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

Not Applicable

Not yet recruiting

• Conditions: Non Small Cell Lung Caner

• Registration Number: NCT07110259
• Lead Sponsor: Jianxing He

Brief 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).

Detailed Description

Not available

Study Timeline

• Status Verified: 2025-07
• Study Start: 2025-07-31
• Study First Submitted: 2025-07-31
• Study First Submitted QC: 2025-07-31
• Last Update Submitted: 2025-07-31
• Study First Posted: 2025-08-07
• Last Update Posted: 2025-08-07
• Primary Completion: 2028-07-31
• Study Completion: 2028-07-31

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 950

Inclusion Criteria

• Age between 18 and 75 years, inclusive, at the time of enrollment.
• Histologically or cytologically confirmed non-small cell lung cancer (NSCLC) with clinical stage II-IV as per the 8th edition of the AJCC staging system.
• Availability of qualified histopathological whole-slide images that can be reviewed through the KindMED system(DeepGEM).
• Successful mutation prediction of EGFR, ALK, or ROS1 by the DeepGEM AI tool.
• No prior systemic anti-cancer therapy, including chemotherapy, targeted therapy, or immunotherapy.
• Willing and able to comply with study requirements, including follow-up and treatment; written informed consent must be provided.

Exclusion Criteria

• Prior systemic anti-tumor therapy (chemotherapy, radiotherapy, targeted therapy-including but not limited to monoclonal antibodies or tyrosine kinase inhibitors) before enrollment.
• Failure of DeepGEM analysis or unqualified histopathological image quality.
• History of any other malignancy within the past 5 years, except for adequately treated basal cell carcinoma of the skin or in situ carcinoma (e.g., cervical carcinoma in situ).
• Cognitive or psychological barriers to understanding or accepting AI-based prediction or molecular testing.
• Pregnant or breastfeeding women, or women of childbearing potential who are not using effective contraception.
• Any other clinical condition that, in the opinion of the investigators, may interfere with the study protocol or compromise participant safety, including poor compliance with study procedures.

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: PARALLEL

Primary Outcome Measures

Name	Time	Method
Overall Survival (OS)	From randomization to death from any cause, assessed up to 36 months	Comparison of OS between the DeepGEM-informed group and the standard care group.
Targeted Therapy Utilization Rate	Up to 6 months post-randomization	Proportion of participants receiving molecularly matched targeted therapies based on standard genetic testing.

Secondary Outcome Measures

Name	Time	Method
Molecular Testing Rate	Up to 3 months	Proportion of participants who undergo molecular testing after initial DeepGEM prediction.
Prediction Concordance	Up to 3 months	Concordance between DeepGEM-predicted mutation status and results from PCR or NGS molecular testing.
Cost-effectiveness of DeepGEM	Up to 12 months	Evaluation of cost per targeted therapy initiated and cost per life-year gained in the DeepGEM group versus standard care.