Retrospective Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Dec 15, 2024; 16(12): 4663-4674
Published online Dec 15, 2024. doi: 10.4251/wjgo.v16.i12.4663
Deep learning model combined with computed tomography features to preoperatively predicting the risk stratification of gastrointestinal stromal tumors
Yi Li, Yan-Bei Liu, Xu-Bin Li, Xiao-Nan Cui, Dong-Hua Meng, Cong-Cong Yuan, Zhao-Xiang Ye
Yi Li, Xu-Bin Li, Xiao-Nan Cui, Dong-Hua Meng, Zhao-Xiang Ye, Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, Tianjin Key Laboratory of Digestive Cancer, Tianjin 300060, China
Yan-Bei Liu, School of Life Sciences, Tiangong University, Tianjin 300387, China
Cong-Cong Yuan, Department of Radiology, Tianjin First Central Hospital, Tianjin 300190, China
Co-corresponding authors: Zhao-Xiang Ye and Xu-Bin Li.
Author contributions: Li Y completed the research design and data analysis, and was a major contributor in writing the manuscript; Liu YB completed data analysis and model establishment; Cui XN, Yuan CC, and Meng DH participated in data collection and preliminary reports; Li XB and Ye ZX provided guidance on the paper and objectively reviewed it; Li Y and Cui XN were responsible for the processing of inspection analysis and statistical data to ensure the quality of the data. All authors read and approved the final manuscript.
Supported by The Chinese National Key Research and Development Project, No. 2021YFC2500400 and No. 2021YFC2500402; and Tianjin Key Medical Discipline (Specialty) Construction Project, No. TJYXZDXK-009A.
Institutional review board statement: The study was reviewed and approved by Tianjin Medical University Cancer Institute and Hospital (EK20240015).
Informed consent statement: All study participants, or their legal guardian, provided informed written consent prior to study enrollment.
Conflict-of-interest statement: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Data sharing statement: Technical appendix, statistical code, and dataset available from the corresponding author at yezhaoxiang@163.com.
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: Zhao-Xiang Ye, PhD, Chief, Chief Doctor, Professor, Department of radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, Tianjin Key Laboratory of Digestive Cancer, Huanhuxi Road, Hexi District, Tianjin 300060, China. yezhaoxiang@163.com
Received: June 25, 2024
Revised: October 2, 2024
Accepted: October 22, 2024
Published online: December 15, 2024
Processing time: 140 Days and 8.7 Hours
Abstract
BACKGROUND

Gastrointestinal stromal tumors (GIST) are prevalent neoplasm originating from the gastrointestinal mesenchyme. Approximately 50% of GIST patients experience tumor recurrence within 5 years. Thus, there is a pressing need to accurately evaluate risk stratification preoperatively.

AIM

To assess the application of a deep learning model (DLM) combined with computed tomography features for predicting risk stratification of GISTs.

METHODS

Preoperative contrast-enhanced computed tomography (CECT) images of 551 GIST patients were retrospectively analyzed. All image features were independently analyzed by two radiologists. Quantitative parameters were statistically analyzed to identify significant predictors of high-risk malignancy. Patients were randomly assigned to the training (n = 386) and validation cohorts (n = 165). A DLM and a combined DLM were established for predicting the GIST risk stratification using convolutional neural network and subsequently evaluated in the validation cohort.

RESULTS

Among the analyzed CECT image features, tumor size, ulceration, and enlarged feeding vessels were identified as significant risk predictors (P < 0.05). In DLM, the overall area under the receiver operating characteristic curve (AUROC) was 0.88, with the accuracy (ACC) and AUROCs for each stratification being 87% and 0.96 for low-risk, 79% and 0.74 for intermediate-risk, and 84% and 0.90 for high-risk, respectively. The overall ACC and AUROC were 84% and 0.94 in the combined model. The ACC and AUROCs for each risk stratification were 92% and 0.97 for low-risk, 87% and 0.83 for intermediate-risk, and 90% and 0.96 for high-risk, respectively. Differences in AUROCs for each risk stratification between the two models were significant (P < 0.05).

CONCLUSION

A combined DLM with satisfactory performance for preoperatively predicting GIST stratifications was developed using routine computed tomography data, demonstrating superiority compared to DLM.

Keywords: Gastrointestinal stromal tumors; Deep learning; Risk stratification; Tomography, X-ray computed; Prognosis

Core Tip: The deep learning model (DLM) was validated to accurately predict the risk classification of gastrointestinal stromal tumors. The combined DLM outperformed DLM in predicting risk stratification. The combined model has potential to guide and facilitate clinical decision-making.