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

Jianxing He

Status: Not yet enrolling

Conditions: Non Small Cell Lung Caner

Treatments: Other: Standard Diagnostic Pathway; Other: DeepGEM-guided Molecular Testing and Treatment

Study type: Interventional

Funder types: Other

Identifiers

NCT07110259

NSCLC-DeepGEM-RCT-2025

Details and patient eligibility

About

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).

Enrollment: 950 estimated patients

Sex: All

Ages: 18 to 75 years old

Volunteers: No Healthy Volunteers

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.

Trial design

Primary purpose: Health Services Research

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Single Blind

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950 participants in 2 patient groups

DeepGEM-Informed Group

Experimental group

Description:

Participants whose clinicians are provided with DeepGEM-predicted mutation status (EGFR/ALK/ROS1). Physicians may choose to proceed with molecular testing and initiate targeted therapy based on AI predictions.

Treatment:

Other: DeepGEM-guided Molecular Testing and Treatment

Standard Care Group

Active Comparator group

Description:

Participants whose clinicians do not receive DeepGEM prediction results and manage the case per standard diagnostic and treatment protocols without AI support.

Treatment:

Other: Standard Diagnostic Pathway