[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100615076":3},{"organization":4,"armGroups":7,"interventions":8,"overallOfficials":13,"centralContacts":17,"locations":25,"responsibleParty":41,"collaborators":7,"id":44,"slug":45,"hasResults":46,"nctId":47,"briefTitle":48,"officialTitle":49,"acronym":7,"eligibilityCriteria":50,"healthyVolunteers":46,"sex":51,"minAge":52,"maxAge":53,"enrollmentInfo":54,"targetDuration":7,"studyType":57,"phases":7,"briefSummary":58,"conditions":59,"keywords":7,"overallStatus":64,"whyStopped":7,"lastUpdateSubmitDate":65,"lastUpdatePostDateStruct":66,"startDateStruct":69,"completionDateStruct":71,"leadSponsor":73,"locationsCount":74},{"fullName":5,"class":6},"Union Hospital, Tongji Medical College, Huazhong University of Science and Technology","OTHER",null,[9],{"type":10,"name":11,"description":12,"armGroupLabels":7,"otherNames":7},"DIAGNOSTIC_TEST","Comprehensive analysis through laboratory tests, imaging techniques, and clinical data","Extract radiomics features of the tumor and peritumoral regions from preoperative CT images, H\\&E-stained digital pathology whole-slide images, and genetic test reports, and integrate them with multidimensional pathological features and gene expression distribution characteristics to construct a radiopathogenomic multi-omics modality, providing more precise prognostic assessment for targeted therapy in lung cancer patients.",[14],{"name":15,"affiliation":5,"role":16},"Xiaorong Dong, Dr","PRINCIPAL_INVESTIGATOR",[18,23],{"name":19,"role":20,"phone":21,"phoneExt":7,"email":22},"Na Li, Dr","CONTACT","02785726114","ln19931020@126.com",{"name":15,"role":20,"phone":7,"phoneExt":7,"email":24},"xiaorongdong@hust.edu.cn",[26],{"facility":27,"status":7,"city":28,"state":29,"zip":30,"country":31,"countryCode":32,"cosmosGeoPoint":33,"geoPoint":38,"contacts":39},"Wuhan Union Hospital","Wuhan","Hubei","430022","China","CN",{"type":34,"coordinates":35},"Point",[36,37],114.26667,30.58333,{"lat":37,"lon":36},[40],{"name":19,"role":20,"phone":21,"phoneExt":7,"email":22},{"type":16,"investigatorFullName":42,"investigatorTitle":43,"investigatorAffiliation":5,"oldNameTitle":7,"oldOrganization":7},"Xiaorong Dong","Professor","100615076","prediction-of-targeted-therapy-efficacy-in-egfr-mutant-lung-cancer-patients-using-ai-based-multimodal-data-100615076",false,"NCT07287904","Prediction of Targeted Therapy Efficacy in EGFR-mutant Lung Cancer Patients Using AI-based Multimodal Data","A Retrospective Analysis Study on Predicting the Efficacy of Targeted Therapy in Lung Cancer Patients With EGFR Mutations Based on AI-driven Multimodal Data","Inclusion Criteria:\n\n1. Age 18-80 years, undergoing radical surgery for lung cancer (R0 resection);\n2. Postoperative pathological stage IB-IIIA, pathology confirmed as adenocarcinoma;\n3. EGFR gene testing positive, EGFR 19del\u002FL858R mutation;\n4. Receiving postoperative EGFR-TKI targeted adjuvant therapy;\n5. Complete and clear preoperative imaging data, genetic testing report, and pathology report available.\n\nExclusion Criteria:\n\n1. Patients negative for EGFR;\n2. Incomplete surgical resection (R1, R2);\n3. Did not receive EGFR-TKI targeted therapy after surgery;\n4. Recurrent or advanced stage patients;\n5. Incomplete preoperative or postoperative data;\n6. Patients who died within 30 days post-surgery.","ALL","18 Years","80 Years",{"count":55,"type":56},1000,"ESTIMATED","OBSERVATIONAL","The main purpose of this study is to explore the value of multimodal imaging information and models in predicting the prognosis of EGFR-positive non-small cell lung cancer patients undergoing targeted therapy, providing a basis for selecting suitable populations for precise tumor treatment and corresponding therapy. We retrospectively analyzed patient case data, extracted preoperative CT images, H\\&E-stained whole-slide digital pathology images, and pre- or postoperative genetic testing reports to extract radiomic features of tumor and peritumoral regions. These features were combined with multidimensional pathological features and gene expression distribution characteristics to construct a multimodal radiopathogenomic model, offering more precise prognostic evaluation for lung cancer patients receiving targeted therapy.",[60,61,62,63],"Lung Cancer (NSCLC)","EGFR Activating Mutation","Adenocarcinoma Lung","Postoperative Adjuvant Therapy","NOT_YET_RECRUITING","2025-12-16",{"date":67,"type":68},"2025-12-17","ACTUAL",{"date":70,"type":56},"2025-12-25",{"date":72,"type":56},"2027-08",{"name":5,"class":6},1]