Guan SH, Wang Q, Ma XM, Qiao WJ, Li MZ, Lai MG, Wang C. Development of an innovative nomogram of risk factors to predict postoperative recurrence of gastrointestinal stromal tumors. World J Gastrointest Surg 2022; 14(9): 940-949 [PMID: 36185569 DOI: 10.4240/wjgs.v14.i9.940]
Corresponding Author of This Article
Cheng Wang, MM, Chief Doctor, Professor, Department of Gastrointestinal Tumor Surgery, The Affiliated Hospital of Qinghai University, No. 29 Tongren Road, Xining 810001, Qinghai Province, China. cheng_wang_vip@163.com
Research Domain of This Article
Gastroenterology & Hepatology
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/
Shi-Hao Guan, Department of General Surgery, Qingpu Branch of Zhongshan Hospital Affiliated to Fudan University, Shanghai 201700, China
Shi-Hao Guan, Medical College, Qinghai University, Xining 810001, Qinghai Province, China
Qiong Wang, Department of Medical Oncology, The Affiliated Hospital of Qinghai University, Xining 810001, Qinghai Province, China
Xiao-Ming Ma, Wen-Jie Qiao, Ming-Zheng Li, Ming-Gui Lai, Cheng Wang, Department of Gastrointestinal Tumor Surgery, The Affiliated Hospital of Qinghai University, Xining 810001, Qinghai Province, China
Author contributions: Guan SH is the author of the main idea, conceived and designed the study, collected and analyzed the data, and wrote and revised the manuscript; Wang C conceived and designed the study, collected and analyzed the data and wrote the manuscript; Wang Q and Ma XM participated in drafting and revising the manuscript, and collected the data; Qiao WJ, Li MZ and Lai MG revised the manuscript and analyzed the data. All authors approved the final version of the manuscript.
Institutional review board statement: This study was approved by the Institutional Research Ethics Board of Qinghai University Affiliated Hospital (Xining, China) and followed the Declaration of Helsinki.
Informed consent statement: All patients signed informed consent forms.
Conflict-of-interest statement: Each author in this paper declares no conflict of interest.
Data sharing statement: No additional data are available.
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: Cheng Wang, MM, Chief Doctor, Professor, Department of Gastrointestinal Tumor Surgery, The Affiliated Hospital of Qinghai University, No. 29 Tongren Road, Xining 810001, Qinghai Province, China. cheng_wang_vip@163.com
Received: April 14, 2022 Peer-review started: April 14, 2022 First decision: June 10, 2022 Revised: July 2, 2022 Accepted: August 7, 2022 Article in press: August 7, 2022 Published online: September 27, 2022
Abstract
BACKGROUND
There are many staging systems for gastrointestinal stromal tumors (GISTs), and the risk indicators selected are also different; thus, it is not possible to quantify the risk of recurrence among individual patients.
AIM
To develop and internally validate a model to identify the risk factors for GIST recurrence after surgery.
METHODS
The least absolute shrinkage and selection operator (LASSO) regression model was performed to identify the optimum clinical features for the GIST recurrence risk model. Multivariable logistic regression analysis was used to develop a prediction model that incorporated the possible factors selected by the LASSO regression model. The index of concordance (C-index), calibration curve, receiver operating characteristic curve (ROC), and decision curve analysis were used to assess the discrimination, calibration, and clinical usefulness of the predictive model. Internal validation of the clinical predictive capability was also evaluated by bootstrapping validation.
RESULTS
The nomogram included tumor site, lesion size, mitotic rate/50 high power fields, Ki-67 index, intracranial necrosis, and age as predictors. The model presented perfect discrimination with a reliable C-index of 0.836 (95%CI: 0.712-0.960), and a high C-index value of 0.714 was also confirmed by interval validation. The area under the curve value of this prediction nomogram was 0.704, and the ROC result indicated good predictive value. Decision curve analysis showed that the predicting recurrence nomogram was clinically feasible when the recurrence rate exceeded 5% after surgery.
CONCLUSION
This recurrence nomogram combines tumor site, lesion size, mitotic rate, Ki-67 index, intracranial necrosis, and age and can easily predict patient prognosis.
Core Tip: This is a retrospective study to explore the risk factors for gastrointestinal stromal tumors recurrence after surgery. The nomogram included tumor site, lesion size, mitotic rate/50 high power fields, Ki-67 index, intracranial necrosis, and age as predictors. The model presented perfect discrimination with a reliable index of concordance (C-index) of 0.836 (95%CI: 0.712-0.960), and a high C-index value of 0.714 was also confirmed by interval validation. The area under the curve value of this prediction nomogram was 0.704, indicating good predictive value. Decision curve analysis showed that the predicting recurrence nomogram was clinically feasible.
