Retrospective Study
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Radiol. Aug 28, 2025; 17(8): 109373
Published online Aug 28, 2025. doi: 10.4329/wjr.v17.i8.109373
Developing and validating a computed tomography radiomics strategy to predict lymph node metastasis in pancreatic cancer
Shuai Ren, Bin Qin, Marcus J Daniels, Liang Zeng, Ying Tian, Zhong-Qiu Wang
Shuai Ren, Bin Qin, Liang Zeng, Ying Tian, Zhong-Qiu Wang, Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, Jiangsu Province, China
Marcus J Daniels, Department of Radiology, NYU Langone Health, New York, NY 10016, United States
Author contributions: Ren S, Qin B, and Wang ZQ designed the research study; Ren S, Tian Y, and Zeng L performed the research; Ren S, Qin B, and Zeng L analyzed the data. Ren S wrote the manuscript; Daniels MJ and Wang ZQ revised the manuscript; all authors read and approved the final manuscript.
Supported by National Natural Science foundation of China, No. 82202135, No. 82371919, No. 82372017, and No. 82171925; China Postdoctoral Science Foundation, No. 2023M741808; Young Elite Scientists Sponsorship Program by China Association of Chinese Medicine, No. 2024-QNRC2-B16; Jiangsu Provincial Key Research and Development Program, No. BE2023789; Young Elite Scientists Sponsorship Program by Jiangsu Association for Science and Technology, No. JSTJ-2023-WJ027; Project funded by Nanjing Postdoctoral Science Foundation, Natural Science Foundation of Nanjing University of Chinese Medicine, No. XZR2023036; and Foundation of Excellent Young Doctor of Jiangsu Province Hospital of Chinese Medicine, No. 2023QB0112.
Institutional review board statement: The study was reviewed and approved by the Ethics Committee of Affiliated Hospital of Nanjing University of Chinese Medicine.
Informed consent statement: Informed consent statement was waived due to the retrospective nature of the study.
Conflict-of-interest statement: All the authors report having no relevant conflicts of interest for this article.
Data sharing statement: Patient imaging data contain sensitive patient information and cannot be released publicly due to the legal and ethical restrictions imposed by the institutional ethics committee. Data is available upon reasonable request from the following e-mail address: zhongqiuwang@njucm.edu.cn.
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: Zhong-Qiu Wang, MD, Deputy Director, Head, Professor, Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, No. 155 Hanzhong Road, Nanjing 210029, Jiangsu Province, China. zhongqiuwang@njucm.edu.cn
Received: May 12, 2025
Revised: May 21, 2025
Accepted: July 22, 2025
Published online: August 28, 2025
Processing time: 112 Days and 0.9 Hours
Abstract
BACKGROUND

Lymph node metastasis (LNM) is a key prognostic factor in pancreatic cancer (PC). Accurate preoperative prediction of LNM remains challenging. Radiomics offers a noninvasive method to extract quantitative imaging features that may aid in predicting LNM.

AIM

To investigate the potential value of a computed tomography (CT)-based radiomics model in prediction of LNM in PC.

METHODS

A retrospective analysis was performed on 168 pathologically confirmed PC patients who underwent contrast-enhanced-CT. Among them, 107 cases had no LNM, while 61 cases had confirmed LNM. These patients were randomly divided into a training cohort (n = 135) and a validation cohort (n = 33). A total of 792 radiomics features were extracted, comprising 396 features from the arterial phase and another 396 from the portal venous phase. The Minimum Redundancy Maximum Relevance and Least Absolute Shrinkage and Selection Operator methods were used for feature selection and Radiomics model construction. The receiver operating characteristic curve was employed to assess the diagnostic potential of the model, and DeLong test was used to compare the area under the curve (AUC) values of the model.

RESULTS

Six radiomics features from the arterial phase and nine from the portal venous phase were selected. The Radscore model demonstrated strong predictive performance for LNM in both the training and test cohorts, with AUC values ranging from 0.86 to 0.94, sensitivity between 66.7% and 91.7%, specificity from 71.4% to 100.0%, accuracy between 78.8% and 91.1%, PPV ranging from 64.7% to 100.0%, and negative predictive value between 84.0% and 93.8%. No significant differences in AUC values were observed between the arterial and portal venous phases in either the training or test set.

CONCLUSION

The preoperative CT-based radiomics model exhibited robust predictive capability for identifying LNM in PC.

Keywords: Computed tomography; Radiomics; Lymph node metastasis; Pancreatic cancer; Model construction

Core Tip: A preoperative computed tomography-based radiomics model demonstrates high accuracy in predicting lymph node metastasis (LNM) in pancreatic cancer, providing a non-invasive tool to guide personalized treatment. Unlike traditional imaging, radiomics detects microstructural patterns invisible to the human eye, enhancing LNM detection irrespective of phase (arterial vs portal). Clinically, this model could refine preoperative staging, identify candidates for curative surgery, or prioritize neoadjuvant chemotherapy for high-risk patients, optimizing outcomes. Prospective validation is needed for broader adoption.