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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.
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Multimodal AI Analysis System | Diagnostic Test | The Multimodal AI System utilizes deep learning algorithms to integrate patient data from three sources: preoperative CT images (Radiomics), digitized pathology slides (Pathomics), and clinical characteristics. The model generates probability scores for: 1) Differential diagnosis of GIST vs. non-GIST, 2) Risk stratification, and 3) Genotype prediction. Note: This is an observational study. The AI model's analysis is performed in parallel to standard clinical care. The results are blinded to the treating physicians and will NOT influence the surgical plan or medical management of the participants. |
|
| Measure | Description | Time Frame |
|---|---|---|
| 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. | Up to 30 days post-surgery |
| Measure | Description | Time Frame |
|---|---|---|
| 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%) | Up to 30 days post-surgery |
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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.
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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).
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| The Fifth Affiliated Hospital of Anhui Medical University | Fuyang | Anhui | 236003 | China | ||
| Baoding Central Hospital |
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| ID | Term |
|---|---|
| D046152 | Gastrointestinal Stromal Tumors |
| ID | Term |
|---|---|
| D009372 | Neoplasms, Connective Tissue |
| D018204 | Neoplasms, Connective and Soft Tissue |
| D009370 | Neoplasms by Histologic Type |
| D009369 | Neoplasms |
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| 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. | 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. | Up to 30 days post-surgery |
| Baoding |
| Hebei |
| 071030 |
| China |
| Cangzhou People's Hospital | Cangzhou | Hebei | 061000 | China |
| Hengshui People's Hospital | Hengshui | Hebei | 053099 | China |
| Shijiazhuang People's Hospital | Shijiazhuang | Hebei | 050011 | China |
| The Second Affiliated Hospital of Xingtai Medical College | Xingtai | Hebei | 054000 | China |
| Renmin Hospital of Wuhan University | Wuhan | Hubei | 430065 | China |
| The First Affiliated Hospital of University of South China | Hengyang | Hunan | 421001 | China |
| Jinling Hospital | Nanjing | Jiangsu | 210002 | China |
| D005770 | Gastrointestinal Neoplasms |
| D004067 | Digestive System Neoplasms |
| D004066 | Digestive System Diseases |
| D005767 | Gastrointestinal Diseases |