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
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 950
- 主要终点
- Overall Survival (OS)
研究概览
简要总结
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).
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Health Services Research
- 盲法
- Single (Investigator)
入排标准
- 年龄范围
- 18 Years 至 75 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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.
排除标准
- •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.
结局指标
主要结局
Overall Survival (OS)
时间窗: 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
时间窗: Up to 6 months post-randomization
Proportion of participants receiving molecularly matched targeted therapies based on standard genetic testing.
次要结局
- Molecular Testing Rate(Up to 3 months)
- Prediction Concordance(Up to 3 months)
- Cost-effectiveness of DeepGEM(Up to 12 months)
研究者
Jianxing He
Clinical Professor
The First Affiliated Hospital of Guangzhou Medical University