Observational Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Apr 15, 2024; 16(4): 1296-1308
Published online Apr 15, 2024. doi: 10.4251/wjgo.v16.i4.1296
Computed tomography radiogenomics: A potential tool for prediction of molecular subtypes in gastric stromal tumor
Xiao-Nan Yin, Zi-Hao Wang, Li Zou, Cai-Wei Yang, Chao-Yong Shen, Bai-Ke Liu, Yuan Yin, Xi-Jiao Liu, Bo Zhang
Xiao-Nan Yin, Zi-Hao Wang, Chao-Yong Shen, Bai-Ke Liu, Yuan Yin, Gastric Cancer Research Center, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
Li Zou, Department of Paediatric Surgery, West China Hospital of Sichuan University, Chengdu 610041, Sichuan Province, China
Cai-Wei Yang, Xi-Jiao Liu, Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
Bo Zhang, Department of Gastrointestinal Surgery, Sichuan University West China Hospital, Chengdu 610041, Sichuan Province, China
Co-first authors: Xiao-Nan Yin and Zi-Hao Wang.
Author contributions: Yin XN and Wang ZH equally contributed to this article; Yin XN, Wang ZH, Zou L, Yang CW, Shen CY, Liu BK, Yin Y, Liu XJ, and Zhang B participated in all stages of manuscript preparation, and read and approved the final version prior to submission.
Supported by the National Natural Science Foundation of China Program Grant, No. 82203108; China Postdoctoral Science Foundation, No. 2022M722275; Beijing Bethune Charitable Foundation, No. WCJZL202105; and Beijing Xisike Clinical Oncology Research Foundation, No. Y-zai2021/zd-0185.
Institutional review board statement: The study was reviewed and approved by the Research Ethics Board of West China Hospital, Sichuan University [approval No. 2022(449)].
Informed consent statement: The informed consent was waived by the Research Ethics Board of West China Hospital, Sichuan University.
Conflict-of-interest statement: The authors have declared that no competing interests exist.
Data sharing statement: All data analyzed during this study are included in this published article.
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: Xi-Jiao Liu, PhD, Doctor, Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Lane, Wuhou District, Chengdu 610041, Sichuan Province, China. bless_jiao@163.com
Received: October 8, 2023
Peer-review started: October 8, 2023
First decision: January 15, 2024
Revised: January 23, 2024
Accepted: February 25, 2024
Article in press: February 25, 2024
Published online: April 15, 2024
Processing time: 185 Days and 23.1 Hours
Abstract
BACKGROUND

Preoperative knowledge of mutational status of gastrointestinal stromal tumors (GISTs) is essential to guide the individualized precision therapy.

AIM

To develop a combined model that integrates clinical and contrast-enhanced computed tomography (CE-CT) features to predict gastric GISTs with specific genetic mutations, namely KIT exon 11 mutations or KIT exon 11 codons 557-558 deletions.

METHODS

A total of 231 GIST patients with definitive genetic phenotypes were divided into a training dataset and a validation dataset in a 7:3 ratio. The models were constructed using selected clinical features, conventional CT features, and radiomics features extracted from abdominal CE-CT images. Three models were developed: ModelCT sign, modelCT sign + rad, and model CTsign + rad + clinic. The diagnostic performance of these models was evaluated using receiver operating characteristic (ROC) curve analysis and the Delong test.

RESULTS

The ROC analyses revealed that in the training cohort, the area under the curve (AUC) values for modelCT sign, modelCT sign + rad, and modelCT sign + rad + clinic for predicting KIT exon 11 mutation were 0.743, 0.818, and 0.915, respectively. In the validation cohort, the AUC values for the same models were 0.670, 0.781, and 0.811, respectively. For predicting KIT exon 11 codons 557-558 deletions, the AUC values in the training cohort were 0.667, 0.842, and 0.720 for modelCT sign, modelCT sign + rad, and modelCT sign + rad + clinic, respectively. In the validation cohort, the AUC values for the same models were 0.610, 0.782, and 0.795, respectively. Based on the decision curve analysis, it was determined that the modelCT sign + rad + clinic had clinical significance and utility.

CONCLUSION

Our findings demonstrate that the combined modelCT sign + rad + clinic effectively distinguishes GISTs with KIT exon 11 mutation and KIT exon 11 codons 557-558 deletions. This combined model has the potential to be valuable in assessing the genotype of GISTs.

Keywords: Gastrointestinal stromal tumor; Radiomics; Gene mutation; Computed tomography; Model

Core Tip: In this study, we developed and validated a radiomics model to predict the genotypes of gastric gastrointestinal stromal tumors (GISTs) using contrast-enhanced computed tomography images. Our findings demonstrated that the radiomics model exhibited a satisfactory performance in distinguishing gastric GISTs with KIT exon 11 mutations and GISTs with KIT exon 11 codons 557-558 deletions. Among the different models evaluated, the combined modelCT sign + rad + clinic demonstrated the highest predictive accuracy. This model holds promise as an effective and noninvasive approach to guide personalized treatment decisions prior to surgery.