Federated Learning in Renal Tumor Model

October 28, 2026 updated by: Jiwen Cheng, First Affiliated Hospital of Guangxi Medical University

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.

Study Overview

Status

Not yet recruiting

Conditions

Study Type

Observational

Enrollment (Estimated)

3000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

  • Name: Hai-qi Liang, MD
  • Phone Number: 86+18176618137
  • Email: liahiki@163.com

Study Contact Backup

Study Locations

  • China
    • Guangxi
      • Guigang, Guangxi, China, 537100
        • Guigang People's Hospital
        • Contact:
      • Guilin, Guangxi, China, 541002
        • Guilin People's Hospital
        • Contact:
      • Liuzhou, Guangxi, China, 545006
        • Liuzhou People's Hospital
        • Contact:
      • Nanning, Guangxi, China, 530021
        • The First Affiliated Hospital of Guangxi Medical University
        • Contact:
        • Contact:
        • Sub-Investigator:
          • Shenghua Deng, MD
      • Nanning, Guangxi, China, 530021
        • Nanning Second People's Hospital
        • Contact:
      • Wuzhou, Guangxi, China, 543002
        • Wuzhou Red Cross Hospital
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years to 60 years (Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Sampling Method

Probability Sample

Study Population

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/exclusion 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/residual), and incomplete data. Enrollment targets are 600 cases at the lead site and 300 at each of the nine participating centers.

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Observational Models: CASE_CONTROL
  • Time Perspectives: OTHER

Cohorts and Interventions

Group / Cohort
Malignant renal tumor
Benign renal tumor

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Dice Similarity Coefficient for Renal Tumor Segmentation
Time Frame: 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.

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.

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Classification Performance Metrics and Heterogeneity Impact Assessment
Time Frame: 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

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

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

First Affiliated Hospital of Guangxi Medical University

Investigators

  • Study Director: Min Qin, MD, First Affiliated Hospital of Guangxi Medical University

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

October 1, 2026

Primary Completion (Estimated)

August 30, 2028

Study Completion (Estimated)

August 30, 2028

Study Registration Dates

First Submitted

August 19, 2026

First Submitted That Met QC Criteria

September 21, 2026

First Posted (Actual)

September 25, 2026

Study Record Updates

Last Update Posted (Actual)

October 30, 2026

Last Update Submitted That Met QC Criteria

October 28, 2026

Last Verified

October 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2026-S0835

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

IPD Plan Description

Individual Participant Data (IPD) will not be shared in this study. The primary reasons are threefold: (1) strict legal restrictions under China's Data Security Law and Personal Information Protection Law prohibit cross-institutional transfer of raw medical data without explicit consent; (2) as a retrospective study spanning several years, obtaining renewed consent from all participants is practically infeasible; and (3) the core federated learning framework inherently eliminates the need for IPD sharing-only encrypted model parameters are transmitted, while raw CT images and clinical data remain securely stored at each participating center. This design fully protects patient privacy, complies with all regulatory requirements, and ensures the study's collaborative objectives are achieved without ever exposing individual-level data.

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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