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Development of a Multimodal AI System for GIST Management

NCT07454967 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 300

Last updated 2026-04-02

No results posted yet for this study

Summary

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

Interventions

DIAGNOSTIC_TEST

Multimodal AI Analysis System

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.

Sponsors & Collaborators

  • Qun Zhao

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-01-01
Primary Completion
2026-01-01
Completion
2026-01-01

Countries

  • China

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07454967 on ClinicalTrials.gov