Peng MH, Zhang KL, Guan SW, Lin Q, Yu HB. Advances and challenges in pathomics for liver cancer: From diagnosis to prognostic stratification. World J Clin Oncol 2025; 16(6): 107646 [DOI: 10.5306/wjco.v16.i6.107646]
Corresponding Author of This Article
Hai-Bo Yu, Chief, PhD, Department of Hepatobiliary Surgery, The Dingli Clinical Institute of Wenzhou Medical University (Wenzhou Central Hospital), Wenzhou 325000, Zhejiang Province, China. zjuboby@zuaa.zju.edu.cn
Research Domain of This Article
Oncology
Article-Type of This Article
Review
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Clin Oncol. Jun 24, 2025; 16(6): 107646 Published online Jun 24, 2025. doi: 10.5306/wjco.v16.i6.107646
Advances and challenges in pathomics for liver cancer: From diagnosis to prognostic stratification
Ming-Hui Peng, Kai-Lun Zhang, Shi-Wei Guan, Quan Lin, Hai-Bo Yu
Ming-Hui Peng, Kai-Lun Zhang, Shi-Wei Guan, Quan Lin, Hai-Bo Yu, Department of Hepatobiliary Surgery, The Dingli Clinical Institute of Wenzhou Medical University (Wenzhou Central Hospital), Wenzhou 325000, Zhejiang Province, China
Author contributions: All authors contributed to the original ideas and writing of this paper. Peng MH wrote the paper; Zhang KL, Guan SW and Lin Q draw tables; Yu HB made critical revisions of this paper.
Supported by Wenzhou Municipal Science and Technology Bureau, No. Y20240109.
Conflict-of-interest statement: Authors declare no conflict of interests for this article.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Hai-Bo Yu, Chief, PhD, Department of Hepatobiliary Surgery, The Dingli Clinical Institute of Wenzhou Medical University (Wenzhou Central Hospital), Wenzhou 325000, Zhejiang Province, China. zjuboby@zuaa.zju.edu.cn
Received: April 1, 2025 Revised: April 11, 2025 Accepted: May 28, 2025 Published online: June 24, 2025 Processing time: 84 Days and 22.1 Hours
Core Tip
Core Tip: Artificial intelligence (AI)-powered pathomics revolutionizes liver cancer management by decoding histopathological patterns through the use of deep learning models such as microvascular invasion (MVI)-AI diagnostic model and CHOWDER, which excel in detecting MVI, immune biomarkers, and prognostic features. The integration of multiomics data links tumour morphology with molecular pathways (e.g., EZH2 dysregulation and immune evasion). Key challenges persist in model generalizability, interpretability, and clinical integration. Future advancements will require cross-modal AI systems combining radiogenomics and liquid biopsy alongside standardized platforms to translate pathomics results into personalized therapeutic strategies for hepatocellular carcinoma and metastatic liver malignancies.
