ORIGINAL RESEARCH article
Front. Oncol.
Sec. Cancer Imaging and Image-directed Interventions
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1590432
Oral contrast-enhanced ultrasonographic features and radiomics analysis to predict NIH risk stratification for Gastrointestinal stromal tumors
Provisionally accepted
- 1Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- 2Tianjin Chest Hospital, Tianjin, Tianjin Municipality, China
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Objective: To evaluate the value of oral contrast-enhanced ultrasonography and radiomics analysis in predicting the National Institutes of Health (NIH) staging of gastrointestinal stromal tumors (GISTs).: A retrospective cohort study was conducted on 204 patients presenting with GISTs in Tianjin Medical University Cancer Institute and Hospital from January 2020 to January 2023. The clinical profiles, oral contrast-enhanced ultrasonography (CEUS), and endoscopic ultrasound (EUS) imaging data were collected. 105 patients with high-risk and moderate-risk GISTs were classified into the high-risk group, while 99 patients with low-risk and very-low-risk GISTs were classified into the low-risk group. The ITK-SNAP software and Pyradiomics (version 3.0.1) package were used to extract a comprehensive set of ultrasonographic radiomics features from the segmented regions of interest (ROIs). The patient dataset was randomly divided into a training set and a validation set at a ratio of 7:3. Leveraging the XGBoost (XGB) algorithm within the Scikit-learn (Sklearn) machine-learning library, three distinct predictive models were developed: a clinical ultrasound imaging model (US model), an ultrasonographic radiomics model (US radiomics model), and a combined model integrating both clinical, ultrasound, and radiomics features. Additionally, 51 GIST patients from Tianjin Medical University General Hospital were included in the external validation analysis.Results: 636 ultrasonic radiomics features from ROIs were successfully extracted. 6 key ultrasonic radiomics features were finally selected for subsequent model construction. In the internal validation set, the area under the curve (AUC), sensitivity, specificity, and accuracy for the US model, US radiomics model, combined model,
Keywords: Gastrointestinal Stromal Tumor, Oral contrast-enhanced ultrasonography, Radiomics, NIH risk stratification, endoscopic ultrasound (EUS)
Received: 09 Mar 2025; Accepted: 17 Jun 2025.
Copyright: © 2025 Yang, Liu, Zhang, Wang, Wei and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Mo Yang, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
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