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Federated Learning in Renal Tumor Model

28 oktober 2026 bijgewerkt door: 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.

Studie Overzicht

Toestand

Nog niet aan het werven

Conditie

Studietype

Observationeel

Inschrijving (Geschat)

3000

Contacten en locaties

In dit gedeelte vindt u de contactgegevens van degenen die het onderzoek uitvoeren en informatie over waar dit onderzoek wordt uitgevoerd.

Studiecontact

  • Naam: Hai-qi Liang, MD
  • Telefoonnummer: 86+18176618137
  • E-mail: liahiki@163.com

Studie Contact Back-up

Studie Locaties

  • 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:
        • Onderonderzoeker:
          • Shenghua Deng, MD
      • Nanning, Guangxi, China, 530021
        • Nanning Second People's Hospital
        • Contact:
      • Wuzhou, Guangxi, China, 543002
        • Wuzhou Red Cross Hospital
        • Contact:

Deelname Criteria

Onderzoekers zoeken naar mensen die aan een bepaalde beschrijving voldoen, de zogenaamde geschiktheidscriteria. Enkele voorbeelden van deze criteria zijn iemands algemene gezondheidstoestand of eerdere behandelingen.

Geschiktheidscriteria

Leeftijden die in aanmerking komen voor studie

18 jaar tot 60 jaar (Volwassen)

Accepteert gezonde vrijwilligers

Nee

Geslachten die in aanmerking komen voor studie

Allemaal

Bemonsteringsmethode

Kanssteekproef

Studie Bevolking

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.

Beschrijving

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.

Studie plan

Dit gedeelte bevat details van het studieplan, inclusief hoe de studie is opgezet en wat de studie meet.

Hoe is de studie opgezet?

Ontwerpdetails

  • Observatiemodellen: CASE_CONTROL
  • Tijdsperspectieven: OTHER

Cohorten en interventies

Groep / Cohort
Malignant renal tumor
Benign renal tumor

Wat meet het onderzoek?

Primaire uitkomstmaten

Uitkomstmaat
Maatregel Beschrijving
Tijdsspanne
Dice Similarity Coefficient for Renal Tumor Segmentation
Tijdsspanne: 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.

Secundaire uitkomstmaten

Uitkomstmaat
Maatregel Beschrijving
Tijdsspanne
Classification Performance Metrics and Heterogeneity Impact Assessment
Tijdsspanne: 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

Medewerkers en onderzoekers

Hier vindt u mensen en organisaties die betrokken zijn bij dit onderzoek.

Sponsor

First Affiliated Hospital of Guangxi Medical University

Onderzoekers

  • Studie directeur: Min Qin, MD, First Affiliated Hospital of Guangxi Medical University

Publicaties en nuttige links

De persoon die verantwoordelijk is voor het invoeren van informatie over het onderzoek stelt deze publicaties vrijwillig ter beschikking. Dit kan gaan over alles wat met het onderzoek te maken heeft.

Algemene publicaties

Studie record data

Deze datums volgen de voortgang van het onderzoeksdossier en de samenvatting van de ingediende resultaten bij ClinicalTrials.gov. Studieverslagen en gerapporteerde resultaten worden beoordeeld door de National Library of Medicine (NLM) om er zeker van te zijn dat ze voldoen aan specifieke kwaliteitscontrolenormen voordat ze op de openbare website worden geplaatst.

Bestudeer belangrijke data

Studie start (Geschat)

1 oktober 2026

Primaire voltooiing (Geschat)

30 augustus 2028

Studie voltooiing (Geschat)

30 augustus 2028

Studieregistratiedata

Eerst ingediend

19 augustus 2026

Eerst ingediend dat voldeed aan de QC-criteria

21 september 2026

Eerst geplaatst (Werkelijk)

25 september 2026

Updates van studierecords

Laatste update geplaatst (Werkelijk)

30 oktober 2026

Laatste update ingediend die voldeed aan QC-criteria

28 oktober 2026

Laatst geverifieerd

1 oktober 2026

Meer informatie

Termen gerelateerd aan deze studie

Trefwoorden

Aanvullende relevante MeSH-voorwaarden

Andere studie-ID-nummers

  • 2026-S0835

Plan Individuele Deelnemersgegevens (IPD)

Bent u van plan om gegevens van individuele deelnemers (IPD) te delen?

NEE

Beschrijving IPD-plan

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.

Informatie over medicijnen en apparaten, studiedocumenten

Bestudeert een door de Amerikaanse FDA gereguleerd geneesmiddel

Nee

Bestudeert een door de Amerikaanse FDA gereguleerd apparaatproduct

Nee

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