Retrospective Study
Copyright ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Jun 15, 2023; 15(6): 1073-1085
Published online Jun 15, 2023. doi: 10.4251/wjgo.v15.i6.1073
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
Processing time: 97 Days and 21.9 Hours
Abstract
BACKGROUND

Computed tomography (CT) imaging features are associated with risk stratification of gastric gastrointestinal stromal tumors (GISTs).

AIM

To determine the multi-slice CT imaging features for predicting risk stratification in patients with primary gastric GISTs.

METHODS

The clinicopathological and CT imaging data for 147 patients with histologically confirmed primary gastric GISTs were retrospectively analyzed. All patients had received dynamic contrast-enhanced CT (CECT) followed by surgical resection. According to the modified National Institutes of Health criteria, 147 lesions were classified into the low malignant potential group (very low and low risk; 101 lesions) and high malignant potential group (medium and high-risk; 46 lesions). The association between malignant potential and CT characteristic features (including tumor location, size, growth pattern, contour, ulceration, cystic degeneration or necrosis, calcification within the tumor, lymphadenopathy, enhancement patterns, unenhanced CT and CECT attenuation value, and enhancement degree) was analyzed using univariate analysis. Multivariate logistic regression analysis was performed to identify significant predictors of high malignant potential. The receiver operating curve (ROC) was used to evaluate the predictive value of tumor size and the multinomial logistic regression model for risk classification.

RESULTS

There were 46 patients with high malignant potential and 101 with low-malignant potential gastric GISTs. Univariate analysis showed no significant differences in age, gender, tumor location, calcification, unenhanced CT and CECT attenuation values, and enhancement degree between the two groups (P > 0.05). However, a significant difference was observed in tumor size (3.14 ± 0.94 vs 6.63 ± 3.26 cm, P < 0.001) between the low-grade and high-grade groups. The univariate analysis further revealed that CT imaging features, including tumor contours, lesion growth patterns, ulceration, cystic degeneration or necrosis, lymphadenopathy, and contrast enhancement patterns, were associated with risk stratification (P < 0.05). According to binary logistic regression analysis, tumor size [P < 0.001; odds ratio (OR) = 26.448; 95% confidence interval (CI): 4.854-144.099)], contours (P = 0.028; OR = 7.750; 95%CI: 1.253-47.955), and mixed growth pattern (P = 0.046; OR = 4.740; 95%CI: 1.029-21.828) were independent predictors for risk stratification of gastric GISTs. ROC curve analysis for the multinomial logistic regression model and tumor size to differentiate high-malignant potential from low-malignant potential GISTs achieved a maximum area under the curve of 0.919 (95%CI: 0.863-0.975) and 0.940 (95%CI: 0.893-0.986), respectively. The tumor size cutoff value between the low and high malignant potential groups was 4.05 cm, and the sensitivity and specificity were 93.5% and 84.2%, respectively.

CONCLUSION

CT features, including tumor size, growth patterns, and lesion contours, were predictors of malignant potential for primary gastric GISTs.

Keywords: Computed tomography; Gastrointestinal stromal tumor; Risk stratification, Stomach

Core Tip: Gastrointestinal stromal tumors (GISTs) are rare but are nevertheless the most common mesenchymal neoplasms of the gastrointestinal tract. GISTs are most frequently found in the stomach. Preoperative prediction of the malignant potential and prognosis of these GISTs is crucial for clinical decision-making. The present study identified the computed tomography (CT) imaging characteristics for predicting the malignancy risk stratification in 147 patients with primary gastric GISTs. We demonstrated that the qualitative and quantitative features of gastric GISTs on contrast-enhanced CT may be favorable for preoperative risk stratification. This may provide a simple yet effective tool for clinicians to make appropriate clinical decisions.