Recruiting
Observational
Multicenter Observational Study of Multimodal AI for Upper GI Mesenchymal Tumor Diagnosis
Condition(s) studied
Submucosal TumorGastrointestinal Stromal Tumor (GIST)LeiomyomaSchwannoma
Investigational drug(s) / intervention(s)
Multimodal AI ModelExpert Endoscopist Assessment
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.
Study 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.
Eligibility
Inclusion Criteria:
* 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
Exclusion Criteria:
* 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
Primary outcome measure(s)
- Diagnostic accuracy of a multimodal AI model for differentiating gastrointestinal stromal tumors (GISTs) from other upper gastrointestinal mesenchymal tumors — After the training process of the multimodal AI model is completed,on average per year
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
- Predictive accuracy of the multimodal AI model for risk stratification of GISTs — After the training process of the multimodal AI model is completed,on average per year
ROC analyses, sensitivity, specificity, accuracy, positive predictive value and negative predictive value will be used to evaluate the efficacy of the model
Trial sites (1)
| Facility | City | Region | Status |
| Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology |
Wuhan |
Hubei |
Recruiting |
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