ArticleFrontiers in oncology2022
Ultrasound radiomics model-based nomogram for predicting the risk Stratification of gastrointestinal stromal tumors.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Preoperatively Predicting Risk Stratification for GISTs ≤2 cm by Radiomics Model: A Dual-center StudyCurrent medical imaging · 2026Article
- A deep learning and radiomics fusion model enhances endoscopic ultrasonography diagnosis of gastric tumors.Frontiers in oncology · 2026Article
- Development and validation of an ultrasound-based AI-radiomics model for diagnosing and risk-stratifying gastrointestional stromal tumors: a retrospective diagnostic study.BMC medical imaging · 2025Article
- Preclinical Application of Computer-Aided High-Frequency Ultrasound (HFUS) Imaging: A Preliminary Report on the In Vivo Characterization of Hepatic Steatosis Progression in Mouse Models.Journal of imaging · 2025Article
- Predicting the risk stratification of gastrointestinal stromal tumors using machine learning-based ultrasound radiomics.Journal of medical ultrasonics (2001) · 2024Article
- An Ultrasound-based Machine Learning Model for Predicting Tumor-Infiltrating Lymphocytes in Breast Cancer.Technology in cancer research & treatmentArticle
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Abstract
This study aimed to develop and evaluate a nomogram based on an ultrasound radiomics model to predict the risk grade of gastrointestinal stromal tumors (GISTs). 216 GIST patients pathologically diagnosed between December 2016 and December 2021 were reviewed and divided into a training cohort (n = 163) and a validation cohort (n = 53) in a ratio of 3:1. The tumor region of interest was depicted on each patient's ultrasound image using ITK-SNAP, and the radiomics features were extracted. By filtering unstable features and using Spearman's correlation analysis, and the least absolute shrinkage and selection operator algorithm, a radiomics score was derived to predict the malignant potential of GISTs. a radiomics nomogram that combines the radiomics score and clinical ultrasound predictors was constructed and assessed in terms of calibration, discrimination, and clinical usefulness. The radiomics score from ultrasound images was significantly associated with the malignant potential of GISTs. The radiomics nomogram was superior to the clinical ultrasound nomogram and the radiomics score, and it achieved an AUC of 0.90 in the validation cohort. Based on the decision curve analysis, the radiomics nomogram was found to be more clinically significant and useful. A nomogram consisting of radiomics score and the maximum tumor diameter demonstrated the highest accuracy in the prediction of risk grade in GISTs. The outcomes of our study provide vital insights for important preoperative clinical decisions.
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