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Federated Learning in Renal Tumor Model

28. oktober 2026 oppdatert av: 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.

Studieoversikt

Status

Har ikke rekruttert ennå

Forhold

Studietype

Observasjonsmessig

Registrering (Antatt)

3000

Kontakter og plasseringer

Denne delen inneholder kontaktinformasjon for de som utfører studien, og informasjon om hvor denne studien blir utført.

Studiekontakt

  • Navn: Hai-qi Liang, MD
  • Telefonnummer: 86+18176618137
  • E-post: liahiki@163.com

Studer Kontakt Backup

Studiesteder

  • Kina
    • Guangxi
      • Guigang, Guangxi, Kina, 537100
        • Guigang People's Hospital
        • Ta kontakt med:
      • Guilin, Guangxi, Kina, 541002
        • Guilin People's Hospital
        • Ta kontakt med:
      • Liuzhou, Guangxi, Kina, 545006
        • Liuzhou People's Hospital
        • Ta kontakt med:
      • Nanning, Guangxi, Kina, 530021
        • The First Affiliated Hospital of Guangxi Medical University
        • Ta kontakt med:
        • Ta kontakt med:
        • Underetterforsker:
          • Shenghua Deng, MD
      • Nanning, Guangxi, Kina, 530021
        • Nanning Second People's Hospital
        • Ta kontakt med:
      • Wuzhou, Guangxi, Kina, 543002
        • Wuzhou Red Cross Hospital
        • Ta kontakt med:

Deltakelseskriterier

Forskere ser etter personer som passer til en bestemt beskrivelse, kalt kvalifikasjonskriterier. Noen eksempler på disse kriteriene er en persons generelle helsetilstand eller tidligere behandlinger.

Kvalifikasjonskriterier

Alder som er kvalifisert for studier

18 år til 60 år (Voksen)

Tar imot friske frivillige

Nei

Kjønn som er kvalifisert for studier

Alle

Prøvetakingsmetode

Sannsynlighetsprøve

Studiepopulasjon

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.

Beskrivelse

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.

Studieplan

Denne delen gir detaljer om studieplanen, inkludert hvordan studien er utformet og hva studien måler.

Hvordan er studiet utformet?

Designdetaljer

  • Observasjonsmodeller: CASE_CONTROL
  • Tidsperspektiver: OTHER

Kohorter og intervensjoner

Gruppe / Kohort
Malignant renal tumor
Benign renal tumor

Hva måler studien?

Primære resultatmål

Resultatmål
Tiltaksbeskrivelse
Tidsramme
Dice Similarity Coefficient for Renal Tumor Segmentation
Tidsramme: 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.

Sekundære resultatmål

Resultatmål
Tiltaksbeskrivelse
Tidsramme
Classification Performance Metrics and Heterogeneity Impact Assessment
Tidsramme: 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

Samarbeidspartnere og etterforskere

Det er her du vil finne personer og organisasjoner som er involvert i denne studien.

Sponsor

First Affiliated Hospital of Guangxi Medical University

Etterforskere

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

Publikasjoner og nyttige lenker

Den som er ansvarlig for å legge inn informasjon om studien leverer frivillig disse publikasjonene. Disse kan handle om alt relatert til studiet.

Generelle publikasjoner

Studierekorddatoer

Disse datoene sporer fremdriften for innsending av studieposter og sammendragsresultater til ClinicalTrials.gov. Studieposter og rapporterte resultater gjennomgås av National Library of Medicine (NLM) for å sikre at de oppfyller spesifikke kvalitetskontrollstandarder før de legges ut på det offentlige nettstedet.

Studer hoveddatoer

Studiestart (Antatt)

1. oktober 2026

Primær fullføring (Antatt)

30. august 2028

Studiet fullført (Antatt)

30. august 2028

Datoer for studieregistrering

Først innsendt

19. august 2026

Først innsendt som oppfylte QC-kriteriene

21. september 2026

Først lagt ut (Faktiske)

25. september 2026

Oppdateringer av studieposter

Sist oppdatering lagt ut (Faktiske)

30. oktober 2026

Siste oppdatering sendt inn som oppfylte QC-kriteriene

28. oktober 2026

Sist bekreftet

1. oktober 2026

Mer informasjon

Begreper knyttet til denne studien

Nøkkelord

Ytterligere relevante MeSH-vilkår

Andre studie-ID-numre

  • 2026-S0835

Plan for individuelle deltakerdata (IPD)

Planlegger du å dele individuelle deltakerdata (IPD)?

NEI

IPD-planbeskrivelse

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.

Legemiddel- og utstyrsinformasjon, studiedokumenter

Studerer et amerikansk FDA-regulert medikamentprodukt

Nei

Studerer et amerikansk FDA-regulert enhetsprodukt

Nei

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