Clinical trial · Observational
Federated Learning in Renal Tumor Model
Research on the Application of Federated Learning in the Construction of Deep Learning Models for Renal Tumor Imaging
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 26, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260926-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Renal Tumors | Kidney Neoplasm | ALIAS | 0.90 |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (2)
- label
- Malignant renal tumor
- label
- Benign renal tumor
Primary outcomes (1)
- measure
- Dice Similarity Coefficient for Renal Tumor Segmentation
- timeFrame
- 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.
- description
- 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.
Secondary outcomes (1)
- measure
- Classification Performance Metrics and Heterogeneity Impact Assessment
- timeFrame
- 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
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
- Maximum age
- 60 Years
Show eligibility criteria text
Inclusion Criteria: 1. Patients pathologically confirmed as having renal tumors (either benign or malignant) by surgery or biopsy; 2. Underwent contrastenhanced renal CT before surgery or treatment; 3. CT images are of good quality and clearly depict the renal lesion contour; 4. Complete and traceable clinical and pathological data. Exclusion Criteria: 1. Patients who did not undergo contrastenhanced CT before surgery, or only had noncontrast CT; 2. CT images with significant motion artifacts, metal artifacts, or excessive noise that impair lesion assessment; 3. Lesions too small (maximum diameter \<1 mm) or not identifiable on imaging; 4. Patients who previously underwent partial or radical nephrectomy for renal tumors (except for recurrent/residual lesions); 5. Incomplete clinical data or pathological results.
References
Publications (3)
- BACKGROUNDXi IL, Zhao Y, Wang R, Chang M, Purkayastha S, Chang K, Huang RY, Silva AC, Vallieres M, Habibollahi P, Fan Y, Zou B, Gade TP, Zhang PJ, Soulen MC, Zhang Z, Bai HX, Stavropoulos SW. Deep Learning to Distinguish Benign from Malignant Renal Lesions Based on Routine MR Imaging. Clin Cancer Res. 2020 Apr 15;26(8):1944-1952. doi: 10.1158/1078-0432.CCR-19-0374. Epub 2020 Jan 14. PMID 31937619
- BACKGROUNDBex A, Ghanem YA, Albiges L, Bonn S, Campi R, Capitanio U, Dabestani S, Hora M, Klatte T, Kuusk T, Lund L, Marconi L, Palumbo C, Pignot G, Powles T, Schouten N, Tran M, Volpe A, Bedke J. European Association of Urology Guidelines on Renal Cell Carcinoma: The 2025 Update. Eur Urol. 2025 Jun;87(6):683-696. doi: 10.1016/j.eururo.2025.02.020. Epub 2025 Mar 20. PMID 40118739
- BACKGROUNDSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021 May;71(3):209-249. doi: 10.3322/caac.21660. Epub 2021 Feb 4. PMID 33538338