Kidney Transplant Failure and RejectionNCT07145944
To Explore the Application Value of Magnetic Resonance Imaging in Noninvasive Quantitative Evaluation of Graft Function and Systemic Metabolism After Renal Transplantation
An observational study of Kidney Transplant Failure and Rejection and Transplantation, Kidney, run by Tongji Hospital.
Trial summary
At present, renal biopsy is the gold standard for evaluating the pathology of renal transplants, but it is invasive and has the risk of serious complications; and the sampled tissue is only a small part of the kidney, which is prone to sampling bias and lacks reliable and comprehensive detection results. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Kidney Transplant Failure and Rejection, Transplantation, Kidney, Kidney Transplant Dysfunction
- How many people
- 500 participants
- Where
- No sites listed yet
- Study dates
- 1 Sep 2025to 12 Dec 2031
- 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
- No healthy volunteers
- Child, Adult, Older adult
Study population: renal allograft patients
Who may be able to join
- (1) Patients with MR examination after kidney transplantation;
- Age/gender: unlimited;
- Patients who voluntarily participate in clinical trials and sign written subject informed consent
Who cannot join
- (1) Patients with pacemakers, unknown materials, metal implants, neurostimulators, claustrophobia, etc.
- Patients who cannot tolerate adequate breath-holding for adequate MR examination;
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
At present, renal biopsy is the gold standard for evaluating the pathology of renal transplants, but it is invasive and has the risk of serious complications; and the sampled tissue is only a small part of the kidney, which is prone to sampling bias and lacks reliable and comprehensive detection results. Therefore, it is an urgent problem to develop a non-invasive dynamic detection method for renal insufficiency and transplanted kidney.
With the continuous development and updating of technology, imaging provides a new way for non-invasive evaluation of renal allograft pathology including rejection reaction, acute renal allograft injury, viral infection, etc. MRI technology has developed the diagnosis of renal allograft rejection, fibrosis and other renal allograft dysfunction from macroscopic simple biomorphological changes to microscopic complex pathophysiological changes due to its high resolution of soft tissue and its ability to perform multi-parameter analysis.
In recent years, under the background of precision medicine, artificial intelligence technologies such as radiomics and machine learning are rapidly becoming very promising auxiliary tools in the evaluation of transplanted kidney images. They can extract and learn features in images with high throughput, make greater use of information that cannot be recognized by human eyes in medical images, and realize disease diagnosis, prognosis evaluation, and curative effect prediction by establishing models. However, most of the current research is in the preliminary stage. There are few evaluation studies on kidney transplantation. It is believed that with the continuous improvement of algorithms and optimization of models, radiomics and machine learning will make great progress, which will promote the development of individualized and precise medicine for patients with renal insufficiency to a certain extent.
Read the detailed description
At present, renal biopsy is the gold standard for evaluating the pathology of renal transplants, but it is invasive and has the risk of serious complications; and the sampled tissue is only a small part of the kidney, which is prone to sampling bias and lacks reliable and comprehensive detection results. Therefore, it is an urgent problem to develop a non-invasive dynamic detection method for renal insufficiency and transplanted kidney.
With the continuous development and updating of technology, imaging provides a new way for non-invasive evaluation of renal allograft pathology including rejection reaction, acute renal allograft injury, viral infection, etc. MRI technology has developed the diagnosis of renal allograft rejection, fibrosis and other renal allograft dysfunction from macroscopic simple biomorphological changes to microscopic complex pathophysiological changes due to its high resolution of soft tissue and its ability to perform multi-parameter analysis.
In recent years, under the background of precision medicine, artificial intelligence technologies such as radiomics and machine learning are rapidly becoming very promising auxiliary tools in the evaluation of transplanted kidney images. They can extract and learn features in images with high throughput, make greater use of information that cannot be recognized by human eyes in medical images, and realize disease diagnosis, prognosis evaluation, and curative effect prediction by establishing models. However, most of the current research is in the preliminary stage. There are few evaluation studies on kidney transplantation. It is believed that with the continuous improvement of algorithms and optimization of models, radiomics and machine learning will make great progress, which will promote the development of individualized and precise medicine for patients with renal insufficiency to a certain extent.
- Conditions
- Kidney Transplant Failure and Rejection, Transplantation, Kidney, Kidney Transplant Dysfunction
- Medical subject headings
- Rejection, Psychology
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 |
|---|---|---|
| renal allograft patients | renal allograft patients |
What is being measured
The primary outcome is the main question the study is designed to answer.
ESKD
The patient reaches CKD stage 5 and the glomerular filtration rate is less than 15 ml/min
Measured: From date of randomization until the date of first documented progression or date of death from any cause, whichever came first, assessed up to 120 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 Aug 2025
- Study start1 Sep 2025 (estimated)
- Primary completion30 Dec 2030 (estimated)
- Study completion12 Dec 2031 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Kidney Transplant Failure and RejectionStudies of the same condition, open and under way.
- More trials run by Tongji HospitalThe sponsor's other registered studies.
