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Development of a Multimodal AI System for GIST Management
Sponsor: Qun Zhao
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.
Official title: Development and Validation of a Multimodal Artificial Intelligence Model Integrating CT Radiomics, Pathomics, and Clinical Features for the Diagnosis, Risk Stratification, and Genotype Prediction of Gastrointestinal Stromal Tumors
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
OBSERVATIONAL
Enrollment
300
Start Date
2026-03-01
Completion Date
2026-07-01
Last Updated
2026-10-06
Healthy Volunteers
Not specified
Conditions
Interventions
Multimodal AI Analysis System
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.
Locations (9)
The Fifth Affiliated Hospital of Anhui Medical University
Fuyang, Anhui, China
Baoding Central Hospital
Baoding, Hebei, China
Cangzhou People's Hospital
Cangzhou, Hebei, China
Hengshui People's Hospital
Hengshui, Hebei, China
Shijiazhuang People's Hospital
Shijiazhuang, Hebei, China
The Second Affiliated Hospital of Xingtai Medical College
Xingtai, Hebei, China
Renmin Hospital of Wuhan University
Wuhan, Hubei, China
The First Affiliated Hospital of University of South China
Hengyang, Hunan, China
Jinling Hospital
Nanjing, Jiangsu, China