Kidney Diseases, ChronicNCT07816874
Model Development and Application Based on Renal Pathology Images
An observational study of Kidney Diseases, Chronic and Glomerular Diseases, run by Union Hospital, Tongji Medical College, Huazhong University of Science and Technology at 1 site in China.
Trial summary
This single-center retrospective observational cohort study will utilize digital renal pathology images along with corresponding clinical and laboratory data from patients who underwent kidney biopsy at Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, between January 1, 2019, and… Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Kidney Diseases, Chronic, Glomerular Diseases, Tubulointerstitial Kidney Diseases
- How many people
- 8,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2019to 10 May 2027
- 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 Any age
- All sexes
- Child, Adult, Older adult
Study population: The study population will comprise patients who underwent native kidney biopsy at Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, between January 1, 2019, and March 31, 2026. Eligible cases will be retrospectively identified from the hospital's electronic medical record and pathology image systems. Patients with biopsy-confirmed kidney diseases and available digital renal pathology images and corresponding clinical and laboratory data will be included for model development and validation. Kidney allograft biopsy cases will not be included.
Who may be able to join
- Patients with kidney disease confirmed by kidney biopsy.
- Availability of complete digital renal pathology images, including light microscopy, immunofluorescence microscopy, and/or electron microscopy images, as required for the relevant diagnostic task.
- Availability of the clinical and laboratory data required for the planned analyses.
Who cannot join
- Renal pathology images of insufficient quality for analysis.
- Missing key clinical data required for the planned analyses.
- Kidney allograft biopsy specimens.
Trial locations and contacts
1 site in 1 country.
China1 site
Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Wuhan, Hubei, 430000
About this study
This single-center retrospective observational cohort study will utilize digital renal pathology images along with corresponding clinical and laboratory data from patients who underwent kidney biopsy at Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, between January 1, 2019, and March 31, 2026. Deep learning and image analysis techniques will be employed to develop and validate models for the pathological diagnosis and classification of kidney diseases. The study will also assess model performance in evaluating disease activity and chronicity, as well as investigate the association between image-derived features and long-term renal outcomes. Diagnoses will be established by consensus among three senior renal pathologists, who will serve as the reference standard. A minimum of 8,000 eligible cases are planned for inclusion, which will be divided into training, validation, and independent test sets.
- Conditions
- Kidney Diseases, Chronic, Glomerular Diseases, Tubulointerstitial Kidney Diseases, IgA Nephropathy (IgAN), Membranous Nephropathy
- Medical subject headings
- Renal Insufficiency, Chronic, Glomerulonephritis, IGA, Glomerulonephritis, Membranous
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic classification accuracy of the deep learning model, measured as the percentage of correctly classified renal pathology cases
Diagnostic classification accuracy will be assessed in the independent test set by comparing the renal disease classification predicted by the prespecified deep learning model with the consensus diagnosis established by three senior renal pathologists. Accuracy will be calculated as the number of correctly classified cases divided by the total number of evaluable cases and reported as a percentage (%). The measurement tool will be the prespecified deep learning model evaluated against the expert consensus reference standard.
Measured: At completion of independent test-set evaluation using retrospective data collected from January 1, 2019, through March 31, 2026.
Secondary outcomes (1)
Agreement between deep learning model predictions and expert consensus diagnoses, measured by the Cohen kappa coefficient
Agreement between the renal disease classifications generated by the prespecified deep learning model and the consensus diagnoses established by three senior renal pathologists will be assessed using the Cohen kappa coefficient. The measurement tool will be a prespecified categorical agreement analysis comparing model-predicted classifications with the expert consensus reference standard. The Cohen kappa coefficient is a unitless measure, with higher values indicating greater agreement beyond chance.
