AIMC Topic: Gastrointestinal Stromal Tumors
Clear Filters Showing 1 to 10 of 33 articles
An artificial intelligence model utilizing endoscopic ultrasonography for differentiating small and micro gastric stromal tumors from gastric leiomyomas.
BMC gastroenterology
Apr 9, 2025
BACKGROUND: Gastric stromal tumors (GSTs) and gastric leiomyomas (GLs) represent the primary subtypes of gastric submucosal tumors (SMTs) characterized by distinct biological characteristics and treatment modalities. The accurate differentiation betw...
Next questions on gastrointestinal stromal tumors: unresolved challenges and future directions.
Current opinion in oncology
Apr 4, 2025
PURPOSE OF REVIEW: Despite remarkable progress in the management of gastrointestinal stromal tumors (GISTs), critical challenges persist. Key aspects such as risk stratification, the optimal duration of adjuvant therapy, and strategies to enhance the...
A deep-learning model for predicting tyrosine kinase inhibitor response from histology in gastrointestinal stromal tumor.
The Journal of pathology
Feb 14, 2025
Over 90% of gastrointestinal stromal tumors (GISTs) harbor mutations in KIT or PDGFRA that can predict response to tyrosine kinase inhibitor (TKI) therapies, as recommended by NCCN (National Comprehensive Cancer Network) guidelines. However, gene seq...
Receptor, Platelet-Derived Growth Factor alpha
Gastrointestinal Neoplasms
Protein Kinase Inhibitors
Humans
Middle Aged
Gastrointestinal Stromal Tumors
Female
Deep Learning
Imatinib Mesylate
Aged
Pyrroles
Triazines
Treatment Outcome
Pyrazoles
Male
Mutation
Proto-Oncogene Proteins c-kit
Tyrosine Kinase Inhibitors
Interpretable machine learning model based on CT semantic features and radiomics features to preoperatively predict Ki-67 expression in gastrointestinal stromal tumors.
Scientific reports
Nov 26, 2024
To develop and validate a machine learning (ML) model which combined computed tomography (CT) semantic and radiomics features to preoperatively predict Ki-67 expression in gastrointestinal stromal tumors (GISTs) patients. We retrospectively collected...
Development and validation of a machine-learning model for preoperative risk of gastric gastrointestinal stromal tumors.
Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract
Oct 22, 2024
BACKGROUND: Gastrointestinal stromal tumors (GISTs) have malignant potential, and treatment varies according to risk. However, no specific protocols exist for preoperative assessment of the malignant potential of gastric GISTs (gGISTs). This study ai...
Imatinib adherence prediction using machine learning approach in patients with gastrointestinal stromal tumor.
Cancer
Sep 6, 2024
BACKGROUND: Nonadherence to imatinib is common in patients with gastrointestinal stromal tumor (GIST), which is associated with poor prognosis and financial burden. The primary aim of this study was to investigate the adherence rate in patients with ...
Interpretable artificial intelligence to optimise use of imatinib after resection in patients with localised gastrointestinal stromal tumours: an observational cohort study.
The Lancet. Oncology
Jul 5, 2024
BACKGROUND: Current guidelines recommend use of adjuvant imatinib therapy for many patients with gastrointestinal stromal tumours (GISTs); however, its optimal treatment duration is unknown and some patient groups do not benefit from the therapy. We ...
Deep learning analysis for differential diagnosis and risk classification of gastrointestinal tumors.
Scandinavian journal of gastroenterology
Jul 1, 2024
OBJECTIVES: Recently, artificial intelligence (AI) has been applied to clinical diagnosis. Although AI has already been developed for gastrointestinal (GI) tract endoscopy, few studies have applied AI to endoscopic ultrasound (EUS) images. In this st...
Differentiating Gastrointestinal Stromal Tumors From Leiomyomas of Upper Digestive Tract Using Convolutional Neural Network Model by Endoscopic Ultrasonography.
Journal of clinical gastroenterology
Jul 1, 2024
BACKGROUND: Gastrointestinal stromal tumors (GISTs) and leiomyomas are the most common submucosal tumors of the upper digestive tract, and the diagnosis of the tumors is essential for their treatment and prognosis. However, the ability of endoscopic ...
Preoperative CT-based radiomics and deep learning model for predicting risk stratification of gastric gastrointestinal stromal tumors.
Medical physics
Jun 27, 2024
BACKGROUND: Gastrointestinal stromal tumors (GISTs) are clinically heterogeneous with various malignant potential in different individuals. It is crucial to explore a reliable method for preoperative risk stratification of gastric GISTs noninvasively...
Stay Ahead of Medical AI
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.
Subscribe NowPopular Topics
- Machine Learning 30978
- Neural Networks, Computer 29696
- Algorithms 26813
- Deep Learning 26324
- Artificial Intelligence 24267
- Robotics 13307
- Image Processing, Computer-Assisted 9512
- Retrospective Studies 9097
- Magnetic Resonance Imaging 6173
- Reproducibility of Results 5589
- Tomography, X-Ray Computed 4765
- Support Vector Machine 4678
- Computational Biology 4229
- Brain 4045
- Computer Simulation 3877
Recent Journals
- Scientific reports 5865
- Sensors (Basel, Switzerland) 4794
- PloS one 4011
- Neural networks : the official journal of the International Neural Network Society 2928
- Computational intelligence and neuroscience 1931
- Computers in biology and medicine 1839
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 1686
- Studies in health technology and informatics 1278
- IEEE journal of biomedical and health informatics 1129
- Journal of chemical information and modeling 994
- Nature communications 938
- Computer methods and programs in biomedicine 883
- Bioinformatics (Oxford, England) 861
- Briefings in bioinformatics 850
- IEEE transactions on neural networks and learning systems 823