ArticleJournal of personalized medicine2023
Development and Validation of Artificial-Intelligence-Based Radiomics Model Using Computed Tomography Features for Preoperative Risk Stratification of Gastrointestinal Stromal Tumors.
Article in Journal of personalized medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Radiomics and artificial intelligence-based prediction of tumor response in digestive system neoplasm: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- GPT-4o-Powered Preanesthetic AI: Development and Validation Study.JMIR medical informatics · 2026Article
- Nationwide Multicenter Study of Advanced Endoscopic Resection and Malignant Risk Model for Gastric Myogenic Tumors (GASTRO Trial).Life (Basel, Switzerland) · 2026Article
- Beyond Laparotomy: Multimodal Strategies and Emerging Evidence in Minimally Invasive Management of Gastric Gastrointestinal Stromal Tumors.BioMed research international · 2026Review
- Intratumoral and Peritumoral Radiomics based on Contrast-enhanced CT Images: Biomarkers to Predict the Risk-Grade of Gastrointestinal Stromal TumorsCurrent medical imaging · 2026Article
- A CT-based radiomics model for preoperative risk stratification of gastrointestinal stromal tumors.Frontiers in oncology · 2026Article
- Radiomics models with baseline MRI and clinical data to predict target therapy response and high-risk mortality in metastatic GIST.Scientific reports · 2025Article
- The Many Faces of Intestinal Tumors in Adults, Including the Primary Role of CT Imaging in Emergencies and the Important Role of Cross-Sectional Imaging: A Pictorial Review.Healthcare (Basel, Switzerland) · 2025Review
- Prediction of High-Risk Gastrointestinal Stromal Tumor Recurrence Based on Delta-CT Radiomics Modeling: A 3-Year Follow-up Study After Surgery.Clinical Medicine Insights. Oncology · 2024Article
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundpreoperative risk assessment of gastrointestinal stromal tumors (GISTS) is required for optimal and personalized treatment planning. Radiomics features are promising tools to predict risk assessment. The purpose of this study is to develop and validate an artificial intelligence classification algorithm, based on CT features, to define GIST's prognosis as determined by the Miettinen classification.
methodspatients with histological diagnosis of GIST and CT studies were retrospectively enrolled. Eight morphologic and 30 texture CT features were extracted from each tumor and combined to obtain three models (morphologic, texture and combined). Data were analyzed using a machine learning classification (WEKA). For each classification process, sensitivity, specificity, accuracy and area under the curve were evaluated. Inter- and intra-reader agreement were also calculated.
results52 patients were evaluated. In the validation population, highest performances were obtained by the combined model (SE 85.7%, SP 90.9%, ACC 88.8%, and AUC 0.954) followed by the morphologic (SE 66.6%, SP 81.8%, ACC 76.4%, and AUC 0.742) and texture (SE 50%, SP 72.7%, ACC 64.7%, and AUC 0.613) models. Reproducibility was high of all manual evaluations.
conclusionsthe AI-based radiomics model using a CT feature demonstrates good predictive performance for preoperative risk stratification of GISTs.
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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.