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

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.

Study Overview

• Status: Completed
• Conditions: Gastrointestinal Stromal Tumors; Gastric Subepithelial Tumors; Artificial Intelligence (AI); Gastric Leiomyoma; Multimodal Imaging
• Intervention / Treatment: Diagnostic test: Multimodal AI Analysis System

• Study Type: Observational
• Enrollment (Actual): 300

Contacts and Locations

Study Locations

• China
  • Anhui
    • Fuyang, Anhui, China, 236003
      • The Fifth Affiliated Hospital of Anhui Medical University
  • Hebei
    • Baoding, Hebei, China, 071030
      • Baoding Central Hospital
    • Cangzhou, Hebei, China, 061000
      • Cangzhou People's Hospital
    • Hengshui, Hebei, China, 053099
      • Hengshui People's Hospital
    • Shijiazhuang, Hebei, China, 050011
      • Shijiazhuang People's Hospital
    • Xingtai, Hebei, China, 054000
      • The Second Affiliated Hospital of Xingtai Medical College
  • Hubei
    • Wuhan, Hubei, China, 430065
      • Renmin Hospital of Wuhan University
  • Hunan
    • Hengyang, Hunan, China, 421001
      • The First Affiliated Hospital of University of South China
  • Jiangsu
    • Nanjing, Jiangsu, China, 210002
      • Jinling Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: N/A

• Sampling Method: Non-Probability Sample

Study Population

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).

Description

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.

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	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

Secondary Outcome Measures

Outcome Measure	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
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

Collaborators and Investigators

• Sponsor: Qun Zhao

Study record dates

Study Major Dates

• Study Start (Actual): January 1, 2024
• Primary Completion (Actual): January 1, 2026
• Study Completion (Actual): January 1, 2026

Study Registration Dates

• First Submitted: February 12, 2026
• First Submitted That Met QC Criteria: March 5, 2026
• First Posted (Actual): March 6, 2026

Study Record Updates

• Last Update Posted (Actual): April 2, 2026
• Last Update Submitted That Met QC Criteria: March 28, 2026
• Last Verified: March 1, 2026

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Neoplasms; Neoplasms by Histologic Type; Gastrointestinal Neoplasms; Digestive System Neoplasms; Digestive System Diseases; Gastrointestinal Diseases; Neoplasms, Connective and Soft Tissue; Neoplasms, Connective Tissue; Gastrointestinal Stromal Tumors
• Other Study ID Numbers: GIST-DX-RISK-GEN-01

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED