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Federated Learning in Renal Tumor Model

2026年10月28日 更新者：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.

研究概览

地位

尚未招聘

条件

研究类型

观察性的

注册 (估计的)

3000

联系人和位置

本节提供了进行研究的人员的详细联系信息，以及有关进行该研究的地点的信息。

学习联系方式

  • 姓名：Hai-qi Liang, MD
  • 电话号码：86+18176618137
  • 邮箱：liahiki@163.com

研究联系人备份

学习地点

  • 中国
    • Guangxi
      • Guigang、Guangxi、中国、537100
        • Guigang People's Hospital
        • 接触：
      • Guilin、Guangxi、中国、541002
        • Guilin People's Hospital
        • 接触：
      • Liuzhou、Guangxi、中国、545006
        • Liuzhou People's Hospital
        • 接触：
      • Nanning、Guangxi、中国、530021
        • The First Affiliated Hospital of Guangxi Medical University
        • 接触：
        • 接触：
        • 副研究员：
          • Shenghua Deng, MD
      • Nanning、Guangxi、中国、530021
        • Nanning Second People's Hospital
        • 接触：
      • Wuzhou、Guangxi、中国、543002
        • Wuzhou Red Cross Hospital
        • 接触：

参与标准

研究人员寻找符合特定描述的人，称为资格标准。这些标准的一些例子是一个人的一般健康状况或先前的治疗。

资格标准

适合学习的年龄

18年 至 60年 (成人)

接受健康志愿者

不

有资格学习的性别

全部

取样方法

概率样本

研究人群

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.

描述

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.

学习计划

本节提供研究计划的详细信息，包括研究的设计方式和研究的衡量标准。

研究是如何设计的？

设计细节

  • 观测模型：CASE_CONTROL
  • 时间观点：OTHER

队列和干预

团体/队列
Malignant renal tumor
Benign renal tumor

研究衡量的是什么？

主要结果指标

结果测量
措施说明
大体时间
Dice Similarity Coefficient for Renal Tumor Segmentation
大体时间：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.

次要结果测量

结果测量
措施说明
大体时间
Classification Performance Metrics and Heterogeneity Impact Assessment
大体时间：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

合作者和调查者

在这里您可以找到参与这项研究的人员和组织。

赞助

First Affiliated Hospital of Guangxi Medical University

调查人员

  • 研究主任：Min Qin, MD、First Affiliated Hospital of Guangxi Medical University

出版物和有用的链接

负责输入研究信息的人员自愿提供这些出版物。这些可能与研究有关。

一般刊物

研究记录日期

这些日期跟踪向 ClinicalTrials.gov 提交研究记录和摘要结果的进度。研究记录和报告的结果由国家医学图书馆 (NLM) 审查，以确保它们在发布到公共网站之前符合特定的质量控制标准。

研究主要日期

学习开始 (估计的)

2026年10月1日

初级完成 (估计的)

2028年8月30日

研究完成 (估计的)

2028年8月30日

研究注册日期

首次提交

2026年8月19日

首先提交符合 QC 标准的

2026年9月21日

首次发布 (实际的)

2026年9月25日

研究记录更新

最后更新发布 (实际的)

2026年10月30日

上次提交的符合 QC 标准的更新

2026年10月28日

最后验证

2026年10月1日

更多信息

与本研究相关的术语

关键字

其他相关的 MeSH 术语

其他研究编号

  • 2026-S0835

计划个人参与者数据 (IPD)

计划共享个人参与者数据 (IPD)？

不

IPD 计划说明

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.

药物和器械信息、研究文件

研究美国 FDA 监管的药品

不

研究美国 FDA 监管的设备产品

不

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