[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100601420":3},{"organization":4,"armGroups":7,"interventions":20,"overallOfficials":25,"centralContacts":30,"locations":25,"responsibleParty":40,"collaborators":44,"id":48,"slug":49,"hasResults":50,"nctId":51,"briefTitle":52,"officialTitle":53,"acronym":25,"eligibilityCriteria":54,"healthyVolunteers":50,"sex":55,"minAge":56,"maxAge":57,"enrollmentInfo":58,"targetDuration":25,"studyType":61,"phases":62,"briefSummary":64,"conditions":65,"keywords":67,"overallStatus":70,"whyStopped":25,"lastUpdateSubmitDate":71,"lastUpdatePostDateStruct":72,"startDateStruct":75,"completionDateStruct":76,"leadSponsor":78,"locationsCount":25},{"fullName":5,"class":6},"The First Affiliated Hospital of Guangzhou Medical University","OTHER",[8,14],{"label":9,"type":10,"description":11,"interventionNames":12},"DeepGEM-Informed Group","EXPERIMENTAL","Participants whose clinicians are provided with DeepGEM-predicted mutation status (EGFR\u002FALK\u002FROS1). Physicians may choose to proceed with molecular testing and initiate targeted therapy based on AI predictions.",[13],"Other: DeepGEM-guided Molecular Testing and Treatment",{"label":15,"type":16,"description":17,"interventionNames":18},"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.",[19],"Other: Standard Diagnostic Pathway",[21,26],{"type":6,"name":22,"description":23,"armGroupLabels":24,"otherNames":25},"DeepGEM-guided Molecular Testing and Treatment","Artificial intelligence-based mutation prediction using DeepGEM to guide clinical decision-making for molecular testing and therapy selection.",[9],null,{"type":6,"name":27,"description":28,"armGroupLabels":29,"otherNames":25},"Standard Diagnostic Pathway","DeepGEM is used for eligibility screening, but its results are withheld. Clinicians manage patients per standard diagnostic and treatment practices.",[15],[31,36],{"name":32,"role":33,"phone":34,"phoneExt":25,"email":35},"Jianxing He, PhD","CONTACT","13802777270","hejx@vip.163.com",{"name":37,"role":33,"phone":38,"phoneExt":25,"email":39},"Wenhua Liang, PhD","13710249454","550627660@qq.com",{"type":41,"investigatorFullName":42,"investigatorTitle":43,"investigatorAffiliation":5,"oldNameTitle":25,"oldOrganization":25},"SPONSOR_INVESTIGATOR","Jianxing He","Clinical Professor",[45],{"name":46,"class":47},"Guangzhou Kingmed Diagnostics Co., Ltd.","UNKNOWN","100601420","ai-based-deepgem-tool-for-predicting-gene-mutations-in-nsclc-patients-a-randomized-controlled-study-100601420",false,"NCT07110259","AI-Based DeepGEM Tool for Predicting Gene Mutations in NSCLC Patients: A Randomized Controlled Study","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","Inclusion Criteria:\n\n* Age between 18 and 75 years, inclusive, at the time of enrollment.\n* 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.\n* Availability of qualified histopathological whole-slide images that can be reviewed through the KindMED system(DeepGEM).\n* Successful mutation prediction of EGFR, ALK, or ROS1 by the DeepGEM AI tool.\n* No prior systemic anti-cancer therapy, including chemotherapy, targeted therapy, or immunotherapy.\n* Willing and able to comply with study requirements, including follow-up and treatment; written informed consent must be provided.\n\nExclusion Criteria:\n\n* Prior systemic anti-tumor therapy (chemotherapy, radiotherapy, targeted therapy-including but not limited to monoclonal antibodies or tyrosine kinase inhibitors) before enrollment.\n* Failure of DeepGEM analysis or unqualified histopathological image quality.\n* 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).\n* Cognitive or psychological barriers to understanding or accepting AI-based prediction or molecular testing.\n* Pregnant or breastfeeding women, or women of childbearing potential who are not using effective contraception.\n* 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.","ALL","18 Years","75 Years",{"count":59,"type":60},950,"ESTIMATED","INTERVENTIONAL",[63],"NA","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).",[66],"Non Small Cell Lung Caner",[68,69],"Artificial intelligence","gene mutations","NOT_YET_RECRUITING","2025-07-31",{"date":73,"type":74},"2025-08-07","ACTUAL",{"date":71,"type":60},{"date":77,"type":60},"2028-07-31",{"name":42,"class":6}]