Development of a Multimodal AI System for GIST Management

NCT07454967 | Completed

• 1 other identifier: GIST-DX-RISK-GEN-01
• Study Type: observational
• Target: 300
• Locations: 1 country
• Sites: 9

Brief 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

Gastrointestinal Stromal Tumors; Gastric Leiomyoma; Artificial Intelligence (AI); Gastric Subepithelial Tumors; Multimodal Imaging

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (3)

• Concordance Rate between AI-predicted Risk Grade and Pathological Modified NIH Criteria

  Up to 30 days post-surgery

• Sensitivity and Specificity of the AI Model in Predicting KIT/PDGFRA Gene Mutations

  Up to 30 days post-surgery

• Area Under the Receiver Operating Characteristic Curve (AUC) for All Tasks

  Up to 30 days post-surgery

Interventions

Multimodal AI Analysis System DIAGNOSTIC_TEST

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

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• 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).

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Qun Zhao: lead

Study Sites (9)

The Fifth Affiliated Hospital of Anhui Medical University

Fuyang, Anhui, 236003, China

Location

Baoding Central Hospital

Baoding, Hebei, 071030, China

Location

Cangzhou People's Hospital

Cangzhou, Hebei, 061000, China

Location

Hengshui People's Hospital

Hengshui, Hebei, 053099, China

Location

Shijiazhuang People's Hospital

Shijiazhuang, Hebei, 050011, China

Location

The Second Affiliated Hospital of Xingtai Medical College

Xingtai, Hebei, 054000, China

Location

Renmin Hospital of Wuhan University

Wuhan, Hubei, 430065, China

Location

The First Affiliated Hospital of University of South China

Hengyang, Hunan, 421001, China

Location

Jinling Hospital

Nanjing, Jiangsu, 210002, China

Location

MeSH Terms

Conditions

Gastrointestinal Stromal Tumors

Condition Hierarchy (Ancestors)

Neoplasms, Connective Tissue; Neoplasms, Connective and Soft Tissue; Neoplasms by Histologic Type; Neoplasms; Gastrointestinal Neoplasms; Digestive System Neoplasms; Digestive System Diseases; Gastrointestinal Diseases

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: February 12, 2026
• First Posted: March 6, 2026
• Study Start: March 1, 2026
• Primary Completion: July 1, 2026
• Study Completion: July 1, 2026
• Last Updated: July 29, 2026

Record last verified: 2026-03

Locations

CN (9)