Deyu Lu,1 Lingling Zhou,2 Ziyi Zuo,3 Zhao Zhang,4 Xiangwu Zheng,4 Jialu Weng,1 Zhijie Yu,1 Jiansong Ji,2,5 Jinglin Xia1,6,7 

1Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325035, People’s Republic of China; 2Key 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; 3Division of Pulmonary Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou Key Laboratory of Interdiscipline and Translational Medicine, Wenzhou Key Laboratory of Heart and Lung, Wenzhou, Zhejiang, 325000, People’s Republic of China; 4Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People’s Republic of China; 5Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000, People’s Republic of China; 6Liver Cancer Institute, Zhongshan Hospital of Fudan University, Shanghai, 200032, People’s Republic of China; 7National Clinical Research Center for Interventional Medicine, Zhongshan Hospital of Fudan University, Shanghai, 200032, People’s Republic of China

Correspondence: Jinglin Xia, Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, The First Affiliated Hospital of Wenzhou Medical University, Nanbaixiang Street, Ouhai District, Wenzhou, 325035, People’s Republic of China, Email xiajinglin@fudan.edu.cn Jiansong Ji, 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, 289 Kuocang Road, Lishui, 323000, People’s Republic of China, Email jjstcty@wmu.edu.cn

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.

Keywords: radiomics, HCC, portal vein tumor thrombus, lenvatinib, immune checkpoint inhibitor