- 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
- Prospective
- Enrolment
- 500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Tongji Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Zhen Li, Tongji Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07145944
- Sponsor's protocol ID
- TJ-IRB202411041
- Status last verified
- Aug 2025
- First submitted
- 21 Aug 2025
- Last updated
- 28 Aug 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 28 Aug 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- To Explore the Application Value of Magnetic Resonance Imaging in Noninvasive Quantitative Evaluation of Graft Function and Systemic Metabolism After Renal Transplantation
- ClinicalTrials.gov ID
- NCT07145944
- Acronym / sponsor ID
- TJ-IRB202411041
- Conditions
- Kidney Transplant Failure and Rejection, Transplantation, Kidney, Kidney Transplant Dysfunction
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 500 participants (estimated)
- Lead sponsor
- Tongji HospitalUniversity, hospital or other
- Study start
- 1 Sep 2025 (estimated)
- Primary completion
- 30 Dec 2030 (estimated)
- Study completion
- 12 Dec 2031 (estimated)
- First posted
- 28 Aug 2025 (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.
At present, renal biopsy is the gold standard for evaluating the pathology of renal transplants, but it is invasive and has the risk of serious complications; and the sampled tissue is only a small part of the kidney, which is prone to sampling bias and lacks reliable and comprehensive detection results. Therefore, it is an urgent problem to develop a non-invasive dynamic detection method for renal insufficiency and transplanted kidney.
With the continuous development and updating of technology, imaging provides a new way for non-invasive evaluation of renal allograft pathology including rejection reaction, acute renal allograft injury, viral infection, etc. MRI technology has developed the diagnosis of renal allograft rejection, fibrosis and other renal allograft dysfunction from macroscopic simple biomorphological changes to microscopic complex pathophysiological changes due to its high resolution of soft tissue and its ability to perform multi-parameter analysis.
In recent years, under the background of precision medicine, artificial intelligence technologies such as radiomics and machine learning are rapidly becoming very promising auxiliary tools in the evaluation of transplanted kidney images. They can extract and learn features in images with high throughput, make greater use of information that cannot be recognized by human eyes in medical images, and realize disease diagnosis, prognosis evaluation, and curative effect prediction by establishing models. However, most of the current research is in the preliminary stage. There are few evaluation studies on kidney transplantation. It is believed that with the continuous improvement of algorithms and optimization of models, radiomics and machine learning will make great progress, which will promote the development of individualized and precise medicine for patients with renal insufficiency to a certain extent.
Detailed description
At present, renal biopsy is the gold standard for evaluating the pathology of renal transplants, but it is invasive and has the risk of serious complications; and the sampled tissue is only a small part of the kidney, which is prone to sampling bias and lacks reliable and comprehensive detection results. Therefore, it is an urgent problem to develop a non-invasive dynamic detection method for renal insufficiency and transplanted kidney.
With the continuous development and updating of technology, imaging provides a new way for non-invasive evaluation of renal allograft pathology including rejection reaction, acute renal allograft injury, viral infection, etc. MRI technology has developed the diagnosis of renal allograft rejection, fibrosis and other renal allograft dysfunction from macroscopic simple biomorphological changes to microscopic complex pathophysiological changes due to its high resolution of soft tissue and its ability to perform multi-parameter analysis.
In recent years, under the background of precision medicine, artificial intelligence technologies such as radiomics and machine learning are rapidly becoming very promising auxiliary tools in the evaluation of transplanted kidney images. They can extract and learn features in images with high throughput, make greater use of information that cannot be recognized by human eyes in medical images, and realize disease diagnosis, prognosis evaluation, and curative effect prediction by establishing models. However, most of the current research is in the preliminary stage. There are few evaluation studies on kidney transplantation. It is believed that with the continuous improvement of algorithms and optimization of models, radiomics and machine learning will make great progress, which will promote the development of individualized and precise medicine for patients with renal insufficiency to a certain extent.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| renal allograft patients | – | –renal allograft patients |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- renal allograft patients
Key inclusion criteria (3 listed)
- (1) Patients with MR examination after kidney transplantation;
Key exclusion criteria (2 listed)
- (1) Patients with pacemakers, unknown materials, metal implants, neurostimulators, claustrophobia, etc.
- Patients who cannot tolerate adequate breath-holding for adequate MR examination;
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
ESKD
Time frame: From date of randomization until the date of first documented progression or date of death from any cause, whichever came first, assessed up to 120 months
The patient reaches CKD stage 5 and the glomerular filtration rate is less than 15 ml/min
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 28 Aug 2025.
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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.
Questions about this trial
What is this clinical trial studying?
An observational study of Kidney Transplant Failure and Rejection and Transplantation, Kidney, run by Tongji Hospital.
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 2 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 28 Aug 2025. Status can change between updates, so confirm with the study contact before making plans.
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 Zhen Professor Li, PHD,MD (02783663543, zhenli@hust.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
ESKD (time frame: From date of randomization until the date of first documented progression or date of death from any cause, whichever came first, assessed up to 120 months). 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 28 Aug 2025.