ArticleJournal of digital imaging2022
Differential Diagnosis and Molecular Stratification of Gastrointestinal Stromal Tumors on CT Images Using a Radiomics Approach.
Article in Journal of digital imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 31 citations in OpenAlex.
- Prediction of recurrence-free survival and adjuvant therapy benefit in patients with gastrointestinal stromal tumors based on radiomics features.La Radiologia medica · 2022Trial
- Integrating CT radiomics and morphologic features for preoperative risk stratification of gastrointestinal stromal tumours.Scientific reports · 2026Article
- [Current status and challenges of artificial intelligence and organoid technologies in precision diagnosis and treatment of gastrointestinal stromal tumors].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2026Review
- Multimodal models based on radiomics and deep learning in the classification of gastric stromal tumors and gastric leiomyomas.Surgical endoscopy · 2026Article
- A CT-based radiomics model for preoperative risk stratification of gastrointestinal stromal tumors.Frontiers in oncology · 2026Article
- Uncovering the associative mediation pathway from EUS features to malignancy through pathological mediators in gastrointestinal stromal tumor.Frontiers in medicine · 2026Article
- Minimally interactive segmentation of soft-tissue tumors on CT and MRI using deep learning.European radiology · 2025Article
- Advancement in medical treatment for gastrointestinal stromal tumors (GISTs): a ray of hope.Annals of medicine and surgery (2012) · 2025Review
- Radiomics-based predictive model for preoperative risk classification of gastrointestinal stromal tumors using multiparametric magnetic resonance imaging: a retrospective study.Radiologie (Heidelberg, Germany) · 2024Article
- Deep Learning Radiomics Analysis of CT Imaging for Differentiating Between Crohn's Disease and Intestinal Tuberculosis.Journal of imaging informatics in medicine · 2024Article
- Computed tomography radiogenomics: A potential tool for prediction of molecular subtypes in gastric stromal tumor.World journal of gastrointestinal oncology · 2024Article
- CT and MRI of abdominal cancers: current trends and perspectives in the era of radiomics and artificial intelligence.Japanese journal of radiology · 2024Review
- Computed tomography radiomics models of tumor differentiation in canine small intestinal tumors.Frontiers in veterinary science · 2024Article
- A mutation-based radiomics signature predicts response to imatinib in Gastrointestinal Stromal Tumors (GIST).European journal of radiology open · 2023Article
- Radiomics in gastrointestinal stromal tumours: an up-to-date review.Japanese journal of radiology · 2023Review
- Role of Machine Learning in Precision Oncology: Applications in Gastrointestinal Cancers.Cancers · 2022Review
- Early Prediction and Monitoring of Treatment Response in Gastrointestinal Stromal Tumors by Means of Imaging: A Systematic Review.Diagnostics (Basel, Switzerland) · 2022Review
- Optimization of Preoperative Lymph Node Staging in Patients with Muscle-Invasive Bladder Cancer Using Radiomics on Computed Tomography.Journal of personalized medicine · 2022Article
- TheJournal of personalized medicine · 2021Article
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Authors and funding
13 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Treatment planning of gastrointestinal stromal tumors (GISTs) includes distinguishing GISTs from other intra-abdominal tumors and GISTs' molecular analysis. The aim of this study was to evaluate radiomics for distinguishing GISTs from other intra-abdominal tumors, and in GISTs, predict the c-KIT, PDGFRA, BRAF mutational status, and mitotic index (MI). Patients diagnosed at the Erasmus MC between 2004 and 2017, with GIST or non-GIST intra-abdominal tumors and a contrast-enhanced venous-phase CT, were retrospectively included. Tumors were segmented, from which 564 image features were extracted. Prediction models were constructed using a combination of machine learning approaches. The evaluation was performed in a 100 × random-split cross-validation. Model performance was compared to that of three radiologists. One hundred twenty-five GISTs and 122 non-GISTs were included. The GIST vs. non-GIST radiomics model had a mean area under the curve (AUC) of 0.77. Three radiologists had an AUC of 0.69, 0.76, and 0.84, respectively. The radiomics model had an AUC of 0.52 for c-KIT, 0.56 for c-KIT exon 11, and 0.52 for the MI. The numbers of PDGFRA, BRAF, and other c-KIT mutations were too low for analysis. Our radiomics model was able to distinguish GISTs from non-GISTs with a performance similar to three radiologists, but less observer dependent. Therefore, it may aid in the early diagnosis of GIST, facilitating rapid referral to specialized treatment centers. As the model was not able to predict any genetic or molecular features, it cannot aid in treatment planning yet.
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