NCT07110259

AI-Based DeepGEM Tool for Predicting Gene Mutations in NSCLC Patients: A Randomized Controlled Study

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

• Status: Not Yet Recruiting
• Phase: N/A
• Study type: Interventional
• Enrollment: 950 (estimated)
• Sponsor: Jianxing He · Academic / Other
• Sex: All
• Age: 18 Years – 75 Years
• Healthy volunteers: Not accepted

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).

Conditions

• Non Small Cell Lung Caner

Interventions

Type	Name	Description
OTHER	DeepGEM-guided Molecular Testing and Treatment	Artificial intelligence-based mutation prediction using DeepGEM to guide clinical decision-making for molecular testing and therapy selection.
OTHER	Standard Diagnostic Pathway	DeepGEM is used for eligibility screening, but its results are withheld. Clinicians manage patients per standard diagnostic and treatment practices.

Timeline

• Start date: 2025-07-31
• Primary completion: 2028-07-31
• Completion: 2028-07-31
• First posted: 2025-08-07
• Last updated: 2025-08-07