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Vision transformer-based model can optimize curative-intent treatment for patients with recurrent hepatocellular carcinoma

Author

Listed:
  • Ke Zhang

    (Zhejiang University School of Medicine)

  • Jinyu Ru

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Wenbo Wang

    (Peking University Cancer Hospital & Institute)

  • Qiuping Ma

    (The Third Affiliated Hospital of Sun Yat-sen University)

  • Fengwei Gao

    (Sichuan University and Collaborative Innovation Center of Biotherapy)

  • Jiapeng Wu

    (Nankai University
    Fifth Medical Center of Chinese PLA General Hospital)

  • Zhifei Dai

    (Peking University)

  • Qingyun Xie

    (Sichuan University and Collaborative Innovation Center of Biotherapy)

  • Lei Mu

    (Zhejiang University School of Medicine)

  • Haoyan Zhang

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Jinhua Pan

    (Zhejiang University School of Medicine)

  • Liting Xie

    (Zhejiang University School of Medicine)

  • Qiyu Zhao

    (Zhejiang University School of Medicine)

  • Jie Tian

    (Chinese Academy of Sciences
    Beihang University)

  • Jie Yu

    (Fifth Medical Center of Chinese PLA General Hospital)

  • Ping Liang

    (Fifth Medical Center of Chinese PLA General Hospital)

  • Hong Wu

    (Sichuan University and Collaborative Innovation Center of Biotherapy)

  • Kai Li

    (The Third Affiliated Hospital of Sun Yat-sen University)

  • Wei Yang

    (Peking University Cancer Hospital & Institute)

  • Kun Wang

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Tianan Jiang

    (Zhejiang University School of Medicine)

Abstract

The treatment selection for recurrent hepatocellular carcinoma (rHCC) within Milan criteria after hepatectomy remains challenging. Here, we present HEROVision, a Vision Transformer-based model designed for personalized prognosis prediction and treatment optimization between thermal ablation (TA) and surgical resection (SR). HEROVision is trained on initial HCC cohorts (8492 images; 772 patients) and independently tested on rHCC cohorts (9163 images; 833 patients) from five centers. Propensity score matching (PSM) forms two groups of rHCC patients underwent TA and SR to fairly evaluate whether optimized treatment selection by HEROVision have clinical benefits. HEROVision significantly outperforms all six guideline staging systems in the external testing cohort, both in time-dependent concordance index and area under the curve (all P

Suggested Citation

  • Ke Zhang & Jinyu Ru & Wenbo Wang & Qiuping Ma & Fengwei Gao & Jiapeng Wu & Zhifei Dai & Qingyun Xie & Lei Mu & Haoyan Zhang & Jinhua Pan & Liting Xie & Qiyu Zhao & Jie Tian & Jie Yu & Ping Liang & Hon, 2025. "Vision transformer-based model can optimize curative-intent treatment for patients with recurrent hepatocellular carcinoma," Nature Communications, Nature, vol. 16(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-59197-0
    DOI: 10.1038/s41467-025-59197-0
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    References listed on IDEAS

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    1. Gil Shamai & Amir Livne & António Polónia & Edmond Sabo & Alexandra Cretu & Gil Bar-Sela & Ron Kimmel, 2022. "Deep learning-based image analysis predicts PD-L1 status from H&E-stained histopathology images in breast cancer," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
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