Non Small Cell Lung Caner
Conditions
Keywords
Artificial intelligence, gene mutations
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).
Interventions
OTHERDeepGEM-guided Molecular Testing and Treatment
Artificial intelligence-based mutation prediction using DeepGEM to guide clinical decision-making for molecular testing and therapy selection.
OTHERStandard Diagnostic Pathway
DeepGEM is used for eligibility screening, but its results are withheld. Clinicians manage patients per standard diagnostic and treatment practices.
Sponsors
Guangzhou Kingmed Diagnostics Co., Ltd.
Jianxing He
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
HEALTH_SERVICES_RESEARCH
Masking
SINGLE (Investigator)
Eligibility
Sex/Gender
ALL
Age
18 Years to 75 Years
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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
| Measure | Time frame | Description |
|---|---|---|
| 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. |
Contacts
Primary ContactJianxing He, PhD
Backup ContactWenhua Liang, PhD
Outcome results
None listed