Artificial intelligence-driven pathomics in hepatocellular carcinoma: current developments, challenges and perspectives - Wei Ding; Jinxing Zhang; Zhicheng Jin; Hongjin Hua; Qingquan Zu; Shudong Yang; Weidong Wang; Sheng Liu; Haifeng Zhou; Haibin Shi (Discover Oncology)
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.