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.

IF 4.2 3区 医学 Q2 ONCOLOGY
Journal of Hepatocellular Carcinoma Pub Date : 2025-05-16 eCollection Date: 2025-01-01 DOI:10.2147/JHC.S513696
Deyu Lu, Lingling Zhou, Ziyi Zuo, Zhao Zhang, Xiangwu Zheng, Jialu Weng, Zhijie Yu, Jiansong Ji, Jinglin Xia
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Abstract

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 t-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.

MRI放射组学预测TACE联合Lenvatinib + PD-1抑制剂治疗肝细胞癌合并门静脉肿瘤血栓的早期治疗反应
目的:开发并验证肝细胞癌(HCC)合并门静脉肿瘤血栓(PVTT)患者接受经动脉化疗栓塞(TACE)、lenvatinib和程序性细胞死亡蛋白1 (PD-1)抑制剂(TLP)治疗的早期治疗反应预测因子。患者和方法:在这项回顾性研究中,纳入了来自两个机构接受三联TLP治疗的HCC和PVTT患者。通过使用类内相关系数(ICC)、Student’st检验、最小绝对收缩和选择算子(LASSO)以及递归特征消除(RFE)对预处理对比增强MRI得出的放射组学特征进行筛选,以确保鲁棒性选择。然后使用各种机器学习(ML)算法来构建模型。通过logistic回归分析获得有意义的临床指标,最终与放射组学特征相结合,形成联合模型。此外,我们使用Shapley加性解释(SHAP)来阐明模型的运行动力学。结果:我们的研究最终共纳入115例患者(7:3随机分组,训练组和试验组分别为80例和35例)。没有患者达到完全缓解,47例达到部分缓解,29例达到疾病稳定,39例出现疾病进展。客观缓解率(orr)和疾病控制率(dcr)分别为40.9%和66.1%。经过测试,采用四个ML分类器中性能最优的一个,即随机森林作为放射组学模型。在性能评估方面,放射组学模型的曲线下面积(AUC)值分别为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图说明了全局变量和个体样本预测过程的重要性。结论:基于机器学习和放射组学的模型表现出良好的性能,并且可以通过SHAP可视化操作模式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
0.50
自引率
2.40%
发文量
108
审稿时长
16 weeks
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