[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100650270":3},{"organization":4,"armGroups":7,"interventions":14,"overallOfficials":10,"centralContacts":20,"locations":26,"responsibleParty":42,"collaborators":45,"id":56,"slug":57,"hasResults":58,"nctId":59,"briefTitle":60,"officialTitle":60,"acronym":10,"eligibilityCriteria":61,"healthyVolunteers":58,"sex":62,"minAge":63,"maxAge":10,"enrollmentInfo":64,"targetDuration":10,"studyType":67,"phases":10,"briefSummary":68,"conditions":69,"keywords":72,"overallStatus":29,"whyStopped":10,"lastUpdateSubmitDate":82,"lastUpdatePostDateStruct":83,"startDateStruct":86,"completionDateStruct":88,"leadSponsor":90,"locationsCount":91},{"fullName":5,"class":6},"Cancer Institute and Hospital, Chinese Academy of Medical Sciences","OTHER",[8],{"label":9,"type":10,"description":11,"interventionNames":12},"Patients With Renal Tumors",null,"Adult patients with pathologically confirmed renal tumors who underwent preoperative multisequence renal MRI as part of routine clinical care and had available pathological subtype and, when applicable, histological grade information. Existing de-identified MRI, pathological, clinical, and laboratory data were collected for artificial intelligence model development and evaluation. No additional examination, treatment, or study-specific intervention was administered.",[13],"Diagnostic Test: MRI-Based Artificial Intelligence Analysis",[15],{"type":16,"name":17,"description":18,"armGroupLabels":19,"otherNames":10},"DIAGNOSTIC_TEST","MRI-Based Artificial Intelligence Analysis","Existing preoperative multisequence renal MRI images, including T1-weighted imaging, T2-weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient imaging, fat-suppressed imaging, and contrast-enhanced imaging when available, were retrospectively analyzed using artificial intelligence and deep learning methods. The models were developed to detect and segment renal tumors and to predict pathological subtype and histological grade. Postoperative pathological findings were used as the reference standard. No additional MRI examination or diagnostic procedure was performed for the study.",[9],[21],{"name":22,"role":23,"phone":24,"phoneExt":24,"email":25},"Xiongjun Ye","CONTACT","010-87787170","yexiongjun@cicams.ac.cn",[27],{"facility":28,"status":29,"city":30,"state":10,"zip":10,"country":31,"countryCode":32,"cosmosGeoPoint":33,"geoPoint":38,"contacts":39},"Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College","RECRUITING","Beijing","China","CN",{"type":34,"coordinates":35},"Point",[36,37],116.39723,39.9075,{"lat":37,"lon":36},[40],{"name":41,"role":23,"phone":24,"phoneExt":10,"email":25},"xiongjun YE",{"type":43,"investigatorFullName":22,"investigatorTitle":44,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"PRINCIPAL_INVESTIGATOR","Chief Physician",[46,48,50,52,54],{"name":47,"class":6},"Peking University People's Hospital",{"name":49,"class":6},"Shanxi Province Cancer Hospital",{"name":51,"class":6},"Chinese PLA General Hospital",{"name":53,"class":6},"RenJi Hospital",{"name":55,"class":6},"Cancer Hospital Chinese Academy of Medical Science, Shenzhen Center","100650270","an-mri-based-study-of-intelligent-pathological-subtyping-and-grading-of-renal-tumors-100650270",false,"NCT07743749","An MRI-Based Study of Intelligent Pathological Subtyping and Grading of Renal Tumors","Inclusion Criteria:\n\n* Patients aged 18 years or older.\n* Patients diagnosed with a renal tumor.\n* Availability of preoperative renal magnetic resonance imaging examinations.\n* Availability of a corresponding pathological diagnosis, including pathological subtype and, where applicable, histological grade.\n* Magnetic resonance images that can be successfully retrieved and are of - - sufficient quality for image analysis.\n\nExclusion Criteria:\n\n* Absence of renal magnetic resonance imaging data.\n* Absence of a corresponding pathological diagnosis or insufficient pathological subtype or grading information.\n* Magnetic resonance images that cannot be retrieved, opened, or read.\n* Poor image quality that precludes reliable image annotation or artificial intelligence analysis.","ALL","18 Years",{"count":65,"type":66},900,"ESTIMATED","OBSERVATIONAL","This retrospective + prospective, non-interventional study aims to develop and evaluate artificial intelligence methods for the detection, pathological subtyping, and histological grading of renal tumors using magnetic resonance imaging (MRI). Approximately 900 adult patients with available preoperative renal MRI examinations and postoperative pathological results will be included. The pathological findings will be used as the reference standard for model development and evaluation. In addition to MRI data, selected demographic, clinical, and laboratory information may be incorporated to improve model performance. The study will not change participants' diagnosis, treatment, or follow-up, and no additional examinations or interventions will be required. All study data will be de-identified before analysis. The ultimate goal is to develop an MRI-based intelligent diagnostic approach that may assist clinicians in the preoperative assessment and individualized management of patients with renal tumors.",[70,71],"Renal Tumor","Kidney Neoplasm",[73,74,75,70,76,77,78,79,80,81],"Magnetic Resonance Imaging","Artificial Intelligence","Deep Learning","Pathological Subtyping","Histological Grading","Multimodal Learning","Computer-Aided Diagnosis","Tumor Segmentation","Rare Renal Tumor Subtypes","2026-07-29",{"date":84,"type":85},"2026-08-04","ACTUAL",{"date":87,"type":85},"2021-01-01",{"date":89,"type":66},"2026-12-31",{"name":5,"class":6},1]