE from two medical centers were retrospectively collected from January 2020 to December 2021. The response to the combination therapy was evaluated over the following 4-12 weeks. Five types of ML algorithms were applied to develop the predictive models, including classification and regression tree (CART), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM). The performance of the models was assessed by the receiver operating characteristic (ROC) curve and area under the receiver operating characteristic curve (AUC). The Shapley Additive exPlanation (SHAP) method was applied to explain the model.

RESULTS

A total of 125 unresectable HCC patients were included in the analysis after the inclusion and exclusion criteria, among which 42 (33.6%) patients showed progression disease (PD), 49 (39.2%) showed stable disease (SD), and 34 (27.2%) achieved partial response (PR). The nonresponse group (PD + SD) included 91 patients, while the response group (PR) included 34 patients. The top 40 most important features from all 64 clinical features were selected using the recursive feature elimination (RFE) algorithm to develop the predictive models. The predictive power was satisfactory, with AUCs of 0.74 to 0.91. The SVM model and RF model showed the highest accuracy (86.5%), and the RF model showed the largest AUC (0.91, 95% confidence interval (CI): 0.61-0.95). The SHAP summary plot and decision plot illustrated the impact of the top 40 features on the efficacy of the combination therapy, and the SHAP force plot successfully predicted the efficacy at the individualized level.

CONCLUSIONS

A new predictive model based on clinical data was developed using ML algorithms, which showed favorable performance in predicting the response to lenvatinib combined with TACE for unresectable HCC. Combining ML with SHAP could provide an explicit explanation of the efficacy prediction.

摘要

背景

乐伐替尼和经动脉化疗栓塞术（TACE）是不可切除肝细胞癌（HCC）的一线治疗方法，但客观缓解率（ORR）并不令人满意。我们旨在使用基于临床数据的机器学习（ML）算法，在不可切除HCC治疗前预测乐伐替尼联合TACE的疗效。

方法

回顾性收集2020年1月至2021年12月期间在两个医疗中心接受乐伐替尼联合TACE联合治疗的不可切除HCC患者。在接下来的4 - 12周内评估联合治疗的疗效。应用五种类型的ML算法来开发预测模型，包括分类与回归树（CART）、自适应增强（AdaBoost）、极端梯度提升（XGBoost）、随机森林（RF）和支持向量机（SVM）。通过受试者工作特征（ROC）曲线和受试者工作特征曲线下面积（AUC）评估模型的性能。应用Shapley加性解释（SHAP）方法来解释模型。

结果

纳入和排除标准后，共有125例不可切除HCC患者纳入分析，其中42例（33.6%）患者出现疾病进展（PD），49例（39.2%）患者疾病稳定（SD），34例（27.2%）患者获得部分缓解（PR）。无反应组（PD + SD）包括91例患者，而反应组（PR）包括34例患者。使用递归特征消除（RFE）算法从所有64个临床特征中选择前40个最重要的特征来开发预测模型。预测能力令人满意，AUC为0.74至0.91。SVM模型和RF模型显示出最高的准确率（86.5%），RF模型显示出最大的AUC（0.91，95%置信区间（CI）：0.61 - 0.95）。SHAP汇总图和决策图说明了前40个特征对联合治疗疗效的影响，SHAP力场图成功地在个体水平上预测了疗效。