Federated Learning in Renal Tumor Model

NCT07841756 | Not Yet Recruiting DMC

• 1 other identifier: 2026-S0835
• Study Type: observational
• Target: 3,000
• Locations: 1 country
• Sites: 6

Brief Summary

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

Renal Tumors

Keywords

renal tumors; multi-center; medical image; pathology; Genomics; multimodal

Outcome Measures

Primary Outcomes (1)

• Dice Similarity Coefficient for Renal Tumor Segmentation

  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 Outcomes (1)

• Classification Performance Metrics and Heterogeneity Impact Assessment

  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

Study Arms (2)

Malignant renal tumor

Benign renal tumor

Eligibility Criteria

• Age: 18 Years - 60 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64)
• 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.

You may qualify if:

• Patients pathologically confirmed as having renal tumors (either benign or malignant) by surgery or biopsy;
• Underwent contrastenhanced renal CT before surgery or treatment;
• CT images are of good quality and clearly depict the renal lesion contour;
• Complete and traceable clinical and pathological data.

You may not qualify if:

• Patients who did not undergo contrastenhanced CT before surgery, or only had noncontrast CT;
• CT images with significant motion artifacts, metal artifacts, or excessive noise that impair lesion assessment;
• Lesions too small (maximum diameter \<1 mm) or not identifiable on imaging;
• Patients who previously underwent partial or radical nephrectomy for renal tumors (except for recurrent/residual lesions);
• Incomplete clinical data or pathological results.

Sponsors & Collaborators

• First Affiliated Hospital of Guangxi Medical University: lead

Study Sites (6)

Guigang People's Hospital

Guigang, Guangxi, 537100, China

Location

Guilin People's Hospital

Guilin, Guangxi, 541002, China

Location

Liuzhou People's Hospital

Liuzhou, Guangxi, 545006, China

Location

Nanning Second People's Hospital

Nanning, Guangxi, 530021, China

Location

The First Affiliated Hospital of Guangxi Medical University

Nanning, Guangxi, 530021, China

Location

Wuzhou Red Cross Hospital

Wuzhou, Guangxi, 543002, China

Location

Related Publications (3)

• Xi 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 BACKGROUND

• Bex 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 BACKGROUND

• Sung 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 BACKGROUND

MeSH Terms

Conditions

Carcinoma, Renal Cell

Condition Hierarchy (Ancestors)

Adenocarcinoma; Carcinoma; Neoplasms, Glandular and Epithelial; Neoplasms by Histologic Type; Neoplasms; Kidney Neoplasms; Urologic Neoplasms; Urogenital Neoplasms; Neoplasms by Site; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Kidney Diseases; Urologic Diseases; Male Urogenital Diseases

Study Officials

• Min Qin, MD

  First Affiliated Hospital of Guangxi Medical University

  STUDY DIRECTOR

Central Study Contacts

Hai-qi Liang, MD

CONTACT

86+18176618137; liahiki@163.com

Nai-kai Liao, MD

CONTACT

86+18776891703; 2546419718@qq.com

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: OTHER
• Target Duration: 2 Years
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Vice president of the First Affiliated Hospital of Guangxi Medical University

Study Record Dates

• First Submitted: August 19, 2026
• First Posted: September 25, 2026
• Study Start: September 1, 2026
• Primary Completion (Estimated): August 30, 2028
• Study Completion (Estimated): August 30, 2028
• Last Updated: September 25, 2026

Record last verified: 2026-09

Data Sharing

• IPD Sharing: Will not share

Locations

CN (6)