Non Small Cell Lung CanerNCT07110259
AI-Based DeepGEM Tool for Predicting Gene Mutations in NSCLC Patients: A Randomized Controlled Study
A study testing DeepGEM-guided Molecular Testing and Treatment and Standard Diagnostic Pathway in people with Non Small Cell Lung Caner, run by Jianxing He.
Trial 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). Read more about this study
- Who is it for?
- Ages 18 to 75 years
- Conditions the study is for
- Non Small Cell Lung Caner
- What is being tested?
- DeepGEM-guided Molecular Testing and Treatment and Standard Diagnostic Pathway
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 950 participants
- Where
- No sites listed yet
- Study dates
- 31 Jul 2025to 31 Jul 2028
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 75 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- 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.
Who cannot join
- 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 locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
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
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
- Being tested
- DeepGEM-guided Molecular Testing and Treatment, Standard Diagnostic Pathway
- Keywords
- Artificial intelligence, gene mutations
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
DeepGEM-guided Molecular Testing and TreatmentOther
Artificial intelligence-based mutation prediction using DeepGEM to guide clinical decision-making for molecular testing and therapy selection.
Standard Diagnostic PathwayOther
DeepGEM is used for eligibility screening, but its results are withheld. Clinicians manage patients per standard diagnostic and treatment practices.
| Study group | Role | What they receive |
|---|---|---|
| DeepGEM-Informed Group | Experimental | 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. Receives: Other: DeepGEM-guided Molecular Testing and Treatment |
| Standard Care Group | Active comparator | Participants whose clinicians do not receive DeepGEM prediction results and manage the case per standard diagnostic and treatment protocols without AI support. Receives: Other: Standard Diagnostic Pathway |
What is being measured
The primary outcome is the main question the study is designed to answer.
Overall Survival (OS)
Comparison of OS between the DeepGEM-informed group and the standard care group.
Measured: From randomization to death from any cause, assessed up to 36 months
Targeted Therapy Utilization Rate
Proportion of participants receiving molecularly matched targeted therapies based on standard genetic testing.
Measured: Up to 6 months post-randomization
Secondary outcomes (3)
Molecular Testing Rate
Proportion of participants who undergo molecular testing after initial DeepGEM prediction.
Measured: Up to 3 months
Prediction Concordance
Concordance between DeepGEM-predicted mutation status and results from PCR or NGS molecular testing.
Measured: Up to 3 months
Cost-effectiveness of DeepGEM
Evaluation of cost per targeted therapy initiated and cost per life-year gained in the DeepGEM group versus standard care.
Measured: Up to 12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted7 Aug 2025
- Study start31 Jul 2025 (estimated)
- Primary completion31 Jul 2028 (estimated)
- Study completion31 Jul 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Non Small Cell Lung CanerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind
- Who is masked
- Investigator
- Enrolment
- 950 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Jianxing He
- Sponsor type
- University, hospital or other
- Collaborators
- Guangzhou Kingmed Diagnostics Co., Ltd.
- Responsible party
- Sponsor-investigator, Jianxing He, The First Affiliated Hospital of Guangzhou Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07110259
- Sponsor's protocol ID
- NSCLC-DeepGEM-RCT-2025
- Status last verified
- Jul 2025
- First submitted
- 31 Jul 2025
- Last updated
- 7 Aug 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 7 Aug 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- 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
- ClinicalTrials.gov ID
- NCT07110259
- Acronym / sponsor ID
- NSCLC-DeepGEM-RCT-2025
- Conditions
- Non Small Cell Lung Caner
- Keywords
- Artificial intelligence, gene mutations
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 950 participants (estimated)
- Lead sponsor
- Jianxing HeUniversity, hospital or other
- Collaborators
- Guangzhou Kingmed Diagnostics Co., Ltd.
- Study start
- 31 Jul 2025 (estimated)
- Primary completion
- 31 Jul 2028 (estimated)
- Study completion
- 31 Jul 2028 (estimated)
- First posted
- 7 Aug 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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).
Study design
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (investigator)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| DeepGEM-Informed Group | Experimental | Other: DeepGEM-guided Molecular Testing and TreatmentParticipants 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. |
| Standard Care Group | Active comparator | Other: Standard Diagnostic PathwayParticipants whose clinicians do not receive DeepGEM prediction results and manage the case per standard diagnostic and treatment protocols without AI support. |
Who may qualify
- Ages
- 18 to 75 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (6 listed)
- 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.
Key exclusion criteria (6 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| DeepGEM-guided Molecular Testing and TreatmentArtificial intelligence-based mutation prediction using DeepGEM to guide clinical decision-making for molecular testing and therapy selection. | Other | DeepGEM-Informed Group |
| Standard Diagnostic PathwayDeepGEM is used for eligibility screening, but its results are withheld. Clinicians manage patients per standard diagnostic and treatment practices. | Other | Standard Care Group |
Primary outcome
Overall Survival (OS)
Time frame: 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
Time frame: Up to 6 months post-randomization
Proportion of participants receiving molecularly matched targeted therapies based on standard genetic testing.
3 secondary outcomes
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
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 7 Aug 2025.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Questions about this trial
What is this clinical trial studying?
A study testing DeepGEM-guided Molecular Testing and Treatment and Standard Diagnostic Pathway in people with Non Small Cell Lung Caner, run by Jianxing He.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 7 Aug 2025. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Jianxing He, PhD (13802777270, hejx@vip.163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Overall Survival (OS) (time frame: From randomization to death from any cause, assessed up to 36 months). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 7 Aug 2025.