ArticleInternational journal of computer assisted radiology and surgery2019
Computer-aided diagnosis of gastrointestinal stromal tumors: a radiomics method on endoscopic ultrasound image.
Article in International journal of computer assisted radiology and surgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 26 citations in OpenAlex.
- Artificial intelligence-assisted endoscopic ultrasound in the diagnosis of gastrointestinal stromal tumors: a meta-analysis.Surgical endoscopy · 2023Pooled it
- Application of artificial intelligence in the diagnosis of subepithelial lesions using endoscopic ultrasonography: a systematic review and meta-analysis.Frontiers in oncology · 2022Pooled it
- Article
- An endoscopic ultrasound-based interpretable deep learning model and nomogram for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer.Scientific reports · 2025Article
- Endoscopic ultrasonography-based intratumoral and peritumoral machine learning ultrasomics model for predicting the pathological grading of pancreatic neuroendocrine tumors.BMC medical imaging · 2025Article
- Article
- Role of Artificial Intelligence in Endoscopic Intervention: A Clinical Review.Journal of community hospital internal medicine perspectives · 2024Review
- Construction and validation of an endoscopic ultrasonography-based ultrasomics nomogram for differentiating pancreatic neuroendocrine tumors from pancreatic cancer.Frontiers in oncology · 2024Article
- Endoscopic ultrasonography-based intratumoral and peritumoral machine learning radiomics analyses for distinguishing insulinomas from non-functional pancreatic neuroendocrine tumors.Frontiers in endocrinology · 2024Article
- Artificial intelligence in endoscopic ultrasonography: risk stratification of gastric gastrointestinal stromal tumors.Therapeutic advances in gastroenterology · 2023Article
- Frontiers and hotspots ofFrontiers in oncology · 2022Article
- Malignancy risk of gastrointestinal stromal tumors evaluated with noninvasive radiomics: A multi-center study.Frontiers in oncology · 2022Article
- Gastrointestinal stromal tumors diagnosis on multi-center endoscopic ultrasound images using multi-scale image normalization and transfer learning.Technology and health care : official journal of the European Society for Engineering and Medicine · 2022Article
- Current and Potential Applications of Artificial Intelligence in Gastrointestinal Stromal Tumor Imaging.Contrast media & molecular imaging · 2020Review
- Risk stratification system of gastrointestinal stromal tumors under EUS elastography based on artificial intelligence.Endoscopic ultrasoundArticle
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
purposeThe purpose of our study is to propose a preoperative computer-aided diagnosis system based on a radiomics method to differentiate gastrointestinal stromal tumors (GISTs) of the higher-risk group (HRG) from those of the lower-risk group (LRG) on endoscopic ultrasound (EUS) images. MATERIALS AND
methodGastro-EUS (G-EUS) images of four different risk level GISTs were collected from 19 hospitals. The datasheet included 168 case HRG GISTs and 747 case LRG GISTs. A radiomics method with image segmentation, feature extraction, feature selection and classification was developed. Here 439 radiomics features were firstly extracted, and then, the least absolute shrinkage selection operator (lasso) model with a tenfold cross-validation and 31 bootstraps was used to reduce the dimension of feature sets. Finally, random forest was applied to establish the classification model.
resultsThe proposed model differentiated 32 case HRG GISTs from 149 case LRG GISTs. Result for the testing set achieved the area under the receiver operating characteristic curve of 0.839, the accuracy of 0.823, the sensitivity of 0.813 and the specificity of 0.826.
conclusionThe model could increase preoperative diagnostic accuracy and provide a valuable reference for the doctors.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.