[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100657784":3},{"organization":4,"outcomesModule":7,"designInfo":19,"detailedDescription":18,"studyPopulation":21,"armGroups":22,"interventions":18,"overallOfficials":27,"centralContacts":31,"locations":41,"responsibleParty":130,"collaborators":18,"id":134,"slug":135,"hasResults":136,"nctId":137,"briefTitle":138,"officialTitle":139,"acronym":18,"eligibilityCriteria":140,"healthyVolunteers":136,"sex":141,"minAge":142,"maxAge":143,"enrollmentInfo":144,"targetDuration":147,"studyType":148,"phases":18,"briefSummary":149,"conditions":150,"keywords":152,"overallStatus":159,"whyStopped":18,"lastUpdateSubmitDate":160,"lastUpdatePostDateStruct":161,"startDateStruct":164,"completionDateStruct":166,"leadSponsor":168,"locationsCount":169},{"fullName":5,"class":6},"First Affiliated Hospital of Guangxi Medical University","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":13,"otherOutcomes":18},[9],{"measure":10,"description":11,"timeFrame":12},"Dice Similarity Coefficient for Renal Tumor Segmentation","The primary outcome measure is the Dice Similarity Coefficient for renal tumor segmentation on contrast-enhanced CT, comparing the federated learning model against a centralized training model with a non-inferiority margin of Δ = -0.05. This outcome will be assessed at the end of the model training phase using an independent multi-center test set.","The primary outcome will be measured in October 2027, upon completion of model training, final parameter aggregation, and ensemble model establishment, using the test set from the multi-center retrospective cohort.",[14],{"measure":15,"description":16,"timeFrame":17},"Classification Performance Metrics and Heterogeneity Impact Assessment","The secondary outcomes are: (1) classification performance metrics-AUC, accuracy, sensitivity, and specificity-for benign versus malignant renal tumor discrimination, with pathological diagnosis as the reference; and (2) the impact of data heterogeneity on model performance, assessed by comparing Dice coefficients and AUC values across centers and pathological subtypes. All secondary outcomes will be evaluated at the completion of the model training phase, approximately 18 months after study initiation, after the final federated aggregation and ensemble model construction, using the independent multi-center test set, with center-specific and subtype-specific analyses performed thereafter.","Assessed at the completion of the model training phase, approximately 18 months after study initiation, following final aggregation and ensemble construction, using the independent multi-center test set, with center-specific and subtype-specific analyses",null,{"allocation":18,"interventionModel":18,"interventionModelDescription":18,"primaryPurpose":18,"observationalModel":20,"timePerspective":6,"maskingInfo":18},"CASE_CONTROL","The study population consists of approximately 3,000 patients with pathologically confirmed renal tumors (benign or malignant) who underwent contrast-enhanced CT at ten Guangxi hospitals between January 2019 and June 2026. Cases are consecutively enrolled using a unified time window and strict inclusion\u002Fexclusion criteria. Eligible patients must have complete pre-operative contrast-enhanced CT, good image quality, and traceable clinical-pathological data. Exclusions include non-contrast CT only, severe artifacts, prior nephrectomy (except recurrence\u002Fresidual), and incomplete data. Enrollment targets are 600 cases at the lead site and 300 at each of the nine participating centers.",[23,25],{"label":24,"type":18,"description":18,"interventionNames":18},"Malignant renal tumor",{"label":26,"type":18,"description":18,"interventionNames":18},"Benign renal tumor",[28],{"name":29,"affiliation":5,"role":30},"Min Qin, MD","STUDY_DIRECTOR",[32,37],{"name":33,"role":34,"phone":35,"phoneExt":18,"email":36},"Hai-qi Liang, MD","CONTACT","86+18176618137","liahiki@163.com",{"name":38,"role":34,"phone":39,"phoneExt":18,"email":40},"Nai-kai Liao, MD","86+18776891703","2546419718@qq.com",[42,60,74,88,102,116],{"facility":43,"status":18,"city":44,"state":45,"zip":46,"country":47,"countryCode":48,"cosmosGeoPoint":49,"geoPoint":54,"contacts":55},"Guigang People's Hospital","Guigang","Guangxi","537100","China","CN",{"type":50,"coordinates":51},"Point",[52,53],109.59472,23.11603,{"lat":53,"lon":52},[56],{"name":57,"role":34,"phone":58,"phoneExt":18,"email":59},"Jiale Feng, MD","86+17695684196","15498419548@qq.com",{"facility":61,"status":18,"city":62,"state":45,"zip":63,"country":47,"countryCode":48,"cosmosGeoPoint":64,"geoPoint":68,"contacts":69},"Guilin People's Hospital","Guilin","541002",{"type":50,"coordinates":65},[66,67],110.29639,25.28022,{"lat":67,"lon":66},[70],{"name":71,"role":34,"phone":72,"phoneExt":18,"email":73},"Ke Qin, MD","86+15954268457","QINKE541002@163.com",{"facility":75,"status":18,"city":76,"state":45,"zip":77,"country":47,"countryCode":48,"cosmosGeoPoint":78,"geoPoint":82,"contacts":83},"Liuzhou