Research on the Application of Federated Learning in the Construction of Deep Learning Models for Renal Tumor Imaging

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.