AI-Based DeepGEM Tool for Predicting Gene Mutations in NSCLC Patients: A Randomized Controlled Study
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: DeepGEM-guided Molecular Testing and Treatment, Standard Diagnostic Pathway.
- Who it may be relevant to
- Registry conditions: Non Small Cell Lung Caner. Basic parameters: 18 years — 75 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Center list to be confirmed — check the primary protocol.
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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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
Overview
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).
Interventions
- 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.
Primary outcome measures
- Overall Survival (OS) [Time frame: From randomization to death from any cause, assessed up to 36 months]
- Targeted Therapy Utilization Rate [Time frame: Up to 6 months post-randomization]
Secondary outcome measures (3)
- Molecular Testing Rate [Time frame: Up to 3 months]
- Prediction Concordance [Time frame: Up to 3 months]
- Cost-effectiveness of DeepGEM [Time frame: Up to 12 months]
Eligibility criteria
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.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Allocation
- Randomized
- Model
- Parallel assignment
- Masking
- Single blind
- Primary purpose
- Health services research
Study locations
Center list to be confirmed — check the primary protocol.
Identifiers
NCT: NCT07110259 · NSCLC-DeepGEM-RCT-2025