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).
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.
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.
Jianxing He
Lead sponsor
The First Affiliated Hospital of Guangzhou Medical University
Sponsor institution
Guangzhou Kingmed Diagnostics Co., Ltd.
Collaborator