Explainable attention-enhanced heuristic paradigm for multi-view prognostic risk score development in hepatocellular carcinoma.

Journal: Hepatology international
Published Date: Mar 16, 2025

Abstract

PURPOSE: Existing prognostic staging systems depend on expensive manual extraction by pathologists, potentially overlooking latent patterns critical for prognosis, or use black-box deep learning models, limiting clinical acceptance. This study introduces a novel deep learning-assisted paradigm that complements existing approaches by generating interpretable, multi-view risk scores to stratify prognostic risk in hepatocellular carcinoma (HCC) patients.

Authors

  • Anran Liu
    Department of Health Technology and Informatics, Hong Kong Polytechnic University, 11 Yuk Choi Road, Hong Kong SAR, China.
  • Jiang Zhang
    College of Electrical Engineering and Information Technology, Sichuan University, Chengdu, 610065, China.
  • Tong Li
    School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing 102488, China.
  • Danyang Zheng
    School of Urban Railway Transportation, Shanghai University of Engineering Science, Shanghai 201620, China.
  • Yihong Ling
    Department of Pathology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, 651 Dongfeng East Road, Guangzhou, 510060, Guangdong, China.
  • Lianghe Lu
    Department of Liver Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, 651 Dongfeng East Road, Guangzhou, 510060, Guangdong, China.
  • Yuanpeng Zhang
  • Jing Cai
    Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.

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