Pretreatment Multi-sequence Contrast-Enhanced MRI to Predict Response to Immunotherapy in Unresectable Hepatocellular Carcinoma Using Transformer: A Multicenter Study.

Journal: Journal of Cancer
Published Date: Jun 12, 2025

Abstract

Targeted combined immunotherapy (TCI) has shown certain antitumor effects in patients with unresectable hepatocellular carcinoma(uHCC), but only a subset of patients benefit. This study aims to develop a Transformer-based radiomics model to predict the objective response to combined therapy in patients with uHCC. This multicenter, retrospective study involved 264 HCC patients who underwent contrast-enhanced MRI prior to immunotherapy. The patients were divided into a training cohort(n=180) and a validation cohort(n=84). Using a multi-instance learning approach, tumor lesions in multi-sequence MRI were segmented into cross-sectional images, and features were extracted using the ResNet50 model. The Transformer model was then trained to predict the objective response rate (ORR). The prediction process was visualized using Grad-CAM and SHAP algorithms. Model performance was assessed using ROC and DCA curves, while survival analysis was conducted using Kaplan-Meier curves. Among 264 patients, one achieved complete response (0.4%), 64 experienced partial response (24.2%). The ORR was 26.1% in the training group and 21.4% in the validation group. The model demonstrated high predictive accuracy, achieving a perfect area under the curve (AUC) of 1.000. Further validation using screenshot-based model inputs revealed an AUC of 0.929 (95% CI: 0.904, 0.947), confirming the model's clinical applicability. Kaplan-Meier analysis indicated that objective responders experienced better overall survival (OS) in both the training set (HR: 0.50, 95% CI: 0.27, 0.90) and the validation set (HR: 0.28, 95% CI: 0.08, 0.91). The deep learning framework combining ResNet50 and Transformer has proven its clinical applicability in predicting and assessing the efficacy of targeted combination immunotherapy in unresectable hepatocellular carcinoma, providing crucial guidance for clinical treatment decisions.

Authors

  • Jialin Chen
    Department of Ultrasound, Guangzhou Red Cross Hospital (Guangzhou Red Cross Hospital of Jinan University), GuangZhou, China.
  • Juan Chen
    Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing 100191, China. chenjuan94@bjmu.edu.cn.
  • Yamei Ye
    Department of Hepatology, Mengchao Hepatobiliary Hospital of Fujian Medical University, 350028, Fuzhou, Fujian, PR China.
  • Linbin Lu
    Department of Oncology, Mengchao Hepatobiliary Hospital of Fujian Medical University, 350028, Fuzhou, Fujian, PR China.
  • Xinying Guo
    Department of Oncology, Mengchao Hepatobiliary Hospital of Fujian Medical University, 350028, Fuzhou, Fujian, PR China.
  • Simiao Gao
    Department of Oncology, the 900th Hospital of Joint Logistic Support Force, PLA, Fuzong Clinical College of Fujian Medical University, 350001, Fuzhou, Fujian, PR China.
  • Lifang Liu
    Department of Breast Surgery, First Affiliated Hospital of Hunan University of Traditional Chinese Medicine, Changsha, 410007, Hunan, China.
  • Hongyi Yang
    Department of Oncology, Fuzhou General Teaching Hospital of Fujian University of Traditional Chinese Medicine, 350001, Fuzhou, Fujian, PR China.
  • Chun Lin
    Department of Hepatology, Mengchao Hepatobiliary Hospital of Fujian Medical University, 350028, Fuzhou, Fujian, PR China.
  • Xiong Chen

Keywords

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