Renal TumorsNCT07841756
Federated Learning in Renal Tumor Model
An observational study of Renal Tumors, run by First Affiliated Hospital of Guangxi Medical University at 6 sites in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 60 years
- Conditions the study is for
- Renal Tumors
- How many people
- 3,000 participants
- Where
- 6 sitesChina
- Study dates
- 1 Oct 2026to 30 Aug 2028
- How long will I take part?
- 2 Years
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 60 years
- All sexes
- No healthy volunteers
- Adult
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.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
6 sites in 1 country. Contact the study team or a site near you to ask about taking part.
6 sites in 1 country
China6 sites
Guigang People's Hospital
Guigang, Guangxi, 537100
Jiale Feng, MD, 86+17695684196, 15498419548@qq.com
Guilin People's Hospital
Guilin, Guangxi, 541002
Ke Qin, MD, 86+15954268457, QINKE541002@163.com
Liuzhou People's Hospital
Liuzhou, Guangxi, 545006
Jianwen Yang, MD, 86+13675215625, JWyang235@163.com
Nanning Second People's Hospital
Nanning, Guangxi, 530021
Yang Xie, MD, 86+18346826751, XieYNan@163.com
The First Affiliated Hospital of Guangxi Medical University
Nanning, Guangxi, 530021
Ji-wen Cheng, PhD, 86+18776891703, chengjiwen@stu.gxmu.edu.cn
Haiqi Liang, MD, 86+18176618137, liahiki@163.com
Wuzhou Red Cross Hospital
Wuzhou, Guangxi, 543002
Deyan Ou, MD, 86+18675651254, OudYan1254@163.com
About this study
Official title: 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.
- Conditions
- Renal Tumors
- Keywords
- renal tumors, multi-center, medical image, pathology, Genomics, multimodal
- Medical subject headings
- Carcinoma, Renal Cell
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Malignant renal tumor | Not described | |
| Benign renal tumor | Not described |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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
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.
Measured: 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
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Sep 2026
- Study start1 Oct 2026 (estimated)
- Primary completion30 Aug 2028 (estimated)
- Study completion30 Aug 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for Renal TumorsStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Other
- Enrolment
- 3,000 participants (estimated)
- Target duration
- 2 Years
Sponsor and investigators
- Lead sponsor
- First Affiliated Hospital of Guangxi Medical University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Jiwen Cheng, First Affiliated Hospital of Guangxi Medical University
- Investigators
- Min Qin, MD, Study director, First Affiliated Hospital of Guangxi Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07841756
- Sponsor's protocol ID
- 2026-S0835
- Status last verified
- Oct 2026
- First submitted
- 19 Aug 2026
- Last updated
- 30 Oct 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: 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.
Publications
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.
PubMed 31937619Bex 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.
PubMed 40118739Sung 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.
PubMed 33538338
Study at a glance
From the registry record, last updated by the sponsor on 30 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Research on the Application of Federated Learning in the Construction of Deep Learning Models for Renal Tumor Imaging
- ClinicalTrials.gov ID
- NCT07841756
- Acronym / sponsor ID
- 2026-S0835
- Conditions
- Renal Tumors
- Keywords
- renal tumors, multi-center, medical image, pathology, Genomics, multimodal
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 3,000 participants (estimated)
- Lead sponsor
- First Affiliated Hospital of Guangxi Medical UniversityUniversity, hospital or other
- Study start
- 1 Oct 2026 (estimated)
- Primary completion
- 30 Aug 2028 (estimated)
- Study completion
- 30 Aug 2028 (estimated)
- First posted
- 25 Sep 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Case-control
- Time perspective
- Other
- Target follow-up
- 2 Years
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Malignant renal tumor | – | – |
| Benign renal tumor | – | – |
Who may qualify
- Ages
- 18 to 60 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult
- 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.
Key inclusion criteria (4 listed)
- 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.
Key exclusion criteria (5 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Dice Similarity Coefficient for Renal Tumor Segmentation
Time frame: 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.
1 secondary outcome
Classification Performance Metrics and Heterogeneity Impact Assessment
Time frame: 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
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 30 Oct 2026.
- Background: PubMed 31937619Xi 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.
- Background: PubMed 40118739Bex 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.
- Background: PubMed 33538338Sung 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.
- Individual participant dataSharing planned: No. 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Min Qin, MD study director
First Affiliated Hospital of Guangxi Medical University
6 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Renal Tumors, run by First Affiliated Hospital of Guangxi Medical University at 6 sites in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 30 Oct 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 6 sites in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Hai-qi Liang, MD (86+18176618137, liahiki@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Dice Similarity Coefficient for Renal Tumor Segmentation (time frame: 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.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07841756, which also lists 3 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 30 Oct 2026.