Measured: At completion of independent test-set evaluation using retrospective data collected from January 1, 2019, through March 31, 2026.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted14 Sep 2026
- Study start1 Jan 2019
- Primary completion31 Mar 2026
- Study completion10 May 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Kidney Diseases, ChronicStudies 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
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 8,000 participants (actual)
Sponsor and investigators
- Lead sponsor
- Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07816874
- Sponsor's protocol ID
- Snk202604
- Status last verified
- Aug 2026
- First submitted
- 30 Aug 2026
- Last updated
- 14 Sep 2026
- Results posted
- No
Study at a glance
From the registry record, last updated by the sponsor on 14 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Model Development and Application Based on Renal Pathology Images
- ClinicalTrials.gov ID
- NCT07816874
- Acronym / sponsor ID
- Snk202604
- Conditions
- Kidney Diseases, Chronic, Glomerular Diseases, Tubulointerstitial Kidney Diseases, IgA Nephropathy (IgAN), Membranous Nephropathy
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 8,000 participants (actual)
- Lead sponsor
- Union Hospital, Tongji Medical College, Huazhong University of Science and TechnologyUniversity, hospital or other
- Study start
- 1 Jan 2019 (actual)
- Primary completion
- 31 Mar 2026 (actual)
- Study completion
- 10 May 2027 (estimated)
- First posted
- 14 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 single-center retrospective observational cohort study will utilize digital renal pathology images along with corresponding clinical and laboratory data from patients who underwent kidney biopsy at Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, between January 1, 2019, and March 31, 2026. Deep learning and image analysis techniques will be employed to develop and validate models for the pathological diagnosis and classification of kidney diseases. The study will also assess model performance in evaluating disease activity and chronicity, as well as investigate the association between image-derived features and long-term renal outcomes. Diagnoses will be established by consensus among three senior renal pathologists, who will serve as the reference standard. A minimum of 8,000 eligible cases are planned for inclusion, which will be divided into training, validation, and independent test sets.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Sex
- All
- Age groups
- Child, Adult, Older adult
- Study population
- The study population will comprise patients who underwent native kidney biopsy at Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, between January 1, 2019, and March 31, 2026. Eligible cases will be retrospectively identified from the hospital's electronic medical record and pathology image systems. Patients with biopsy-confirmed kidney diseases and available digital renal pathology images and corresponding clinical and laboratory data will be included for model development and validation. Kidney allograft biopsy cases will not be included.
Key inclusion criteria (3 listed)
- Patients with kidney disease confirmed by kidney biopsy.
- Availability of complete digital renal pathology images, including light microscopy, immunofluorescence microscopy, and/or electron microscopy images, as required for the relevant diagnostic task.
- Availability of the clinical and laboratory data required for the planned analyses.
Key exclusion criteria (3 listed)
- Renal pathology images of insufficient quality for analysis.
- Missing key clinical data required for the planned analyses.
- Kidney allograft biopsy specimens.
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
Diagnostic classification accuracy of the deep learning model, measured as the percentage of correctly classified renal pathology cases
Time frame: At completion of independent test-set evaluation using retrospective data collected from January 1, 2019, through March 31, 2026.
Diagnostic classification accuracy will be assessed in the independent test set by comparing the renal disease classification predicted by the prespecified deep learning model with the consensus diagnosis established by three senior renal pathologists. Accuracy will be calculated as the number of correctly classified cases divided by the total number of evaluable cases and reported as a percentage (%). The measurement tool will be the prespecified deep learning model evaluated against the expert consensus reference standard.
1 secondary outcome
Agreement between deep learning model predictions and expert consensus diagnoses, measured by the Cohen kappa coefficient
Time frame: At completion of independent test-set evaluation using retrospective data collected from January 1, 2019, through March 31, 2026.
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 14 Sep 2026.
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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. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Kidney Diseases, Chronic and Glomerular Diseases, run by Union Hospital, Tongji Medical College, Huazhong University of Science and Technology at 1 site 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 3 inclusion and 3 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: under way, not looking for participants, as of the sponsor's last update on 14 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site 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?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic classification accuracy of the deep learning model, measured as the percentage of correctly classified renal pathology cases (time frame: At completion of independent test-set evaluation using retrospective data collected from January 1, 2019, through March 31, 2026.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 14 Sep 2026.