[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"adenocarcinoma-lung\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:adenocarcinoma-lung":27},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,1,0,[8],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":4,"eligibilityCriteria":15,"healthyVolunteers":11,"sex":16,"minAge":17,"maxAge":18,"enrollmentInfo":19,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":23,"conditions":24,"keywords":4,"overallStatus":29,"whyStopped":4,"lastUpdateSubmitDate":30,"lastUpdatePostDateStruct":31,"startDateStruct":34,"completionDateStruct":36,"leadSponsor":38,"locationsCount":5},"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":20,"type":21},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.",[25,26,27,28],"Lung Cancer (NSCLC)","EGFR Activating Mutation","Adenocarcinoma Lung","Postoperative Adjuvant Therapy","NOT_YET_RECRUITING","2025-12-16",{"date":32,"type":33},"2025-12-17","ACTUAL",{"date":35,"type":21},"2025-12-25",{"date":37,"type":21},"2027-08",{"name":39,"class":40},"Union Hospital, Tongji Medical College, Huazhong University of Science and Technology","OTHER"]