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Clinical Trial NCT07078136 for Submucosal Tumor, Gastrointestinal Stromal Tumor (GIST), Leiomyoma, Schwannoma is recruiting. See the Trial Radar Card View and AI discovery tools for all the details. Or ask anything here.
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Multicenter Observational Study of Multimodal AI for Upper GI Mesenchymal Tumor Diagnosis 130 Observational

Recruiting
Clinical Trial NCT07078136 is an observational study for Submucosal Tumor, Gastrointestinal Stromal Tumor (GIST), Leiomyoma, Schwannoma that is recruiting. It started on July 28, 2025 with plans to enroll 130 participants. Led by Huazhong University of Science and Technology, it is expected to complete by June 1, 2026. The latest data from ClinicalTrials.gov was last updated on July 31, 2025.
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.
Detailed Description
This is a multicenter, observational study designed to evaluate the diagnostic performance of a multimodal artificial intelligence (AI) model for the classification of upper gastrointestinal subepithelial lesions (SELs) and risk stratification of gastric gastrointestinal stromal tumors (gGISTs). The study combines retrospective image data for training and validation with prospectively recruited cases for testing.

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Official Title

Multicenter Observational Study of a Multimodal AI Model Using EUS, White-Light Endoscopy, and Clinical Data for Diagnosis of Upper GI Mesenchymal Tumors and Risk Stratification of Gastric GISTs

Conditions
Submucosal TumorGastrointestinal Stromal Tumor (GIST)LeiomyomaSchwannoma
Publications
Scientific articles and research papers published about this clinical trial:
  • Chinese Society of Digestive Endoscopy Tunnel Technology Collaboration Group, Endoscopist Branch of Chinese Medical Doctor Association, and Digestive Endoscopy Branch of Beijing Medical Association. Expert Consensus on Endoscopic Diagnosis and Treatment of Gastrointestinal Stromal Tumors in China (2020, Beijing). Chinese Journal of Digestive Endoscopy, 2021(07): 505-514.
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Other Study IDs
  • GIST-AI 2025
NCT ID Number
NCT07078136
Start Date (Actual)
2025-07-28
Last Update Posted
2025-07-31
Completion Date (Estimated)
2026-06
Enrollment (Estimated)
130
Study Type
Observational
Status
Recruiting
Keywords
Artificial Intelligence
Endoscopic ultrasound
white-light endoscopy
Arms / Interventions
Participant Group/ArmIntervention/Treatment
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.
Multimodal AI Model
Patients' endoscopic images, EUS images, and clinical data will be analyzed by a multimodal AI model for lesion classification and GIST risk stratification.
Expert Endoscopist Assessment
Endoscopic ultrasound images will be interpreted by experienced endoscopists for comparison with the AI model.
Primary Outcome Measures
Outcome MeasureMeasure DescriptionTime Frame
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 Outcome Measures
Outcome MeasureMeasure DescriptionTime Frame
Comparison of Diagnostic Accuracy Between the Multimodal AI Model and Single-Modality Models
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
Comparison of diagnostic accuracy between the multimodal AI model and experienced endoscopists for differentiating GISTs and non-GIST mesenchymal tumors
The diagnostic accuracy of the AI model and expert endoscopists will be compared within the same participants, using histopathology as the gold standard. 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 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
The diagnostic accuracy of the AI model and expert endoscopists will be compared within the same participants, using histopathology as the gold standard. 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 testing process of the multimodal AI model is completed，on average per year
Participation Assistant
Eligibility Criteria

Eligible Ages
Adult, Older Adult
Minimum Age
18 Years
Eligible Sexes
All

  • 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

    1. 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)
    2. 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)
    3. EUS images must not contain artificial annotations, such as measurement scales, biopsy needles, Doppler signals, or elastography overlays
    4. 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

  • 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
Huazhong University of Science and Technology logoHuazhong University of Science and Technology361 active studies to explore
Study Responsible Party
Bin Cheng, Principal Investigator, Professor, Huazhong University of Science and Technology
Study Central Contact
Contact: Bin Cheng, +8613986097542, [email protected]
1 Study Locations in 1 Countries

Hubei

Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
Bin Cheng, Contact, 13986097542, [email protected]
Recruiting