Graft RejectionNCT07741552
Artificial Intelligence Model for Renal Allograft Rejection Identification, Lesion Quantification and Rejection Risk Prediction Based on Prospective Renal Transplant Pathology and Clinical Multidimensional Data
An observational study of Graft Rejection and Kidney Transplantation, run by Chen Dajin at 1 site in China.
Trial summary
This prospective research collects leftover kidney biopsy tissue slides and matching routine clinical data from patients who received kidney transplants and underwent standard kidney puncture biopsy at Zhejiang University School of Medicine First Affiliated Hospital starting November 2025. Read more about this study
- Who is it for?
- Ages 18 to 75 years
- Conditions the study is for
- Graft Rejection, Kidney Transplantation, Glomerulonephritis
- How many people
- 1,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Nov 2025to 31 Oct 2030
- How long will I take part?
- 12 Months
- 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 75 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: This prospective observational study consecutively enrolls up to 1,000 eligible adult kidney transplant recipients aged 18-75 at The First Affiliated Hospital, Zhejiang University School of Medicine starting November 2025. Participants undergo clinically indicated renal allograft biopsy for monitoring or disease evaluation. The population includes cases with TCMR, ABMR, chronic rejection, polyomavirus nephropathy, recurrent primary renal disease and non-rejection controls. Only residual biopsy slides and anonymized clinical data are collected, with no additional invasive procedures. Subjects with poor specimens, incomplete data or unavailable follow-up records will be excluded. All participants provide informed consent and can withdraw freely. All identifiable information will be removed to protect privacy.
Who may be able to join
- Patients who received kidney transplantation and underwent clinically indicated renal allograft biopsy at The First Affiliated Hospital, Zhejiang University School of Medicine starting November 2025.
- Age range from 18 to 75 years old.
- Complete clinical baseline data, laboratory test records, medication history and pathological information available in electronic medical system.
- Residual paraffin biopsy slides are available after routine pathological examination.
- Voluntarily provide written informed consent for the use of residual pathological specimens and clinical data for research.
Who cannot join
- Poor-quality biopsy specimens including blurred staining, severe tissue damage or insufficient tissue volume which cannot support pathological image analysis.
- Severe missing core clinical variables that cannot be supplemented via standardized data imputation.
- Complicated with severe irreversible dysfunction of heart, liver, brain or other vital organs affecting long-term clinical follow-up data collection.
- Unable to complete routine clinical follow-up, resulting in unavailable outcome data for recurrent rejection labeling.
- Refuse to participate or withdraw informed consent.
Trial locations and contacts
1 site in 1 country.
China1 site
The First Affiliated Hospital, Zhejiang University School of Medicine
Hangzhou, Zhejiang
About this study
Official title: A Study on the Construction of Artificial Intelligence Recognition Model Based on Prospective Multidimensional Renal Transplant Pathology and Clinical Data for Accurate Differentiation of Rejection, Quantitative Lesion Assessment and Prediction of Re-recurrence Risk
This prospective research collects leftover kidney biopsy tissue slides and matching routine clinical data from patients who received kidney transplants and underwent standard kidney puncture biopsy at Zhejiang University School of Medicine First Affiliated Hospital starting November 2025. A total of around 1,000 patient samples will be included, covering transplant rejection (including TCMR and ABMR subtypes, acute and chronic rejection), polyomavirus infection and recurrent original kidney disease after transplantation.
All study materials come from residual biopsy specimens generated during regular clinical examinations, with no extra invasive operations, additional medical costs or physical trauma for participants. We will scan pathological slides into digital images and combine them with patients' medical records, lab test results, medication history and follow-up information. After full anonymization and standardized labeling by senior renal pathologists following the Banff standard, we will build an artificial intelligence (AI) multi-task model.
Read more
This AI system will serve three core clinical functions: accurately distinguish different types of transplant kidney lesions, quantitatively measure tissue damage caused by rejection, and predict the risk of recurrent rejection after surgery. We will optimize and verify the model's diagnostic accuracy, stability and reliability through dataset segmentation, cross validation and algorithm adjustment.
For patients, this study brings no extra physical or economic burden. If suspicious pathological changes are found during data analysis, relevant clues will be fed back to attending doctors to support individual treatment management. For clinical providers, the finished AI tool can reduce pathologists' reading workload, lower missed diagnosis and misdiagnosis caused by individual experience differences, especially improve detection of subclinical and borderline rejection. It helps clinicians evaluate injury severity and forecast recurrence risk, so as to formulate personalized immunosuppression regimens, reduce rejection relapse and prolong graft survival.
