Multicenter Observational Study of Multimodal AI for Upper GI Mesenchymal Tumor Diagnosis
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18+
SponsorHuazhong University of Science and Technology
About this trial
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 criteria
This trial does not accept healthy volunteersQualifiers
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
Disqualifiers
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
Trial population
The cohort will be selected from several hospitals in China, including Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology.
Trial design
Design model
Cohort
Time perspective
Prospective
Treatments tested in this trial
Multimodal AI Model
Diagnostic testPatients' 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
Diagnostic testEndoscopic ultrasound images will be interpreted by experienced endoscopists for comparison with the AI model.
Treatment groups
130 Participants
are divided into 1 treatment groupGroup A: All Participants2 interventions
Trial outcomes
Primary outcomes
1
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
Time frame
After the training process of the multimodal AI model is completed，on average per year2
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
Time frame
After the training process of the multimodal AI model is completed，on average per yearSecondary outcomes
1
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
Time frame
After the training process of the Multimodal AI model is completed，on average per year2
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
Time frame
After the testing process of the multimodal AI model is completed，on average per year3
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
Time frame
After the testing process of the multimodal AI model is completed，on average per yearSponsors and contacts
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Source ClinicalTrials.gov