Retrospective Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Oct 15, 2024; 16(10): 4104-4114
Published online Oct 15, 2024. doi: 10.4251/wjgo.v16.i10.4104
Constructing a nomogram to predict overall survival of colon cancer based on computed tomography characteristics and clinicopathological factors
Zhe-Xing Hu, Yin Li, Xuan Yang, Yu-Xia Li, Yao-Yao He, Xiao-Hui Niu, Ting-Ting Nie, Xiao-Fang Guo, Zi-Long Yuan
Zhe-Xing Hu, Yin Li, Yao-Yao He, Ting-Ting Nie, Xiao-Fang Guo, Zi-Long Yuan, Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430079, Hubei Province, China
Xuan Yang, Department of Radiology, Wuhan Children’s Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430014, Hubei Province, China
Yu-Xia Li, Xiao-Hui Niu, College of Informatics, Huazhong Agriculture University, Wuhan 430070, Hubei Province, China
Co-first authors: Zhe-Xing Hu and Yin Li.
Co-corresponding authors: Xiao-Fang Guo and Zi-Long Yuan.
Author contributions: Guo XF and Yuan ZL designed the research study; Hu ZX and Li Y collected data; Hu ZX wrote the paper; Li YX and Niu XH made statistics; Yang X, He YY and Nie TT revised the manuscript; and all authors had checked and approved the final manuscript. Hu ZX and Li Y contributed equally to this work as co-first authors. Guo XF and Yuan ZL were appointed for this paper. Firstly, the two professors participated in the design of the research study, provided research ideas, and made important revisions to the paper during the writing process, and finally finalized the manuscript. Secondly, these two professors have played a significant role in project management and team collaboration. Finally, Professor Guo XF also provided the fund support, and Professor Yuan ZL participated in the submission and communicated with the magazine. Therefore, both corresponding authors have made important contributions to the article, and this contribution is equal. For this reason, the article designates these two co-corresponding authors.
Supported by Cancer Research Program of National Cancer Center, No. NCC201917B05; and Special Research Fund Project of Biomedical Center of Hubei Cancer Hospital, No. 2022SWZX06.
Institutional review board statement: The study was reviewed and approved by the Ethics Committee of Hubei Cancer Hospital.
Informed consent statement: Patients were not required to give informed consent to the study because we had acquired the Ethics committee’s approval of exemption of the subject’s informed consent. This study does not have direct contact with the subjects, and only collects clinical baseline data from outpatient and inpatient medical records. the study results will remove any characters with the subjects’ identification to ensure that personal privacy will not be disclosed. Therefore, objectively, there will be no risk to the subjects.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: Data will be made available on reasonable 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: Xiao-Fang Guo, PhD, Associate Chief Physician, Doctor, Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 116 Zhuodaoquan South Road, Hongshan District, Wuhan 430079, Hubei Province, China. guoxiaofang2001@163.com
Received: March 18, 2024
Revised: August 18, 2024
Accepted: September 6, 2024
Published online: October 15, 2024
Processing time: 191 Days and 18.7 Hours
Core Tip

Core Tip: It is necessary to establish an accurate survival prediction model for colon cancer to improve patient prognosis. This study combined computed tomography imaging features and clinicopathological factors to identify independent risk factors associated with overall survival using univariate and multivariate logistic regression analyses. A nomogram model was constructed, and it demonstrated high accuracy.