Artificial intelligence-driven pathomics in hepatocellular carcinoma: current developments, challenges and perspectives.

Journal: Discover oncology
Published Date: Jul 28, 2025

Abstract

Hepatocellular carcinoma (HCC) is a highly malignant tumor with elevated incidence and mortality rates globally. Its complex etiology and pronounced heterogeneity present significant challenges in diagnosis and treatment. Recent advancements in artificial intelligence (AI) have demonstrated transformative potential to usher a new wave of precision oncology. Pathomics, an AI-based digital pathology technique, facilitates the extraction of extensive datasets from whole-slide histopathological images, enabling quantitative analyses to improve diagnosis, treatment, and prognostic prediction for HCC. Furthermore, emerging pathological foundation models are revolutionizing traditional paradigms and providing a robust framework for the development of specialized pathomics models tailored to specific clinical tasks in HCC. Despite its promise, pathomics research in HCC remains in its infancy, with clinical implementation hindered by challenges such as data heterogeneity, model interpretability, ethical concerns, regulatory issues, and the absence of standardized industry protocols. Future initiatives should prioritize the conduction of prospective multi-center studies, the integration of multi-modal data, the enhancement of regulatory frameworks, and the establishment of industry-wide standardized guidelines and compliant platform infrastructures to accelerate the clinical adoption of pathomics for personalized HCC treatment.

Authors

  • Wei Ding
    Division of Stem Cell and Tissue Engineering, Regenerative Medicine Research Center, West China Hospital, Sichuan University, Chengdu Sichuan, 610041, P.R.China.
  • Jinxing Zhang
    Department of Interventional Radiology, The First Affiliated Hospital with Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, 210029, China.
  • Zhicheng Jin
    Center of Interventional Radiology and Vascular Surgery, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, No. 87 Dingjiaqiao Road, Nanjing, 210009, China.
  • Hongjin Hua
    Department of Pathology, The First Affiliated Hospital with Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, 210029, China.
  • Qingquan Zu
    Department of Interventional Radiology, The First Affiliated Hospital with Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, 210029, China.
  • Shudong Yang
    Department of Pathology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, No. 299 Qingyang Road, Wuxi, 214023, China.
  • Weidong Wang
    Zhejiang Huade New Materials Co., Ltd., Zhejiang Province, Hangzhou, China.
  • Sheng Liu
    Medical School, Xizang Minzu University, Xianyang, People's Republic of China.
  • Haifeng Zhou
    Department of Mechanical and Electrical Engineering, Soochow University, Suzhou, Jiangsu, China.
  • Haibin Shi
    School of Information Science and Engineering, Xiamen University, Xiamen 361005, China. shihaibin@xmu.edu.cn.

Keywords

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