Multicenter Observational Study of Multimodal AI for Upper GI Mesenchymal Tumor Diagnosis

NCT07078136 | Recruiting

• 1 other identifier: GIST-AI 2025
• Study Type: observational
• Target: 130
• Locations: 1 country
• Sites: 1

Brief Summary

This study develops a multimodal AI model using endoscopic ultrasound, white-light endoscopy, and clinical information to support the diagnosis of upper GI mesenchymal tumors and the risk stratification of gastric GISTs.

Conditions

Submucosal Tumor; Gastrointestinal Stromal Tumor (GIST); Leiomyoma; Schwannoma

Keywords

Artificial Intelligence; Endoscopic ultrasound; white-light endoscopy

Outcome Measures

Primary Outcomes (2)

• Diagnostic accuracy of a multimodal AI model for differentiating gastrointestinal stromal tumors (GISTs) from other upper gastrointestinal mesenchymal tumors

  Receiver operating characteristic (ROC) analyses, sensitivity, specificity, accuracy, positive predictive value and negative predictive value will be used to evaluate the efficacy of the model

  After the training process of the multimodal AI model is completed，on average per year

• Predictive accuracy of the multimodal AI model for risk stratification of GISTs

  ROC analyses, sensitivity, specificity, accuracy, positive predictive value and negative predictive value will be used to evaluate the efficacy of the model

  After the training process of the multimodal AI model is completed，on average per year

Secondary Outcomes (3)

• Comparison of Diagnostic Accuracy Between the Multimodal AI Model and Single-Modality Models

  After the training process of the Multimodal AI model is completed，on average per year

• Comparison of diagnostic accuracy between the multimodal AI model and experienced endoscopists for differentiating GISTs and non-GIST mesenchymal tumors

  After the testing process of the multimodal AI model is completed，on average per year

• Comparison of the predictive accuracy for GIST risk stratification between the multimodal AI model and experienced endoscopists

  After the testing process of the multimodal AI model is completed，on average per year

Study Arms (1)

All Participants

All enrolled patients with upper gastrointestinal subepithelial lesions confirmed by histopathology. Each participant will undergo standard diagnostic evaluation and independent multimodal AI prediction and expert endoscopist diagnosis.

Diagnostic Test: Multimodal AI Model; Diagnostic Test: Expert Endoscopist Assessment

Interventions

Multimodal AI Model DIAGNOSTIC_TEST

Patients' endoscopic images, EUS images, and clinical data will be analyzed by a multimodal AI model for lesion classification and GIST risk stratification.

All Participants

Expert Endoscopist Assessment DIAGNOSTIC_TEST

Endoscopic ultrasound images will be interpreted by experienced endoscopists for comparison with the AI model.

All Participants

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The cohort will be selected from several hospitals in China, including Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology.

You may qualify if:

• Age ≥ 18 years old
• Patients with an upper gastrointestinal subepithelial lesion (SEL) identified by white-light endoscopy and who have completed an endoscopic ultrasound (EUS) examination
• Patients with a histopathological diagnosis of GIST confirmed by surgical or endoscopic resection, or other SELs confirmed by surgical resection, EUS-guided sampling, or other biopsy techniques
• EUS image quality meets the following quality control standards
• Equipment requirements: Olympus EU-ME2/ME1 processor (Olympus Medical Systems Corp., Tokyo, Japan); radial EUS scope (GF-UE260/GF-UE240; Olympus, Tokyo, Japan) or linear EUS scope (GF-UCT260/GF-UCT240; Olympus, Tokyo, Japan); miniature probe (UM2R/3R; Olympus, Tokyo, Japan); Pentax ARIETTA 850 processor (Pentax, Tokyo, Japan); radial EUS scope (EG-3670URK, Pentax, Tokyo, Japan); linear EUS scope (EG-3870UT, Pentax, Tokyo, Japan); Fujifilm SU-8000 or SU-9000 processor; linear EUS scope (EG-580UT, Fujifilm, Tokyo, Japan); radial EUS scope (EG-580UR, Fujifilm, Tokyo, Japan)
• EUS images clearly showing the lesion and surrounding tissue characteristics (at least 5 images or video); must include at least one image of the maximum lesion diameter, one image showing the layer of origin, and one image demonstrating the growth pattern (intraluminal/extraluminal)
• EUS images must not contain artificial annotations, such as measurement scales, biopsy needles, Doppler signals, or elastography overlays
• Image resolution must be at least 448 × 448 pixels
• WLE (white-light endoscopy) image quality meets the following standards: images must clearly show the lesion location, mucosal features, and margins; at least one close-up and one distant view
• Complete clinical data and histopathological reports must be available

You may not qualify if:

• Age \< 18 years old
• Absolute contraindications for EUS examination, history of gastric surgery, pregnancy, severe comorbidities, or known allergy to anesthetic agents
• EUS examination terminated prematurely due to esophageal stricture, obstruction, large space-occupying lesions, rapid changes in heart rate or respiratory rate, patient intolerance, or excessive residual food
• EUS image quality does not meet the required quality control standards
• Pathological specimens do not meet diagnostic requirements: insufficient biopsy tissue (only R0 resection specimens are accepted for the GIST group), or incomplete immunohistochemical staining (missing CD117/CD34/DOG-1 expression report for the GIST group)
• Pathological results indicate that the lesion is a metastatic tumor originating from another site

Sponsors & Collaborators

• Huazhong University of Science and Technology: lead

Study Sites (1)

Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology

Wuhan, Hubei, 430030, China

RECRUITING

Related Publications (40)

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MeSH Terms

Conditions

Gastrointestinal Stromal Tumors; Leiomyoma; Neurilemmoma

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; Neoplasms, Muscle Tissue; Neuroendocrine Tumors; Neuroectodermal Tumors; Neoplasms, Germ Cell and Embryonal; Neuroma; Nerve Sheath Neoplasms; Neoplasms, Nerve Tissue

Central Study Contacts

Bin Cheng

CONTACT

+8613986097542; b.cheng@tjh.tjmu.edu.cn

Study Design

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

Study Record Dates

• First Submitted: July 2, 2025
• First Posted: July 22, 2025
• Study Start: July 28, 2025
• Primary Completion: March 1, 2026
• Study Completion (Estimated): June 1, 2026
• Last Updated: July 31, 2025

Record last verified: 2025-07

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)