Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.
World J Gastrointest Oncol. Oct 15, 2026; 18(10): 122464
Published online Oct 15, 2026. doi: 10.4251/wjgo.122464
Published online Oct 15, 2026. doi: 10.4251/wjgo.122464
Deep learning-enhanced peritumoral radiomics predicts early recurrence after hepatocellular carcinoma ablation: A two-center study
Yong-Hai Li, Department of Anorectal Surgery, The Third Affiliated Hospital of Anhui Medical University, The First People’s Hospital of Hefei, Hefei 230001, Anhui Province, China
Ling Yao, Department of Anorectal Surgery, Guang’ Anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
Gui-Xiang Qian, Bao-Yun Guo, Rui Du, The Third Affiliated Hospital of Anhui Medical University, The First People’s Hospital of Hefei, Hefei 230001, Anhui Province, China
Xue-Di Lei, Zi-Qi Tang, Department of Colorectal Surgery, Bengbu Medical University, Bengbu 233000, Anhui Province, China
Yu Zhu, Department of Hepatopancreatobiliary Surgery, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou 318000, Zhejiang Province, China
Wei-Dong Jia, Department of Hepatic Surgery, The First Affiliated Hospital of University of Science and Technology of China, University of Science and Technology of China, Hefei 230001, Anhui Province, China
Co-first authors: Yong-Hai Li and Ling Yao.
Author contributions: Li YH and Yao L contributed equally to this work as co-first authors; Li YH, Yao L, Qian GX and Jia WD revised the manuscript; Li YH, Yao L, Qian GX, Lei XD, Zi-Qi Tang, and Zhu Y collected the data; Li YH and Jia WD designed the research study; Guo BY and Du R analyzed the data; all authors wrote the manuscript, have read and approve the final manuscript.
AI contribution statement: The authors declare that no AI tools were used in the preparation of this manuscript. All writing, data analysis, and figure preparation were performed solely by the authors. The authors take full responsibility for the integrity, accuracy, and originality of the work.
Supported by The National Key Research and Development Program of China, No. 2022YFA1304504; and Health Science and Technology Projects of Hefei Municipal Health Commission, No. Hwk2025zd005.
Institutional review board statement: This study was approved by approved by the Ethics Management Committee of the First Affiliated Hospital of University of Science and Technology of China, No. 2021-RE-043.
Informed consent statement: The requirement for informed consent was waived owing to the retrospective nature of the study.
Conflict-of-interest statement: All authors declare no conflict of interest in publishing the manuscript.
STROBE statement: The authors have read the STROBE Statement – checklist of items, and the manuscript was prepared and revised according to the STROBE Statement – checklist of items.
Data sharing statement: The datasets generated and/or analysed during the current study are not publicly available due to patient privacy and copyright issues but are available from the corresponding author upon reasonable request.
Corresponding author: Wei-Dong Jia, Full Professor, Department of Hepatic Surgery, The First Affiliated Hospital of University of Science and Technology of China, University of Science and Technology of China, No. 17 Lujiang Road, Luyang District, Hefei 230001, Anhui Province, China. niche118118@sina.com
Received: April 20, 2026
Revised: June 11, 2026
Accepted: July 30, 2026
Published online: October 15, 2026
Processing time: 152 Days and 14.2 Hours
Revised: June 11, 2026
Accepted: July 30, 2026
Published online: October 15, 2026
Processing time: 152 Days and 14.2 Hours
Core Tip
Core Tip: This study developed a deep learning-enhanced peritumoral radiomics model to predict early recurrence after hepatocellular carcinoma ablation. Among the evaluated peritumoral regions, the 10-mm peritumoral model showed the best numerical performance. The combined model integrating 10-mm peritumoral deep learning features and clinical imaging features enabled individualized recurrence risk stratification and may aid in developing personalized treatment strategies.