People's Hospital","Liuzhou","545006",{"type":50,"coordinates":79},[80,81],109.40698,24.32405,{"lat":81,"lon":80},[84],{"name":85,"role":34,"phone":86,"phoneExt":18,"email":87},"Jianwen Yang, MD","86+13675215625","JWyang235@163.com",{"facility":89,"status":18,"city":90,"state":45,"zip":91,"country":47,"countryCode":48,"cosmosGeoPoint":92,"geoPoint":96,"contacts":97},"Nanning Second People's Hospital","Nanning","530021",{"type":50,"coordinates":93},[94,95],108.31667,22.81667,{"lat":95,"lon":94},[98],{"name":99,"role":34,"phone":100,"phoneExt":18,"email":101},"Yang Xie, MD","86+18346826751","XieYNan@163.com",{"facility":103,"status":18,"city":90,"state":45,"zip":91,"country":47,"countryCode":48,"cosmosGeoPoint":104,"geoPoint":106,"contacts":107},"The First Affiliated Hospital of Guangxi Medical University",{"type":50,"coordinates":105},[94,95],{"lat":95,"lon":94},[108,111,113],{"name":109,"role":34,"phone":39,"phoneExt":18,"email":110},"Ji-wen Cheng, PhD","chengjiwen@stu.gxmu.edu.cn",{"name":112,"role":34,"phone":35,"phoneExt":18,"email":36},"Haiqi Liang, MD",{"name":114,"role":115,"phone":18,"phoneExt":18,"email":18},"Shenghua Deng, MD","SUB_INVESTIGATOR",{"facility":117,"status":18,"city":118,"state":45,"zip":119,"country":47,"countryCode":48,"cosmosGeoPoint":120,"geoPoint":124,"contacts":125},"Wuzhou Red Cross Hospital","Wuzhou","543002",{"type":50,"coordinates":121},[122,123],111.28848,23.48054,{"lat":123,"lon":122},[126],{"name":127,"role":34,"phone":128,"phoneExt":18,"email":129},"Deyan Ou, MD","86+18675651254","OudYan1254@163.com",{"type":131,"investigatorFullName":132,"investigatorTitle":133,"investigatorAffiliation":5,"oldNameTitle":18,"oldOrganization":18},"PRINCIPAL_INVESTIGATOR","Jiwen Cheng","Vice president of the First Affiliated Hospital of Guangxi Medical University","100657784","federated-learning-in-renal-tumor-model-100657784",false,"NCT07841756","Federated Learning in Renal Tumor Model","Research on the Application of Federated Learning in the Construction of Deep Learning Models for Renal Tumor Imaging","Inclusion Criteria:\n\n1. Patients pathologically confirmed as having renal tumors (either benign or malignant) by surgery or biopsy;\n2. Underwent contrastenhanced renal CT before surgery or treatment;\n3. CT images are of good quality and clearly depict the renal lesion contour;\n4. Complete and traceable clinical and pathological data.\n\nExclusion Criteria:\n\n1. Patients who did not undergo contrastenhanced CT before surgery, or only had noncontrast CT;\n2. CT images with significant motion artifacts, metal artifacts, or excessive noise that impair lesion assessment;\n3. Lesions too small (maximum diameter \\\u003C1 mm) or not identifiable on imaging;\n4. Patients who previously underwent partial or radical nephrectomy for renal tumors (except for recurrent\u002Fresidual lesions);\n5. Incomplete clinical data or pathological results.","ALL","18 Years","60 Years",{"count":145,"type":146},3000,"ESTIMATED","2 Years","OBSERVATIONAL","This is a multicenter, retrospective and prospective diagnostic clinical trial evaluating the effectiveness and safety of CascadeDiagnose Renal Tumor CT, an AI-assisted detection system for renal tumors based on contrast-enhanced multiphase CT imaging. The system employs a cascaded deep learning architecture to perform fully automated analysis of multiphase CT images, covering image quality review, lesion detection and segmentation, benign-malignant differentiation, and risk stratification, with traceable evidence chains and interpretable outputs. The study is conducted across six tertiary hospitals in Guangxi, China, utilizing a distributed \"data stays on-site, computation moves across centers\" federated learning network, which ensures that original patient data remain within each hospital while encrypted intermediate features are shared for cross-center collaborative analysis. A total of at least 3000 patients with renal tumors will be enrolled (approximately 2600 in the retrospective phase and 400 in the prospective phase). The primary effectiveness outcomes include area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The primary safety outcomes include false negative rate, false positive rate, and adverse events. The study also incorporates a multi-reader, multi-case (MRMC) design to compare the diagnostic performance of the AI system with radiologists of varying seniority, and to evaluate the system's utility in assisting junior radiologists. The findings of this study are expected to provide high-quality clinical evidence for the regulatory approval of this AI-assisted diagnostic system as a Class III medical device.",[151],"Renal Tumors",[153,154,155,156,157,158],"renal tumors","multi-center","medical image","pathology","Genomics","multimodal","NOT_YET_RECRUITING","2026-09-21",{"date":162,"type":163},"2026-09-25","ACTUAL",{"date":165,"type":146},"2026-09-01",{"date":167,"type":146},"2028-08-30",{"name":5,"class":6},6]