AIMC Topic: Gastrointestinal Stromal Tumors

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Subtype classification of gastric spindle cell tumors in whole slide images.

Computers in biology and medicine
AIMS: Accurate cancer subtype classification is critical due to variations in tumor progression and prognosis. Traditionally, pathologists classified subtypes manually by examining pathological slides under the microscope. To address increasing workl...

Hierarchical attention fusion of EUS-doppler features for GISTs risk assessment.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Assessing the preoperative malignancy risk of gastrointestinal stromal tumors (GISTs) is crucial for determining the appropriate treatment plan and prognosis. The current automated diagnosis of GISTs based on endoscopic ultrasound (EUS) pose challeng...

Diagnostic accuracy of radiomics in risk stratification of gastrointestinal stromal tumors: A systematic review and meta-analysis.

European journal of radiology
RATIONALE AND OBJECTIVES: This systematic review and meta-analysis aimed to assess the diagnostic accuracy of radiomics in risk stratification of gastrointestinal stromal tumors (GISTs). It focused on evaluating radiomic models as a non-invasive tool...

An artificial intelligence model utilizing endoscopic ultrasonography for differentiating small and micro gastric stromal tumors from gastric leiomyomas.

BMC gastroenterology
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
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
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...

Interpretable machine learning model based on CT semantic features and radiomics features to preoperatively predict Ki-67 expression in gastrointestinal stromal tumors.

Scientific reports
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
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
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
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 ...