Wang F, Huang C, Ma HQ, Yao XQ, Wang JJ, Long J. Mesentery morphological features on computed tomography for preoperative prediction of tumor invasion and lymph node metastasis in colon cancer. World J Clin Oncol 2025; 16(7): 108095 [DOI: 10.5306/wjco.v16.i7.108095]
Corresponding Author of This Article
Jie Long, Doctor, Department of Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan 2nd Road, Yuexiu District, Guangzhou 510080, Guangdong Province, China. longjie@gdph.org.cn
Research Domain of This Article
Oncology
Article-Type of This Article
Retrospective Study
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. Jul 24, 2025; 16(7): 108095 Published online Jul 24, 2025. doi: 10.5306/wjco.v16.i7.108095
Mesentery morphological features on computed tomography for preoperative prediction of tumor invasion and lymph node metastasis in colon cancer
Fei Wang, Chuan Huang, Hai-Qing Ma, Xue-Qing Yao, Jun-Jiang Wang, Jie Long
Fei Wang, Chuan Huang, Hai-Qing Ma, Jie Long, Department of Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, Guangdong Province, China
Xue-Qing Yao, Jun-Jiang Wang, Department of Gastrointestinal Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, Guangdong Province, China
Co-corresponding authors: Jun-Jiang Wang and Jie Long.
Author contributions: Wang F and Huang C collected and analyzed the data and wrote the manuscript; Ma HQ and Yao XQ participated in the data analysis; Wang JJ and Long J designed and supervised the study, provided expertise in the analysis of the data, and revised the manuscript; all authors have contributed to the manuscript and approved the submitted version.
Supported by National Natural Science Foundation of China, No. 82303785; and Medical Scientific Research Foundation of Guangdong Province, No. A2024096.
Institutional review board statement: The study protocol was approved by Guangdong Provincial People’s Hospital Ethics Review Committee.
Informed consent statement: The informed consent requirement was waived in this retrospective study.
Conflict-of-interest statement: The authors declare that there are no competing interests associated with the manuscript.
Data sharing statement: The data included in this study are available from the corresponding authors upon request.
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: Jie Long, Doctor, Department of Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan 2nd Road, Yuexiu District, Guangzhou 510080, Guangdong Province, China. longjie@gdph.org.cn
Received: April 7, 2025 Revised: April 30, 2025 Accepted: June 13, 2025 Published online: July 24, 2025 Processing time: 108 Days and 19.2 Hours
Abstract
BACKGROUND
Accurate identification of tumor invasion depth and lymph node (LN) involvement in patients with colon cancer (CC) is critical for guiding treatment strategies. However, the preoperative prediction of tumor invasion depth and LN metastasis in CC remains challenging. As the intestinal tumor develops, the fat density in the mesentery increases.
AIM
To investigate the efficacy of computed tomography (CT) value change in the mesentery contributed by the tumor (CT-T value) for predicting tumor invasion depth and LN metastasis.
METHODS
Patients, who were diagnosed with CC and underwent surgery, were included and divided into the training and validation cohorts. CT-T values of the mesentery were extracted from the CT images. Cutoff points were determined using the receiver operating characteristic (ROC) curve, and the area under the ROC curve was employed to assess the performance of the CT-T value for tumor invasion depth and LN status prediction.
RESULTS
Cutoff values of 11.83 and 17.17 were identified to discriminate T1/2 vs T3/4 and N0 vs N1/2, respectively. With a cutoff CT-T value of 11.83, the total diagnostic accuracy for T stage was 83.1% (81.5% for the training cohort and 86.2% for the validation cohort). With a cutoff CT-T value of 17.17, the total diagnostic accuracy for N stage was 77.3% (75.8% for the training cohort and 80.1% for the validation cohort), which was higher than that of CT-reported LN metastasis.
CONCLUSION
In this study, we explored an efficient method for predicting preoperative T and N stages using the tumor-contributed CT value of the mesentery in CC, which displayed superior predictive accuracy.
Core Tip: Preoperative prediction of tumor invasion depth and lymph node (LN) metastasis of colon cancer (CC) remains challenging. As the intestinal tumor develops, the fat density of the mesentery increases. Our study utilized computed tomography (CT) value change in the mesentery contributed by the tumors to discriminate tumor invasion depth and LN metastasis. The total diagnostic accuracy for T stage was 83.1%, and for N stage was 77.3%, which was higher than the CT-reported LN metastasis. This efficient method for predicting preoperative T and N stages using tumor-contributed CT value of the mesentery in CC displayed superior predictive accuracy.