Strict privacy protection measures are implemented throughout the whole research process: all personal identifiable information will be completely removed, and encrypted classified data management is adopted to prevent information leakage. Every participant signs a written informed consent and retains the right to withdraw from the study at any time without affecting their regular medical care. All research procedures have passed ethical review supervision, and all collected data and specimens will be properly stored or destroyed in accordance with standardized medical management rules after the study ends.
The research aims to fill the gap of prospective multi-dimensional AI auxiliary diagnosis research in kidney transplantation, promote standardized, intelligent and precise post-transplant pathological evaluation, and provide new technical support to improve long-term survival outcomes of kidney transplant recipients.
- Medical subject headings
- Glomerulonephritis, Polyomavirus Infections
What is being measured
The primary outcome is the main question the study is designed to answer.
ROC AUC of multi-task AI model for identification of renal allograft lesions
Area under receiver operating characteristic curve to evaluate model ability to detect rejection subtypes, polyomavirus nephropathy and recurrent primary renal disease.
Measured: After model training and internal verification（through study completion, an average of 24 months）
Sensitivity and specificity of multi-task AI model for renal allograft lesion diagnosis
Sensitivity and specificity of the AI model discriminating TCMR, ABMR, polyomavirus nephropathy and recurrent primary renal disease.
Measured: After model training and internal verification（through study completion, an average of 24 months）
Diagnostic accuracy of multi-task AI model for renal allograft lesions
Overall accuracy of the AI model in differentiating post-transplant renal pathological lesions.
Measured: After model training and internal verification（through study completion, an average of 24 months）
Secondary outcomes (3)
ICC/Kappa consistency between AI quantitative scoring and pathologists' Banff evaluation
Agreement comparison between automated AI lesion quantification and manual Banff scoring by senior renal transplant pathologists.
Measured: After model training, optimization and internal test set verification（through study completion, an average of 24 months）
ROC AUC of AI sub-model for prediction of recurrent renal allograft rejection
AUC for the AI prediction model to identify patients at risk of subsequent recurrent rejection within 12 months after biopsy.
Measured: After extraction of 12-month routine follow-up data（through study completion, an average of 24 months）
Change in inter-pathologist diagnostic Kappa with AI model assistance
Comparison of pathologists' inter-observer consistency with and without auxiliary AI diagnosis of transplant kidney lesions.
Measured: After model training and internal verification（through study completion, an average of 24 months）
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted3 Aug 2026
- Study start1 Nov 2025
- Primary completion31 Oct 2030 (estimated)
- Study completion31 Oct 2030 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Graft RejectionStudies 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
- Other
- Time perspective
- Prospective
- Enrolment
- 1,000 participants (estimated)
- Target duration
- 12 Months
Sponsor and investigators
- Lead sponsor
- Chen Dajin
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Chen Dajin, Zhejiang University
- Investigators
- Dajin Chen, MD, PhD, Study chair, Zhejiang University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07741552
- Sponsor's protocol ID
- FAHZU-KT-AI-20260634
- Status last verified
- Jul 2026
- First submitted
- 23 Jul 2026
- Last updated
- 3 Aug 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 3 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Study on the Construction of Artificial Intelligence Recognition Model Based on Prospective Multidimensional Renal Transplant Pathology and Clinical Data for Accurate Differentiation of Rejection, Quantitative Lesion Assessment and Prediction of Re-recurrence Risk
- ClinicalTrials.gov ID
- NCT07741552
- Acronym / sponsor ID
- FAHZU-KT-AI-20260634
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Chen DajinUniversity, hospital or other
- Study start
- 1 Nov 2025 (actual)
- Primary completion
- 31 Oct 2030 (estimated)
- Study completion
- 31 Oct 2030 (estimated)
- First posted
- 3 Aug 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 prospective research collects leftover kidney biopsy tissue slides and matching routine clinical data from patients who received kidney transplants and underwent standard kidney puncture biopsy at Zhejiang University School of Medicine First Affiliated Hospital starting November 2025. A total of around 1,000 patient samples will be included, covering transplant rejection (including TCMR and ABMR subtypes, acute and chronic rejection), polyomavirus infection and recurrent original kidney disease after transplantation.
All study materials come from residual biopsy specimens generated during regular clinical examinations, with no extra invasive operations, additional medical costs or physical trauma for participants. We will scan pathological slides into digital images and combine them with patients' medical records, lab test results, medication history and follow-up information. After full anonymization and standardized labeling by senior renal pathologists following the Banff standard, we will build an artificial intelligence (AI) multi-task model.
This AI system will serve three core clinical functions: accurately distinguish different types of transplant kidney lesions, quantitatively measure tissue damage caused by rejection, and predict the risk of recurrent rejection after surgery. We will optimize and verify the model's diagnostic accuracy, stability and reliability through dataset segmentation, cross validation and algorithm adjustment.
