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©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
Relationship between multi-slice computed tomography features and pathological risk stratification assessment in gastric gastrointestinal stromal tumors
Tian-Tian Wang, Wei-Wei Liu, Xian-Hai Liu, Rong-Ji Gao, Chun-Yu Zhu, Qing Wang, Lu-Ping Zhao, Xiao-Ming Fan, Juan Li
Tian-Tian Wang, Rong-Ji Gao, Chun-Yu Zhu, Xiao-Ming Fan, Juan Li, Department of Medical Imaging, The Second Affiliated Hospital of Shandong First Medical University, Taian 271000, Shandong Province, China
Wei-Wei Liu, Department of Rheumatology, The Second Affiliated Hospital of Shandong First Medical University, Taian 271000, Shandong Province, China
Xian-Hai Liu, Department of Network Information Center, The Second Affiliated Hospital of Shandong First Medical University, Taian 271000, Shandong Province, China
Qing Wang, Department of Ultrasound, The Second Affiliated Hospital of Shandong First Medical University, Taian 271000, Shandong Province, China
Lu-Ping Zhao, Department of Medical Imaging, The Affiliated Hospital of Ji’ning Medical University, Jining 272000, Shandong Province, China
Author contributions: Wang TT designed and performed the research and wrote the paper; Li J designed the research and supervised the report; Liu XH designed the research and contributed to the analysis; Liu WW, Gao RJ, Zhu CY, and Wang Q provided clinical advice; Zhao LP and Fan XM supervised the report.
Supported by the Roentgen Imaging Research Project of Beijing Kangmeng Charitable Foundation, No. SD-202008-017.
Institutional review board statement: This study was approved by the Institutional Ethics Committee of the Second Affiliated Hospital of Shandong First Medical University (2022-016).
Informed consent statement: This is retrospective study that used anonymous clinical data. According to institutional policies, informed consent was not required from patients in this study.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: The data for this study can be obtained from the corresponding author 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: Juan Li, MM, Attending Doctor, Department of Medical Imaging, The Second Affiliated Hospital of Shandong First Medical University, No. 366 Taishan Street, Taian 271000, Shandong Province, China. 191962554@163.com
Received: March 9, 2023
Peer-review started: March 9, 2023
First decision: March 22, 2023
Revised: April 2, 2023
Accepted: April 25, 2023
Article in press: April 25, 2023
Published online: June 15, 2023
ARTICLE HIGHLIGHTS
Research background
Clinical decision-making depends on preoperative assessment of the likelihood of malignancy and prognosis of these gastrointestinal stromal tumors (GISTs). Correlation between computed tomography (CT) image features of GIST and pathological risk grade has been previously reported in several publications. However, only a few studies have attempted to correlate CT features with histologic grading or prediction of gastric malignancy.
Research motivation
The research is to explore the multi-slice CT imaging features for predicting risk stratification in patients with primary gastric GISTs, and to give clinicians a straightforward yet useful tool to use in choosing the best surgical approach and preoperative neoadjuvant therapy.
Research objectives
The purpose of this study was to identify the CT imaging characteristics for predicting risk stratifications in patients with primary gastric GISTs.
Research methods
This retrospective analysis of clinicopathological and CT imaging data for 147 patients with gastric GISTs. The association between malignant potential and CT features was analyzed using univariate analysis and multivariate logistic regression analysis, receiver operating curve was used to evaluate the predictive value of tumor size, and the multinomial logistic regression model for risk classification.
Research results
Tumor size, tumor contours, lesion growth patterns, ulceration, cystic degeneration or necrosis, lymphadenopathy, and contrast enhancement patterns, were associated with the risk stratification; tumor size, contours and growth pattern were independent predictors for risk stratification of gastric GISTs.
Research conclusions
CT features, including tumor size, growth patterns, and lesion contours, were predictors of malignant potential for primary gastric GISTs.
Research perspectives
The CT characteristics could offer clinicians a straightforward yet useful tool for making smart clinical judgments.
