Clinical Research Directory

Federated Learning in Renal Tumor Model

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.

Targeted Decision Making In Renal Cell Carcinoma Relying on a Radiogenomics Translational Platform

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.

A Multicenter Study of RUS NE

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. * Investigational Medical Device: RUS NE (Endoscopic Imaging Treatment Planning Software) * Clinical Trial duration: 24 months from IRB approval -Target number of subjects: Total of 204 participants

Imaging-Guided Classification for Endophytic Renal Tumors: PN Strategies & Outcomes

Study Title: Imaging-Guided Classification \& Surgical Outcomes in Endophytic Renal Tumors Study Goal: This observational study investigates whether an imaging-guided classification system improves partial nephrectomy (PN) outcomes for completely endophytic renal cell carcinoma (RCC). Main Question: Does preoperative imaging classification reduce complications (e.g., ischemia time, urinary leakage) in PN for endophytic RCC compared to traditional methods? Methods: Patients undergoing PN for endophytic RCC will be grouped based on preoperative imaging classification. Surgical outcomes (complications, renal function) will be tracked for 5 years and compared to non-classified PN cases. Significance: Aims to optimize PN planning, minimize risks, and improve long-term renal preservation in complex RCC cases