For patients, this study brings no extra physical or economic burden. If suspicious pathological changes are found during data analysis, relevant clues will be fed back to attending doctors to support individual treatment management. For clinical providers, the finished AI tool can reduce pathologists' reading workload, lower missed diagnosis and misdiagnosis caused by individual experience differences, especially improve detection of subclinical and borderline rejection. It helps clinicians evaluate injury severity and forecast recurrence risk, so as to formulate personalized immunosuppression regimens, reduce rejection relapse and prolong graft survival.
Strict privacy protection measures are implemented throughout the whole research process: all personal identifiable information will be completely removed, and encrypted classified data management is adopted to prevent information leakage. Every participant signs a written informed consent and retains the right to withdraw from the study at any time without affecting their regular medical care. All research procedures have passed ethical review supervision, and all collected data and specimens will be properly stored or destroyed in accordance with standardized medical management rules after the study ends.
The research aims to fill the gap of prospective multi-dimensional AI auxiliary diagnosis research in kidney transplantation, promote standardized, intelligent and precise post-transplant pathological evaluation, and provide new technical support to improve long-term survival outcomes of kidney transplant recipients.
Study design
- Observational model
- Other
- Time perspective
- Prospective
- Target follow-up
- 12 Months
Who may qualify
- Ages
- 18 to 75 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- This prospective observational study consecutively enrolls up to 1,000 eligible adult kidney transplant recipients aged 18-75 at The First Affiliated Hospital, Zhejiang University School of Medicine starting November 2025. Participants undergo clinically indicated renal allograft biopsy for monitoring or disease evaluation. The population includes cases with TCMR, ABMR, chronic rejection, polyomavirus nephropathy, recurrent primary renal disease and non-rejection controls. Only residual biopsy slides and anonymized clinical data are collected, with no additional invasive procedures. Subjects with poor specimens, incomplete data or unavailable follow-up records will be excluded. All participants provide informed consent and can withdraw freely. All identifiable information will be removed to protect privacy.
Key inclusion criteria (5 listed)
- Patients who received kidney transplantation and underwent clinically indicated renal allograft biopsy at The First Affiliated Hospital, Zhejiang University School of Medicine starting November 2025.
- Age range from 18 to 75 years old.
- Complete clinical baseline data, laboratory test records, medication history and pathological information available in electronic medical system.
- Residual paraffin biopsy slides are available after routine pathological examination.
- Voluntarily provide written informed consent for the use of residual pathological specimens and clinical data for research.
Key exclusion criteria (5 listed)
- Poor-quality biopsy specimens including blurred staining, severe tissue damage or insufficient tissue volume which cannot support pathological image analysis.
- Severe missing core clinical variables that cannot be supplemented via standardized data imputation.
- Complicated with severe irreversible dysfunction of heart, liver, brain or other vital organs affecting long-term clinical follow-up data collection.
- Unable to complete routine clinical follow-up, resulting in unavailable outcome data for recurrent rejection labeling.
- Refuse to participate or withdraw informed consent.
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
ROC AUC of multi-task AI model for identification of renal allograft lesions
Time frame: After model training and internal verification（through study completion, an average of 24 months）
Area under receiver operating characteristic curve to evaluate model ability to detect rejection subtypes, polyomavirus nephropathy and recurrent primary renal disease.
Sensitivity and specificity of multi-task AI model for renal allograft lesion diagnosis
Time frame: After model training and internal verification（through study completion, an average of 24 months）
Sensitivity and specificity of the AI model discriminating TCMR, ABMR, polyomavirus nephropathy and recurrent primary renal disease.
Diagnostic accuracy of multi-task AI model for renal allograft lesions
Time frame: After model training and internal verification（through study completion, an average of 24 months）
Overall accuracy of the AI model in differentiating post-transplant renal pathological lesions.
3 secondary outcomes
ICC/Kappa consistency between AI quantitative scoring and pathologists' Banff evaluation
Time frame: After model training, optimization and internal test set verification（through study completion, an average of 24 months）
ROC AUC of AI sub-model for prediction of recurrent renal allograft rejection
Time frame: After extraction of 12-month routine follow-up data（through study completion, an average of 24 months）
Change in inter-pathologist diagnostic Kappa with AI model assistance
Time frame: After model training and internal verification（through study completion, an average of 24 months）
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 3 Aug 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.
Dajin Chen, MD, PhD study chair
Zhejiang University
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 Graft Rejection and Kidney Transplantation, run by Chen Dajin 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 5 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: under way, not looking for participants, as of the sponsor's last update on 3 Aug 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?
ROC AUC of multi-task AI model for identification of renal allograft lesions (time frame: After model training and internal verification（through study completion, an average of 24 months）). The study lists 3 primary outcomes in all. 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 3 Aug 2026.