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Federated Learning in Renal Tumor Model

28 oktober 2026 uppdaterad 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.

Studieöversikt

Status

Har inte rekryterat ännu

Betingelser

Studietyp

Observationell

Inskrivning (Beräknad)

3000

Kontakter och platser

Det här avsnittet innehåller kontaktuppgifter för dem som genomför studien och information om var denna studie genomförs.

Studiekontakt

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

Studera Kontakt Backup

Studieorter

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

Deltagandekriterier

Forskare letar efter personer som passar en viss beskrivning, så kallade behörighetskriterier. Några exempel på dessa kriterier är en persons allmänna hälsotillstånd eller tidigare behandlingar.

Urvalskriterier

Åldrar som är berättigade till studier

18 år till 60 år (Vuxen)

Tar emot friska volontärer

Nej

Kön som är behöriga för studier

Allt

Testmetod

Sannolikhetsprov

Studera befolkning

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.

Beskrivning

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

Det här avsnittet ger detaljer om studieplanen, inklusive hur studien är utformad och vad studien mäter.

Hur är studien utformad?

Designdetaljer

  • Observationsmodeller: CASE_CONTROL
  • Tidsperspektiv: OTHER

Kohorter och interventioner

Grupp / Kohort
Malignant renal tumor
Benign renal tumor

Vad mäter studien?

Primära resultatmått

Resultatmått
Åtgärdsbeskrivning
Tidsram
Dice Similarity Coefficient for Renal Tumor Segmentation
Tidsram: 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.

Sekundära resultatmått

Resultatmått
Åtgärdsbeskrivning
Tidsram
Classification Performance Metrics and Heterogeneity Impact Assessment
Tidsram: 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

Samarbetspartners och utredare

Det är här du hittar personer och organisationer som är involverade i denna studie.

Sponsor

First Affiliated Hospital of Guangxi Medical University

Utredare

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

Publikationer och användbara länkar

Den som ansvarar för att lägga in information om studien tillhandahåller frivilligt dessa publikationer. Dessa kan handla om allt som har med studien att göra.

Allmänna publikationer

Studieavstämningsdatum

Dessa datum spårar framstegen för inlämningar av studieposter och sammanfattande resultat till ClinicalTrials.gov. Studieposter och rapporterade resultat granskas av National Library of Medicine (NLM) för att säkerställa att de uppfyller specifika kvalitetskontrollstandarder innan de publiceras på den offentliga webbplatsen.

Studera stora datum

Studiestart (Beräknad)

1 oktober 2026

Primärt slutförande (Beräknad)

30 augusti 2028

Avslutad studie (Beräknad)

30 augusti 2028

Studieregistreringsdatum

Först inskickad

19 augusti 2026

Först inskickad som uppfyllde QC-kriterierna

21 september 2026

Första postat (Faktisk)

25 september 2026

Uppdateringar av studier

Senaste uppdatering publicerad (Faktisk)

30 oktober 2026

Senaste inskickade uppdateringen som uppfyllde QC-kriterierna

28 oktober 2026

Senast verifierad

1 oktober 2026

Mer information

Termer relaterade till denna studie

Nyckelord

Ytterligare relevanta MeSH-villkor

Andra studie-ID-nummer

  • 2026-S0835

Plan för individuella deltagardata (IPD)

Planerar du att dela individuella deltagardata (IPD)?

NEJ

IPD-planbeskrivning

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.

Läkemedels- och apparatinformation, studiedokument

Studerar en amerikansk FDA-reglerad läkemedelsprodukt

Nej

Studerar en amerikansk FDA-reglerad produktprodukt

Nej

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