Zheng HD, Hu YH, Ye K, Xu JH. Development and validation of a nomogram for preoperative prediction of tumor deposits in colorectal cancer. World J Gastroenterol 2023; 29(39): 5483-5493 [PMID: 37900997 DOI: 10.3748/wjg.v29.i39.5483]
Corresponding Author of This Article
Jian-Hua Xu, MD, Chief Physician, Dean, Research Dean, Surgeon, Surgical Oncologist, Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Fujian Medical University, No. 950 Donghai Street, Fengze District, Quanzhou 362000, Fujian Province, China. xjh630913@126.com
Research Domain of This Article
Oncology
Article-Type of This Article
Retrospective Cohort 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 Gastroenterol. Oct 21, 2023; 29(39): 5483-5493 Published online Oct 21, 2023. doi: 10.3748/wjg.v29.i39.5483
Development and validation of a nomogram for preoperative prediction of tumor deposits in colorectal cancer
Hui-Da Zheng, Yun-Huang Hu, Kai Ye, Jian-Hua Xu
Hui-Da Zheng, Yun-Huang Hu, Kai Ye, Jian-Hua Xu, Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, Fujian Province, China
Author contributions: Zheng HD designed/planned the study and wrote the paper; Zheng HD, Hu YH and Ye K acquired and analyzed data and performed computational modeling; Xu JH participated in discussion of related data; All authors read and approved the final manuscript.
Supported byFujian Province Science and Technology Innovation Joint Fund Project, No. 2021Y9029.
Institutional review board statement: The study was reviewed and approved for publication by our Institutional Reviewer.
Informed consent statement: As the study used anonymous and pre-existing data, the requirement for the informed consent from patients was waived.
Conflict-of-interest statement: All the Authors have no conflict of interest related to the manuscript.
Data sharing statement: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
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: Jian-Hua Xu, MD, Chief Physician, Dean, Research Dean, Surgeon, Surgical Oncologist, Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Fujian Medical University, No. 950 Donghai Street, Fengze District, Quanzhou 362000, Fujian Province, China. xjh630913@126.com
Received: July 11, 2023 Peer-review started: July 11, 2023 First decision: September 18, 2023 Revised: September 21, 2023 Accepted: September 28, 2023 Article in press: September 28, 2023 Published online: October 21, 2023 Processing time: 100 Days and 3 Hours
Abstract
BACKGROUND
Based on the clinical data of colorectal cancer (CRC) patients who underwent surgery at our institution, a model for predicting the formation of tumor deposits (TDs) in this patient population was established.
AIM
To establish an effective model for predicting TD formation, thus enabling clinicians to identify CRC patients at high risk for TDs and implement personalized treatment strategies.
METHODS
CRC patients (n = 645) who met the inclusion criteria were randomly divided into training (n = 452) and validation (n = 193) cohorts using a 7:3 ratio in this retrospective analysis. Least absolute shrinkage and selection operator regression was employed to screen potential risk factors, and multivariable logistic regression analysis was used to identify independent risk factors. Subsequently, a predictive model for TD formation in CRC patients was constructed based on the independent risk factors. The discrimination ability of the model, its consistency with actual results, and its clinical applicability were evaluated using receiver-operating characteristic curves, area under the curve (AUC), calibration curves, and decision curve analysis (DCA).
RESULTS
Thirty-four (7.5%) patients with TDs were identified in the training cohort based on postoperative pathological specimens. Multivariate logistic regression analysis identified female sex, preoperative intestinal obstruction, left-sided CRC, and lymph node metastasis as independent risk factors for TD formation. The AUCs of the nomogram models constructed using these variables were 0.839 and 0.853 in the training and validation cohorts, respectively. The calibration curve demonstrated good consistency, and the training cohort DCA yielded a threshold probability of 7%-78%.
CONCLUSION
This study developed and validated a nomogram with good predictive performance for identifying TDs in CRC patients. Our predictive model can assist surgeons in making optimal treatment decisions.
Core Tip: This article retrospectively analyzed the risk factors for tumor deposits (TDs) in colorectal cancer (CRC) and established a nomogram that included female sex, preoperative intestinal obstruction, left-sided CRC, and lymph node metastasis. This model enabled clinicians to identify high-risk populations for TDs in CRC patients and implement personalized treatment strategies.
