磁共振成像放射组学预测经动脉化疗栓塞联合乐伐替尼加PD-1抑制剂对伴门静脉癌栓肝细胞癌的早期治疗反应
MRI Radiomics to Predict Early Treatment Response to TACE Combined with Lenvatinib Plus a PD-1 Inhibitor for Hepatocellular Carcinoma with Portal Vein Tumor Thrombus.
作者信息
Lu Deyu, Zhou Lingling, Zuo Ziyi, Zhang Zhao, Zheng Xiangwu, Weng Jialu, Yu Zhijie, Ji Jiansong, Xia Jinglin
机构信息
Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325035, People's Republic of China.
Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Hospital of Zhejiang University, Lishui, 323000, People's Republic of China.
出版信息
J Hepatocell Carcinoma. 2025 May 16;12:985-998. doi: 10.2147/JHC.S513696. eCollection 2025.
PURPOSE
To develop and validate a predictor for early treatment response in hepatocellular carcinoma (HCC) patients accompanied by portal vein tumor thrombus (PVTT) undergoing transarterial chemoembolization (TACE), lenvatinib and a programmed cell death protein 1 (PD-1) inhibitor (TLP) therapy.
PATIENTS AND METHODS
In this retrospective study, patients with HCC and PVTT from two institutions receiving triple TLP therapy were enrolled. Radiomics features derived from pretreatment contrast-enhanced MRI were curated using intraclass correlation coefficient (ICC), Student's -test, least absolute shrinkage and selection operator (LASSO), and recursive feature elimination (RFE) to ensure robust selection. Various machine learning (ML) algorithms were then used to construct the models. The meaningful clinical indicators were obtained via logistic regression analysis and ultimately integrated with radiomics features to develop a combined model. In addition, we used Shapley Additive exPlanation (SHAP) to clarify the model's operational dynamics.
RESULTS
Our study ultimately included 115 patients (7:3 randomization, 80 and 35 in the training and test cohorts, respectively) in total. No patients achieved complete remission, 47 achieved partial remission, 29 achieved stable disease, and 39 experienced disease progression. Among objective response rates (ORRs) and disease control rates (DCRs), 40.9% and 66.1% were reported. One of the four ML classifiers with optimal performance, namely random forest, was adopted as the radiomics model after testing. Regarding the performance assessment, the radiomics model's area under the curve (AUC) values reached 0.92 (95% CI: 0.86-0.97) and 0.79 (95% CI: 0.61-0.95), inferior to the combined model's AUCs of 0.95 (95% CI: 0.68-0.98) and 0.84 (95% CI: 0.91-0.99). Moreover, the SHAP plots illustrate the importance of global variables and the prediction process for individual samples.
CONCLUSION
The model based on machine learning and radiomics showed favorable performance, and the operating mode was visualized through SHAP.
目的
开发并验证一种预测指标，用于预测接受经动脉化疗栓塞（TACE）、乐伐替尼和程序性细胞死亡蛋白1（PD-1）抑制剂（TLP）联合治疗的伴有门静脉癌栓（PVTT）的肝细胞癌（HCC）患者的早期治疗反应。
患者与方法
在这项回顾性研究中，纳入了来自两家机构接受三联TLP治疗的HCC合并PVTT患者。使用组内相关系数（ICC）、学生t检验、最小绝对收缩和选择算子（LASSO）以及递归特征消除（RFE）对治疗前对比增强MRI得出的影像组学特征进行筛选，以确保稳健选择。然后使用各种机器学习（ML）算法构建模型。通过逻辑回归分析获得有意义的临床指标，并最终将其与影像组学特征整合以开发联合模型。此外，我们使用Shapley加性解释（SHAP）来阐明模型的运行动态。
结果
我们的研究最终共纳入115例患者（7:3随机分组，训练组和测试组分别为80例和35例）。无患者达到完全缓解，47例达到部分缓解，29例病情稳定，39例病情进展。客观缓解率（ORR）和疾病控制率（DCR）分别为40.9%和66.1%。在测试后，采用性能最佳的四个ML分类器之一即随机森林作为影像组学模型。关于性能评估，影像组学模型的曲线下面积（AUC）值分别达到0.92（95%CI：0.86 - 0.97）和0.79（95%CI：0.61 - 0.95），低于联合模型的AUC值0.95（95%CI：0.68 - 0.98）和0.84（95%CI：0.91 - 0.99）。此外，SHAP图说明了全局变量的重要性以及单个样本的预测过程。
结论
基于机器学习和影像组学的模型表现出良好的性能，并通过SHAP将运行模式可视化。
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