Federated Learning in Renal Tumor Model
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.
연구 개요
상태
상태
정황
정황
연구 유형
연구 유형
등록 (추정된)
등록
연락처 및 위치
연구 연락처
연구 연락처
- 이름: Hai-qi Liang, MD
- 전화번호: 86+18176618137
- 이메일: liahiki@163.com
연구 연락처 백업
- 이름: Nai-kai Liao, MD
- 전화번호: 86+18776891703
- 이메일: 2546419718@qq.com
연구 장소
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Guangxi
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Guigang, Guangxi, 중국, 537100
- Guigang People's Hospital
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연락하다:
- Jiale Feng, MD
- 전화번호: 86+17695684196
- 이메일: 15498419548@qq.com
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Guilin, Guangxi, 중국, 541002
- Guilin People's Hospital
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연락하다:
- Ke Qin, MD
- 전화번호: 86+15954268457
- 이메일: QINKE541002@163.com
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Liuzhou, Guangxi, 중국, 545006
- Liuzhou People's Hospital
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연락하다:
- Jianwen Yang, MD
- 전화번호: 86+13675215625
- 이메일: JWyang235@163.com
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Nanning, Guangxi, 중국, 530021
- The First Affiliated Hospital of Guangxi Medical University
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연락하다:
- Ji-wen Cheng, PhD
- 전화번호: 86+18776891703
- 이메일: chengjiwen@stu.gxmu.edu.cn
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연락하다:
- Haiqi Liang, MD
- 전화번호: 86+18176618137
- 이메일: liahiki@163.com
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부수사관:
- Shenghua Deng, MD
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Nanning, Guangxi, 중국, 530021
- Nanning Second People's Hospital
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연락하다:
- Yang Xie, MD
- 전화번호: 86+18346826751
- 이메일: XieYNan@163.com
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Wuzhou, Guangxi, 중국, 543002
- Wuzhou Red Cross Hospital
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연락하다:
- Deyan Ou, MD
- 전화번호: 86+18675651254
- 이메일: OudYan1254@163.com
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참여기준
자격 기준
자격 기준
공부할 수 있는 나이
건강한 자원 봉사자를 받아들입니다
연구 대상 성별
샘플링 방법
연구 인구
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.
설명
Inclusion Criteria:
- 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.
Exclusion Criteria:
- 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.
공부 계획
연구는 어떻게 설계됩니까?
디자인 세부사항
- 관찰 모델: CASE_CONTROL
- 시간 관점: OTHER
그룹/코호트 수
코호트 및 개입
그룹/코호트그룹/코호트 |
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Malignant renal tumor
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Benign renal tumor
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연구는 무엇을 측정합니까?
주요 결과 측정
주요 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
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Dice Similarity Coefficient for Renal Tumor Segmentation
기간: 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.
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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.
|
2차 결과 측정
2차 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
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Classification Performance Metrics and Heterogeneity Impact Assessment
기간: 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
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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
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공동 작업자 및 조사자
스폰서
스폰서
수사관
수사관
- 연구 책임자: Min Qin, MD, First Affiliated Hospital of Guangxi Medical University
간행물 및 유용한 링크
일반 간행물
- 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.
- 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.
- 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.
연구 기록 날짜
연구 주요 날짜
연구 시작 (추정된)
연구 시작
기본 완료 (추정된)
기본 완료
연구 완료 (추정된)
연구 완료
연구 등록 날짜
최초 제출
최초 제출
QC 기준을 충족하는 최초 제출
QC 기준을 충족하는 최초 제출
처음 게시됨 (실제)
처음 게시됨
연구 기록 업데이트
마지막 업데이트 게시됨 (실제)
마지막 업데이트 게시됨
QC 기준을 충족하는 마지막 업데이트 제출
QC 기준을 충족하는 마지막 업데이트 제출
마지막으로 확인됨
마지막으로 확인됨
추가 정보
이 연구와 관련된 용어
추가 관련 MeSH 약관
기타 연구 ID 번호
기타 연구 ID 번호
- 2026-S0835
개별 참가자 데이터(IPD) 계획
개별 참가자 데이터(IPD)를 공유할 계획입니까?
IPD 계획 설명
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.
약물 및 장치 정보, 연구 문서
미국 FDA 규제 의약품 연구
미국 FDA 규제 기기 제품 연구
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