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AI-Based DeepGEM Tool for Predicting Gene Mutations in NSCLC Patients: A Randomized Controlled Study
Sponsor: Jianxing He
Summary
This prospective, multicenter, randomized controlled trial aims to evaluate the clinical utility of DeepGEM, an artificial intelligence (AI)-based mutation prediction tool based on histopathological whole-slide images, in patients with non-small cell lung cancer (NSCLC). The study will assess whether DeepGEM can facilitate molecular testing, increase targeted therapy utilization, and improve survival outcomes in a real-world clinical setting. Patients with stage II-IV treatment-naïve NSCLC and qualified pathology slides for DeepGEM analysis will be enrolled. Eligible participants with AI-predicted EGFR, ALK, or ROS1 mutations will be randomized in a 4:1 ratio to either the DeepGEM-informed group (clinicians can access AI results to guide further testing and treatment) or the standard care group (clinicians are blinded to AI results and follow routine care).
Official title: Application of the Artificial Intelligence-Based Gene Mutation Prediction Tool DeepGEM in Patients With Non-Small Cell Lung Cancer (NSCLC): A Prospective, Multicenter, Randomized Controlled Trial
Key Details
Gender
All
Age Range
18 Years - 75 Years
Study Type
INTERVENTIONAL
Enrollment
950
Start Date
2025-07-31
Completion Date
2028-07-31
Last Updated
2025-08-07
Healthy Volunteers
No
Conditions
Interventions
DeepGEM-guided Molecular Testing and Treatment
Artificial intelligence-based mutation prediction using DeepGEM to guide clinical decision-making for molecular testing and therapy selection.
Standard Diagnostic Pathway
DeepGEM is used for eligibility screening, but its results are withheld. Clinicians manage patients per standard diagnostic and treatment practices.