Artificial Intelligence Model for Renal Allograft Rejection Identification, Lesion Quantification and Rejection Risk Prediction Based on Prospective Renal Transplant Pathology and Clinical Multidimensional Data

NCT07741552 | Active Not Recruiting DMC

• 1 other identifier: FAHZU-KT-AI-20260634
• Study Type: observational
• Target: 1,000
• Locations: 1 country
• Sites: 1

Brief 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. 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.

Conditions

Graft Rejection; Kidney Transplantation; Glomerulonephritis; Polyomavirus Infections

Outcome Measures

Primary Outcomes (3)

• 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.

  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.

  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.

  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

  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

  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

  After model training and internal verification（through study completion, an average of 24 months）

Eligibility Criteria

• Age: 18 Years - 75 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

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.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Chen Dajin: lead

Study Sites (1)

The First Affiliated Hospital, Zhejiang University School of Medicine

Hangzhou, Zhejiang, China

Location

MeSH Terms

Conditions

Glomerulonephritis; Polyomavirus Infections

Condition Hierarchy (Ancestors)

Nephritis; Kidney Diseases; Urologic Diseases; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Male Urogenital Diseases; DNA Virus Infections; Virus Diseases; Infections

Study Officials

• Dajin Chen, MD, PhD

  Zhejiang University

  STUDY CHAIR

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: PROSPECTIVE
• Target Duration: 12 Months
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Clinical Professor, Principal Investigator, Kidney Transplantation Center

Study Record Dates

• First Submitted: July 23, 2026
• First Posted: August 3, 2026
• Study Start: November 1, 2025
• Primary Completion (Estimated): October 31, 2030
• Study Completion (Estimated): October 31, 2030
• Last Updated: August 3, 2026

Record last verified: 2026-07

Locations

CN (1)