Liu QZ, Zeng L, Sun NZ. Radiomics for preoperative pancreatic ductal adenocarcinoma risk stratification: Cross-population validation, multidimensional integration, challenges, and future directions. World J Radiol 2025; 17(7): 110048 [DOI: 10.4329/wjr.v17.i7.110048]
Corresponding Author of This Article
Nian-Zhe Sun, MD, National Clinical Research Center of Geriatric Disorders, Department of Orthopedics, Xiangya Hospital, Central South University, No. 87 Xiangya Road, Kaifu District, Changsha 410008, Hunan Province, China. sunnzh201921@sina.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 Radiol. Jul 28, 2025; 17(7): 110048 Published online Jul 28, 2025. doi: 10.4329/wjr.v17.i7.110048
Radiomics for preoperative pancreatic ductal adenocarcinoma risk stratification: Cross-population validation, multidimensional integration, challenges, and future directions
Qin-Zhi Liu, Lei Zeng, Nian-Zhe Sun
Qin-Zhi Liu, Lei Zeng, Nian-Zhe Sun, National Clinical Research Center of Geriatric Disorders, Department of Orthopedics, Xiangya Hospital, Central South University, Changsha 410008, Hunan Province, China
Co-corresponding authors: Lei Zeng and Nian-Zhe Sun.
Author contributions: Liu QZ wrote the first draft and developed the main ideas; Zeng L directed the analytical framework, coordinated interdisciplinary collaborations, and supervised the interpretation of results alongside manuscript finalization; Sun NZ spearheaded the conception and design of the study, provided critical revision of the manuscript, and led revisions; Zeng L and Sun NZ have played important and indispensable roles in the manuscript preparation as the co-corresponding authors; all of the authors read and approved the final version of the manuscript to be published.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest 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: Nian-Zhe Sun, MD, National Clinical Research Center of Geriatric Disorders, Department of Orthopedics, Xiangya Hospital, Central South University, No. 87 Xiangya Road, Kaifu District, Changsha 410008, Hunan Province, China. sunnzh201921@sina.com
Received: May 28, 2025 Revised: June 9, 2025 Accepted: July 10, 2025 Published online: July 28, 2025 Processing time: 58 Days and 7.8 Hours
Core Tip
Core Tip: This editorial critically evaluated the retrospective study by Liu et al in validating a preoperative computed tomography-based risk scoring system, while high-lighting the potential of radiomics integrated with machine learning for multimodal data analysis. However, clinical translation continues to face significant challenges, including heterogeneity in imaging parameters, incomplete elucidation of molecular mechanisms, and confounding effects of therapeutic interventions. Future research should prioritize the development of multidimensional predictive frameworks that synergize radiological, molecular, and clinical data. Prospective clinical trials to validate model efficacy and artificial intelligence-driven real-time risk stratification systems represent critical initiatives to overcome current limitations in precision medicine implementation, thereby advancing personalized management strategies for pancreatic ductal adenocarcinoma.
