Ye XX, Qu HH, Yang C, Teng WJ, Chen YP, Lin JM, Wang XB. Precision medicine in the prediction of metachronous liver metastasis in rectal cancer: Applications and challenges. World J Gastrointest Oncol 2025; 17(4): 102469 [DOI: 10.4251/wjgo.v17.i4.102469]
Corresponding Author of This Article
Xiao-Bo Wang, PhD, The Fourth School of Clinical Medicine, Zhejiang Chinese Medical University, No. 548 Binwen Road, Binjiang District, Hangzhou 310000, Zhejiang Province, China. wxb2310@163.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Editorial
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 Gastrointest Oncol. Apr 15, 2025; 17(4): 102469 Published online Apr 15, 2025. doi: 10.4251/wjgo.v17.i4.102469
Precision medicine in the prediction of metachronous liver metastasis in rectal cancer: Applications and challenges
Xu-Xing Ye, Hui-Heng Qu, Chao Yang, Wei-Jun Teng, Yan-Ping Chen, Jun-Mei Lin, Xiao-Bo Wang
Xu-Xing Ye, Jun-Mei Lin, Department of Traditional Chinese Medicine, Jinhua Municipal Central Hospital, Jinhua 321000, Zhejiang Province, China
Hui-Heng Qu, Department of General Surgery, Wuxi No. 2 people’s Hospital, Wuxi 214002, Jiangsu Province, China
Chao Yang, Xiao-Bo Wang, The Fourth School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou 310000, Zhejiang Province, China
Wei-Jun Teng, Yan-Ping Chen, Department of Gastroenterology, Jinhua Municipal Central Hospital, Jinhua 321000, Zhejiang Province, China
Co-first authors: Xu-Xing Ye and Hui-Heng Qu.
Co-corresponding authors: Jun-Mei Lin and Xiao-Bo Wang.
Author contributions: Ye XX and Qu HH contributed equally; Ye XX and Lin JM wrote the paper; Wang XB and Qu HH revised the paper; Lin JM and Yang C polished the language; Teng WJ and Chen YP provided guidance on writing; All authors have read and approved the final manuscript.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
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: Xiao-Bo Wang, PhD, The Fourth School of Clinical Medicine, Zhejiang Chinese Medical University, No. 548 Binwen Road, Binjiang District, Hangzhou 310000, Zhejiang Province, China. wxb2310@163.com
Received: October 21, 2024 Revised: January 20, 2025 Accepted: January 22, 2025 Published online: April 15, 2025 Processing time: 158 Days and 5.2 Hours
Abstract
Rectal cancer is a major global health concern, and metachronous liver metastasis (MLM) significantly worsens patient prognosis. Advances in imaging and machine learning have led to the development of radiomics models, particularly those utilizing multiparametric magnetic resonance imaging, which are highly valuable in predicting MLM. These models analyze imaging features to provide insights that can aid clinical decision-making and potentially improve treatment outcomes and survival rates. However, realizing the full potential of radiomics models faces challenges in terms of accuracy, generalizability, and data dependency. This editorial comments on a study regarding radiomics prediction models for rectal cancer MLM published recently in the World Journal of Gastrointestinal Oncology, discusses the progress, challenges, and strategies for diagnostic models of MLM in rectal cancer, and proposes directions for future research.
Core Tip: Multimodal imaging techniques are capable of capturing morphological, metabolic, and physiological information of tumors, which aids in a deeper understanding of tumor heterogeneity. By integrating clinical, imaging, and biomarker data, the accuracy and precision of predictions can be enhanced, leading to a comprehensive understanding of tumor biology. Therefore, integrating expertise from oncology, radiology, imaging informatics, and machine learning can lead to the development of more accurate predictive models. Such interdisciplinary collaboration is crucial for advancing the field of cancer treatment.
