A multimodal fusion system predicting survival benefits of immune checkpoint inhibitors in unresectable hepatocellular carcinoma.

Journal: NPJ precision oncology
Published Date: Jun 14, 2025

Abstract

Early identification of unresectable hepatocellular carcinoma (HCC) patients who may benefit from immune checkpoint inhibitors (ICIs) is crucial for optimizing outcomes. Here, we developed a multimodal fusion (MMF) system integrating CT-derived deep learning features and clinical data to predict overall survival (OS) and progression-free survival (PFS). Using retrospective multicenter data (n = 859), the MMF combining an ensemble deep learning (Ensemble-DL) model with clinical variables achieved strong external validation performance (C-index: OS = 0.74, PFS = 0.69), outperforming radiomics (29.8% OS improvement), mRECIST (27.6% OS improvement), clinical benchmarks (C-index: OS = 0.67, p = 0.0011; PFS = 0.65, p = 0.033), and Ensemble-DL (C-index: OS = 0.69, p = 0.0028; PFS = 0.66, p = 0.044). The MMF system effectively stratified patients across clinical subgroups and demonstrated interpretability through activation maps and radiomic correlations. Differential gene expression analysis revealed enrichment of the PI3K/Akt pathway in patients identified by the MMF system. The MMF system provides an interpretable, clinically applicable approach to guide personalized ICI treatment in unresectable HCC.

Authors

  • Jun Xu
    Department of Nephrology, The Affiliated Baiyun Hospital of Guizhou Medical University, Guizhou, China.
  • Tengfei Wang
    Department of Cardiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
  • Junjun Li
  • Yong Wang
    State Key Laboratory of Chemical Biology and Drug Discovery, Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University Hunghom Kowloon Hong Kong P. R. China kwok-yin.wong@polyu.edu.hk.
  • Zhangxiang Zhu
    Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, P. R. China.
  • Xiao Fu
    State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China. fuxiaohhu@163.com.
  • Junjie Wang
    School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
  • Zhenglin Zhang
    Department of Computer Science, University of Maryland, College Park, 20740, USA.
  • Wei Cai
    Department of Gastrointestinal Surgery, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Ruipeng Song
    Department of Hepatobiliary Surgery, Division of Life Sciences and Medicine, Anhui Province Key Laboratory of Hepatopancreatobiliary Surgery, Anhui Provincial Clinical Research Center for Hepatobiliary Diseases, The First Affiliated Hospital of USTC, The University of Science and Technology of China, Hefei, 230001, P. R. China.
  • Changlong Hou
    Department of Interventional Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, P. R. China.
  • Li-Zhuang Yang
  • Hongzhi Wang
  • Stephen T C Wong
    Translational Biophotonics Laboratory, Department of Systems Medicine and Bioengineering, Houston Me, United States.
  • Hai Li
    School of Economics and Management, Shanghai University of Sport, Shanghai, China.

Keywords

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