An observational study in Gastrointestinal Stromal Tumors, Gastric Subepithelial Tumors and Gastric Leiomyoma, sponsored by Qun Zhao. Completed at 9 sites in China. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-10-06.
Sponsored by Qun Zhao · Observational
Background: Gastrointestinal Stromal Tumors (GISTs) are the most common mesenchymal tumors of the gastrointestinal tract. Accurate pre-operative diagnosis, risk stratification, and genotyping are critical for determining the appropriate surgical approach and targeted therapy (such as Imatinib). However, current methods often rely on invasive postoperative pathology and expensive genetic testing.
Study Objective: The purpose of this study is to develop and validate a multimodal Artificial Intelligence (AI) model that integrates clinical data, CT radiomics (imaging features), and pathomics (digital pathology features) to improve the precision of GIST management.
Study Design: This is a prospective, observational study. The researchers will recruit patients with suspected gastric submucosal tumors who are scheduled for surgery or biopsy at The Fourth Hospital of Hebei Medical University.
Core Tasks: The AI model will be trained to perform three specific tasks:
Diagnosis: Distinguish GISTs from other non-GIST mesenchymal tumors (e.g., leiomyomas, schwannomas).
Risk Assessment: Stratify GISTs into risk categories (e.g., Low vs. High risk) to predict malignant potential.
Genotyping: Predict specific gene mutations (e.g., KIT or PDGFRA mutations) to guide immunotherapy or targeted therapy.
Methodology: Patient data (CT scans, pathology slides, and clinical history) will be collected and analyzed by the AI system. The AI's predictions will be compared against the "Gold Standard" results derived from postoperative pathological examination and Next-Generation Sequencing (NGS). This study is non-interventional; the AI results will not affect the standard of care received by the patients.
333 studies on the registry are indexed under Gastrointestinal Stromal Tumors; 61 are open to participants now.
This study's enrollment of 300 is above the median of 116 across 68 observational studies indexed under Gastrointestinal Stromal Tumors.
Browse Gastrointestinal Stromal Tumors studies →Qun Zhao is the lead sponsor of 15 studies on the registry; 9 are open to participants now.
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Patients presenting with gastric submucosal tumors (SMTs) who are admitted to the Department of Gastrointestinal Surgery at The Fourth Hospital of Hebei Medical University for surgical or endoscopic treatment. The cohort includes patients with subsequently pathologically confirmed GISTs and other mesenchymal tumors (e.g., leiomyoma, schwannoma).
Age ≥ 18 years, gender not limited.
Clinical diagnosis of gastric submucosal tumor (SMT) or suspected gastrointestinal stromal tumor (GIST) based on gastroscopy or ultrasound.
Scheduled for surgical resection or endoscopic biopsy at the study center.
Standard preoperative contrast-enhanced CT scans are available (performed within 2 weeks prior to surgery).
Patients or their legal guardians have signed the informed consent form.
Exclusion Criteria:
Received neoadjuvant therapy (e.g., Imatinib, chemotherapy, or radiotherapy) prior to surgery/biopsy.
Poor quality of CT images (e.g., severe motion artifacts) affecting radiomics analysis.
Insufficient tissue samples for pathological diagnosis or genetic testing.
Confirmed diagnosis of other primary malignancies.
Incomplete clinical data or lost to follow-up immediately after surgery.
CT-based multitask deep learning system (GIST-Net). Input is the routine preoperative contrast-enhanced CT only; no pathology, molecular or laboratory data are used at inference, and no extra imaging, radiation, blood sampling or biopsy is required. The tumour is segmented on the portal venous phase, and four task-specific heads output probabilities for: (1) GIST vs non-GIST submucosal lesions; (2) modified NIH risk category; (3) driver genotype (KIT exon 11/9, PDGFRA non-D842V, D842V, wild-type); (4) recurrence within 24 months after R0 resection. Steps 2-4 are conditioned on step 1. A prespecified reader component evaluates human-AI interaction: 10 radiologists of three experience levels read the same cases unaided, then re-read with model scores and attention maps after a 4-week washout, giving paired within-reader comparisons of AUC, accuracy, agreement, confidence and reading time. Observational only; outputs are blinded to treating physicians and do not affect management.
Also known as: GIST-RadPath-AI Model
Diagnostic Accuracy of the AI Model for Distinguishing GIST from Non-GIST Tumors
The diagnostic accuracy is calculated as the proportion of correctly classified patients (GIST vs. Non-GIST) by the multimodal AI model, compared to the gold standard postoperative pathological diagnosis.
Time frame: Up to 30 days post-surgery
Concordance Rate between AI-predicted Risk Grade and Pathological Modified NIH Criteria
The proportion of patients whose risk category (Very Low/Low vs. Intermediate/High) predicted by the AI model matches the actual risk grade determined by postoperative pathology according to the modified National Institutes of Health (NIH) criteria. This will be reported as a percentage (0-100%)
Time frame: Up to 30 days post-surgery
Sensitivity and Specificity of the AI Model in Predicting KIT/PDGFRA Gene Mutations
The AI model's performance in identifying specific mutations (e.g., KIT exon 11, PDGFRA) compared to the results of Next-Generation Sequencing (NGS). Data will be reported as percentages with 95% confidence intervals.
Time frame: Up to 30 days post-surgery
Area Under the Receiver Operating Characteristic Curve (AUC) for All Tasks
The AUC values will be calculated to evaluate the overall performance of the AI model in diagnosis, risk stratification, and genotype prediction. Sensitivity and Specificity will also be reported.
Time frame: Up to 30 days post-surgery
Plan to share: Undecided
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This study is completed, as verified in Mar 2026. You cannot join it, but the record below documents what was studied.
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