[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"renal-tumors\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:renal-tumors":26},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,3,0,[8,47,69],{"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":22,"studyType":23,"phases":4,"briefSummary":24,"conditions":25,"keywords":27,"overallStatus":34,"whyStopped":4,"lastUpdateSubmitDate":35,"lastUpdatePostDateStruct":36,"startDateStruct":39,"completionDateStruct":41,"leadSponsor":43,"locationsCount":46},"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":20,"type":21},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.",[26],"Renal Tumors",[28,29,30,31,32,33],"renal tumors","multi-center","medical image","pathology","Genomics","multimodal","NOT_YET_RECRUITING","2026-09-21",{"date":37,"type":38},"2026-09-25","ACTUAL",{"date":40,"type":21},"2026-09-01",{"date":42,"type":21},"2028-08-30",{"name":44,"class":45},"First Affiliated Hospital of Guangxi Medical University","OTHER",6,{"id":48,"slug":49,"hasResults":11,"nctId":50,"briefTitle":51,"officialTitle":51,"acronym":52,"eligibilityCriteria":53,"healthyVolunteers":11,"sex":16,"minAge":17,"maxAge":4,"enrollmentInfo":54,"targetDuration":4,"studyType":23,"phases":4,"briefSummary":56,"conditions":57,"keywords":4,"overallStatus":58,"whyStopped":4,"lastUpdateSubmitDate":59,"lastUpdatePostDateStruct":60,"startDateStruct":62,"completionDateStruct":64,"leadSponsor":66,"locationsCount":68},"100582870","targeted-decision-making-in-renal-cell-carcinoma-relying-on-a-radiogenomics-translational-platform-100582870","NCT06868927","Targeted Decision Making In Renal Cell Carcinoma Relying on a Radiogenomics Translational Platform","TARGET-RCC","Inclusion Criteria:\n\n* Presence of a renal mass up to 4 cm\n* Adult \\> 18 years\n* Candidate to renal surgery\n* Informed consent signed\n\nExclusion Criteria:\n\n* Contraindication for MRI",{"count":55,"type":21},50,"This project aims to provide an accurate strategy to guide the decision making relying on tumor biopsies using standard histology, immunohistochemistry (IHC), spatial transcriptomics, genomics combined with Magnetic Resonance Imaging (MRI) and radiomics. Moreover, the investigators will explore potential markers indicative of response to systemic agents or ablative therapy.",[26],"RECRUITING","2026-03-16",{"date":61,"type":38},"2026-03-17",{"date":63,"type":38},"2025-09-26",{"date":65,"type":21},"2029-12-31",{"name":67,"class":45},"IRCCS San Raffaele",1,{"id":70,"slug":71,"hasResults":11,"nctId":72,"briefTitle":73,"officialTitle":74,"acronym":4,"eligibilityCriteria":75,"healthyVolunteers":11,"sex":16,"minAge":76,"maxAge":77,"enrollmentInfo":78,"targetDuration":4,"studyType":80,"phases":81,"briefSummary":83,"conditions":84,"keywords":85,"overallStatus":58,"whyStopped":4,"lastUpdateSubmitDate":90,"lastUpdatePostDateStruct":91,"startDateStruct":93,"completionDateStruct":95,"leadSponsor":97,"locationsCount":100},"100582813","a-multicenter-study-of-rus-ne-100582813","NCT06868186","A Multicenter Study of RUS NE","A Multicenter, Randomized, Parallel-group Study to Evaluate Efficacy of Surgical Navigation System 'RUS NE' in Patient Underwent Robot-assisted Partial Nephrectomy","Inclusion Criteria:\n\n* Individuals who were diagnosed with renal tumor and undergo robot-assisted partial nephrectomy\n* The diameter of renal tumor is 7cm or below\n* Individuals aged between 19 and 80\n* Individuals who, before participating in the clinical trial, undergo an interview in a separate place, understand sufficient explanation of the purpose and content of the trial, and voluntarily sign informed consent form\n\nExclusion Criteria:\n\n* Individuals with solitary kidney or horseshoe kidney\n* Individuals undergo bilateral nephrectomy\n* Individuals undergo operation other than partial nephrecotmy\n* Individuals with a history of abdominal surgery and are expected to have severe intra-abdominal adhesion influencing the surgical outcomes\n* Individuals with severe chronic kidney disease (eGFR \\\u003C 45, calculated with EPI-CKI equation)\n* Individuals with American society of anesthesiologists physical status classification (ASA) IV-VI\n* Individuals with a history of psychosis, alcohol abuse or are expected not to adhere to the schedule of the clinical study\n* Individuals who were participated in other clinical study within 4 weeks of screening\n* Individuals who are thought to be inappropriate to participate to the clinical trial by the investigators","19 Years","80 Years",{"count":79,"type":21},204,"INTERVENTIONAL",[82],"NA","This sponsor-initiated, randomized controlled clinical trial aims to demonstrate the clinical efficacy of RUS NE Surgical Navigation System in patients undergoing robotic-assisted partial nephrectomy. The trial will compare the experimental group (n=102) using RUS NE with a control group (n=102), aiming to show an 10% reduction in operation time. The study will involve 6 medical centers in South Korea in patient recruitment and evaluate the clinical efficacy and feasibility of the software, which has been shown to be reliable in previous studies.\n\n* Investigational Medical Device: RUS NE (Endoscopic Imaging Treatment Planning Software)\n* Clinical Trial duration: 24 months from IRB approval -Target number of subjects: Total of 204 participants",[26],[86,87,88,89],"renal tumor","robot assisted partial nephrectomy","3D model","surgical navigation","2025-09-10",{"date":92,"type":38},"2025-09-11",{"date":94,"type":38},"2025-04-29",{"date":96,"type":21},"2026-04-30",{"name":98,"class":99},"Hutom Corp","INDUSTRY",4]