[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100616352":3},{"organization":4,"armGroups":7,"interventions":7,"overallOfficials":7,"centralContacts":8,"locations":17,"responsibleParty":34,"collaborators":7,"id":37,"slug":38,"hasResults":39,"nctId":40,"briefTitle":41,"officialTitle":42,"acronym":7,"eligibilityCriteria":43,"healthyVolunteers":39,"sex":44,"minAge":45,"maxAge":46,"enrollmentInfo":47,"targetDuration":50,"studyType":51,"phases":7,"briefSummary":52,"conditions":53,"keywords":56,"overallStatus":59,"whyStopped":7,"lastUpdateSubmitDate":60,"lastUpdatePostDateStruct":61,"startDateStruct":64,"completionDateStruct":66,"leadSponsor":68,"locationsCount":69},{"fullName":5,"class":6},"Fudan University","OTHER",null,[9,14],{"name":10,"role":11,"phone":12,"phoneExt":7,"email":13},"Yajia Gu, MD","CONTACT","+8621-64175590","guyajia@fudan.edu.cn",{"name":15,"role":11,"phone":12,"phoneExt":7,"email":16},"Bingni Zhou, MD","jobay2621405@126.com",[18],{"facility":19,"status":7,"city":20,"state":21,"zip":22,"country":23,"countryCode":24,"cosmosGeoPoint":25,"geoPoint":30,"contacts":31},"Fudan university Shanghai Cancer Center","Shanghai","Shanghai Municipality","200032","China","CN",{"type":26,"coordinates":27},"Point",[28,29],121.45806,31.22222,{"lat":29,"lon":28},[32,33],{"name":10,"role":11,"phone":12,"phoneExt":7,"email":13},{"name":15,"role":11,"phone":12,"phoneExt":7,"email":16},{"type":35,"investigatorFullName":10,"investigatorTitle":36,"investigatorAffiliation":5,"oldNameTitle":7,"oldOrganization":7},"PRINCIPAL_INVESTIGATOR","Director, Head of Radiology, Principal Investigator, Clinical Professor","100616352","ai-for-renal-tumors-using-non-contrast-ct-100616352",false,"NCT07304492","AI for Renal Tumors Using Non-Contrast CT","An Artificial Intelligence Model for Screening and Diagnosis of Renal Tumors Based on Non-Contrast CT","Inclusion Criteria:\n\n1. Patients who underwent an abdominal CT examination.\n2. Patients with renal lesions were managed according to standard clinical pathways, which included follow-up, biopsy, or surgery.\n3. Malignant lesions were pathologically confirmed; benign lesions were confirmed by either pathological diagnosis or imaging follow-up.\n4. No prior treatment had been received for the renal disease.\n\nExclusion Criteria:\n\n1. Patients refuse to undergo recommended follow-up, biopsy, or surgery, which precluded definitive diagnosis of the renal lesion.\n2. Absence of complete pathological confirmation for lesions suspected to be malignant.\n3. Patients have received any form of prior treatment for the renal lesion.\n4. Poor image quality that hampered diagnostic evaluation.","ALL","18 Years","80 Years",{"count":48,"type":49},10000,"ESTIMATED","6 Months","OBSERVATIONAL","The goal of this observational study is to learn whether the artificial intelligence method can automatically identify and diagnose renal lesions using non-contrast CT or opportunistic screening.",[54,55],"Renal Neoplasms","Renal Cyst",[57,54,55,58],"Artificial intelligence","Computer tomography","NOT_YET_RECRUITING","2025-12-13",{"date":62,"type":63},"2025-12-26","ACTUAL",{"date":65,"type":49},"2026-01",{"date":67,"type":49},"2028-12",{"name":5,"class":6},1]