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

NCT07110259 | Not Yet Recruiting

• 1 other identifier: NSCLC-DeepGEM-RCT-2025
• Study Type: interventional
• Target: 950
• Locations: 0 countries
• Sites: N/A

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).

Conditions

Non Small Cell Lung Caner

Keywords

Artificial intelligence; gene mutations

Outcome Measures

Primary Outcomes (2)

• Overall Survival (OS)

  Comparison of OS between the DeepGEM-informed group and the standard care group.

  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.

  Up to 6 months post-randomization

Secondary Outcomes (3)

• Molecular Testing Rate

  Up to 3 months

• Prediction Concordance

  Up to 3 months

• Cost-effectiveness of DeepGEM

  Up to 12 months

Study Arms (2)

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.

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.

Other: Standard Diagnostic Pathway

Interventions

DeepGEM-guided Molecular Testing and Treatment OTHER

Artificial intelligence-based mutation prediction using DeepGEM to guide clinical decision-making for molecular testing and therapy selection.

DeepGEM-Informed Group

Standard Diagnostic Pathway OTHER

DeepGEM is used for eligibility screening, but its results are withheld. Clinicians manage patients per standard diagnostic and treatment practices.

Standard Care Group

Eligibility Criteria

• Age: 18 Years - 75 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Jianxing He: lead
• Guangzhou Kingmed Diagnostics Co., Ltd.: collaborator

MeSH Terms

Interventions

Therapeutics

Central Study Contacts

Jianxing He, PhD

CONTACT

13802777270; hejx@vip.163.com

Wenhua Liang, PhD

CONTACT

13710249454; 550627660@qq.com

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: INVESTIGATOR
• Purpose: HEALTH SERVICES RESEARCH
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Clinical Professor

Study Record Dates

• First Submitted: July 31, 2025
• First Posted: August 7, 2025
• Study Start: July 31, 2025
• Primary Completion (Estimated): July 31, 2028
• Study Completion (Estimated): July 31, 2028
• Last Updated: August 7, 2025

Record last verified: 2025-07

Data Sharing

• IPD Sharing: Will not share