Clinical trial · Observational
Development of a Multimodal AI System for GIST Management
Development and Validation of a Multimodal Artificial Intelligence Model Integrating CT Radiomics, Pathomics, and Clinical Features for the Diagnosis, Risk Stratification, and Genotype Prediction of Gastrointestinal Stromal Tumors
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (5)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Artificial Intelligence (AI) | — | UNRESOLVED | — |
| Gastric Leiomyoma | Gastric Leiomyoma | ONTOLOGY_EXACT | 0.98 |
| Gastric Subepithelial Tumors | — | UNRESOLVED | — |
| Gastrointestinal Stromal Tumors | Gastrointestinal Stromal Tumor | ONTOLOGY_EXACT | 0.98 |
| Multimodal Imaging | — | UNRESOLVED | — |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Multimodal AI Analysis System | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (1)
- measure
- Diagnostic Accuracy of the AI Model for Distinguishing GIST from Non-GIST Tumors
- timeFrame
- Up to 30 days post-surgery
- description
- 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.
Secondary outcomes (3)
- measure
- Concordance Rate between AI-predicted Risk Grade and Pathological Modified NIH Criteria
- timeFrame
- Up to 30 days post-surgery
- description
- 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%)
- measure
- Sensitivity and Specificity of the AI Model in Predicting KIT/PDGFRA Gene Mutations
- timeFrame
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: 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.
References
Publications (0)
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