Retrospective Study
Copyright ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. May 15, 2022; 14(5): 1014-1026
Published online May 15, 2022. doi: 10.4251/wjgo.v14.i5.1014
Preoperative prediction of malignant potential of 2-5 cm gastric gastrointestinal stromal tumors by computerized tomography-based radiomics
Xue-Feng Sun, Hai-Tao Zhu, Wan-Ying Ji, Xiao-Yan Zhang, Xiao-Ting Li, Lei Tang, Ying-Shi Sun
Xue-Feng Sun, Hai-Tao Zhu, Wan-Ying Ji, Xiao-Yan Zhang, Xiao-Ting Li, Lei Tang, Ying-Shi Sun, Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Beijing 100142, China
Author contributions: Sun XF designed the study and was responsible for the work; Sun XF, Tang L, and Ji WY conducted data collection; Sun XF and Zhang XY conducted image measurement; Zhu HT and Li XT conducted statistical analyses; Sun XF wrote the paper; all authors edited the paper.
Supported by Beijing Hospitals Authority Ascent Plan, No. 20191103; Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support, No. ZYLX201803; Beijing Natural Science Foundation, No. Z180001 and No. Z200015; and PKU-Baidu Fund, No. 2020BD027.
Institutional review board statement: This retrospective study was approved by the Institutional Review Board of Peking University Cancer Hospital & Institute.
Informed consent statement: Patients were not required to give informed consent to the study because the analysis used anonymous clinical data that were obtained after each patient agreed to treatment by written consent.
Conflict-of-interest statement: The authors have no conflicts to declare.
Data sharing statement: The authors do not want to share the data.
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: Ying-Shi Sun, MD, Chief Doctor, Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, No. 52 Fucheng Road, Haidian District, Beijing 100142, China. sys27@163.com
Received: December 1, 2021
Peer-review started: December 1, 2021
First decision: December 27, 2021
Revised: December 29, 2021
Accepted: April 21, 2022
Article in press: April 21, 2022
Published online: May 15, 2022
Processing time: 160 Days and 1 Hours
Abstract
BACKGROUND

The use of endoscopic surgery for treating gastrointestinal stromal tumors (GISTs) between 2 and 5 cm remains controversial considering the potential risk of metastasis and recurrence. Also, surgeons are facing great difficulties and challenges in assessing the malignant potential of 2-5 cm gastric GISTs.

AIM

To develop and evaluate computerized tomography (CT)-based radiomics for predicting the malignant potential of primary 2-5 cm gastric GISTs.

METHODS

A total of 103 patients with pathologically confirmed gastric GISTs between 2 and 5 cm were enrolled. The malignant potential was categorized into low grade and high grade according to postoperative pathology results. Preoperative CT images were reviewed by two radiologists. A radiological model was constructed by CT findings and clinical characteristics using logistic regression. Radiomic features were extracted from preoperative contrast-enhanced CT images in the arterial phase. The XGboost method was used to construct a radiomics model for the prediction of malignant potential. Nomogram was established by combing the radiomics score with CT findings. All of the models were developed in a training group (n = 69) and evaluated in a test group (n = 34).

RESULTS

The area under the curve (AUC) value of the radiological, radiomics, and nomogram models was 0.753 (95% confidence interval [CI]: 0.597-0.909), 0.919 (95%CI: 0.828-1.000), and 0.916 (95%CI: 0.801-1.000) in the training group vs 0.642 (95%CI: 0.379-0.870), 0.881 (95%CI: 0.772-0.990), and 0.894 (95%CI: 0.773-1.000) in the test group, respectively. The AUC of the nomogram model was significantly larger than that of the radiological model in both the training group (Z = 2.795, P = 0.0052) and test group (Z = 2.785, P = 0.0054). The decision curve of analysis showed that the nomogram model produced increased benefit across the entire risk threshold range.

CONCLUSION

Radiomics may be an effective tool to predict the malignant potential of 2-5 cm gastric GISTs and assist preoperative clinical decision making.

Keywords: Gastrointestinal stromal tumors; Gastric gastrointestinal stromal tumors; Computed tomography; Malignant potential; Radiomics; Nomogram

Core Tip: The use of endoscopic surgery in gastrointestinal stromal tumors (GISTs) between 2 and 5 cm remains controversial considering the potential risk of metastasis and recurrence. Also, surgeons are facing great difficulties and challenges in assessing the malignant potential of 2-5 cm gastric GISTs. This study aimed to develop and evaluate computerized tomography-based radiomics for predicting the malignant potential of primary 2-5 cm gastric GISTs.