Clinical Trial Details
— Status: Recruiting
Administrative data
| NCT number |
NCT06366529 |
| Other study ID # |
TJ-IRB202308123 |
| Secondary ID |
|
| Status |
Recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
September 1, 2023 |
| Est. completion date |
September 2030 |
Study information
| Verified date |
April 2024 |
| Source |
Tongji Hospital |
| Contact |
Zhen Li, Doctor |
| Phone |
02783663543 |
| Email |
zhenli[@]hust.edu.cn |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
Currently, renal biopsy is the gold standard for evaluating renal pathology and renal
fibrosis, but it is invasive and carries the risk of serious complications; and the sampled
tissue is only a small part of the kidney, which is prone to sampling bias. The lack of
reliable, comprehensive test results has hindered the research of new anti-fibrotic drugs and
delayed the clinical application of effective new drugs. Therefore, the development of a
non-invasive dynamic detection method for renal insufficiency and renal fibrosis in vivo is
an urgent clinical problem to be solved.
With the continuous development and update of technology, imaging provides a new way to
non-invasively evaluate renal fibrosis. Due to the high resolution of soft tissue and the
ability to perform multi-parameter analysis, magnetic resonance has developed the diagnosis
of renal insufficiency and renal fibrosis from macroscopic simple biomorphological changes to
microscopically complex pathophysiological changes. Many imaging techniques measure renal
dysfunction and renal fibrosis by assessing the impact of fibrosis on the functional status,
physical properties, and molecular properties of the kidney.
In recent years, in the context of precision medicine, artificial intelligence technologies
such as radiomics and machine learning are rapidly becoming very promising auxiliary tools in
the imaging assessment of renal fibrosis. It can extract and learn features in images with
high throughput, make greater use of information in medical images that cannot be recognized
by the human eye, and achieve disease diagnosis, prognosis assessment, and efficacy
prediction by building models. However, most of the current research is in the preliminary
stage, and there are still few studies on the assessment of renal insufficiency and renal
fibrosis. I believe that with the continuous improvement of algorithms and the optimization
of models, the progress of radiomics and machine learning will be great. To a certain extent,
it promotes the development of personalized medicine and precision medicine for patients with
renal insufficiency and renal fibrosis.
Description:
Renal insufficiency can be divided into acute kidney injury (AKI) and chronic kidney disease
(CKD). There are many causes of AKI, which are mainly divided into three categories:
prerenal, renal and postrenal according to the anatomical location. The incidence rate of AKI
is about 3% to 10%, and the incidence rate in intensive care units is higher, as high as 30%
to 60%; when AKI patients are seriously ill, the mortality rate is higher, about 30% to 80%.
The prognosis of AKI is mainly related to the cause and severity of complications. For AKI
caused by pre-renal and post-renal causes, if diagnosed early and treated promptly, most
patients can recover well in renal function. For patients with AKI caused by renal
parenchymal disease, the degree of recovery varies depending on the cause, and some patients
will have chronic renal damage. When the duration of AKI is greater than or equal to three
months, it can be called CKD. More than 700 million people worldwide suffer from kidney
disease, and CKD is the third leading cause of death after tumors and heart disease . CKD can
be caused by a variety of kidney diseases, such as diabetic nephropathy, hypertensive
nephropathy, and chronic glomerulonephritis. In addition, there is a special type of renal
transplant insufficiency, which refers to the renal insufficiency that occurs after a patient
undergoes a kidney transplant.
In recent years, people have gradually paid more and more attention to the relationship
between various metabolic diseases and body composition analysis and renal insufficiency.
These are all risk factors for renal insufficiency. Early identification and management of
these risk factors is of great significance to the prognosis and delaying disease progression
in patients with renal insufficiency. Over the past 40 years, global obesity rates have
continued to rise, with more than one-third of countries having their rates doubled.
Moreover, obesity is related to various mechanisms such as insulin resistance, and obese
patients are also more likely to suffer from various metabolic diseases. Therefore, obesity
is also a factor in renal insufficiency that needs to be prevented and managed urgently.
There are anatomical, cellular, and molecular differences in adipose tissue in different
parts of the human body. Therefore, it is of great significance to more accurately measure
and divide adipose tissue and muscle in the human body and explore its correlation with renal
insufficiency. Various metabolic diseases have a high incidence rate, long course, affect
tissues and organs throughout the body, and are important risk factors and causes of renal
insufficiency. Therefore, it is also important to explore their correlation with renal
insufficiency and explore related mechanisms. Contribute to doctors' clinical diagnosis and
subsequent auxiliary designation of treatment plans.
However, regardless of its pathogenesis, renal fibrosis is the final pathological
manifestation of CKD. Its main pathological characteristics are inflammatory cell
infiltration, fibroblast proliferation, extracellular matrix (ECM) deposition, and
replacement of normal kidney tissue by scar tissue. Renal fibrosis is the main determinant of
renal insufficiency, and its presence and extent are closely related to CKD disease
progression and prognosis. Early diagnosis and accurate assessment of the degree of renal
insufficiency and renal fibrosis are effective means of delaying the development of end-stage
renal disease and are of great clinical significance in improving the survival rate and
quality of life of patients with renal insufficiency.
Usually, the indicator used clinically to evaluate renal function is the estimated glomerular
filtration rate (eGFR), as well as urea, creatinine, uric acid and bicarbonate to assist in
the evaluation. However, these indicators are easily affected by many factors such as drugs
and diet, are not very accurate, and may change significantly in a short period of time. More
importantly, these indicators reflect overall kidney function. When patients have mild kidney
damage and are in the early stages of kidney disease, these indicators are usually still
within the normal range. By the time they are significantly reduced, patients have usually
developed irreversible kidney damage. The Gates method of renal dynamic imaging is currently
the only method widely used in clinical practice to evaluate renal function. However, its
examination time is long (more than half an hour), the price is high, and it also imposes a
radiation dose on the patient, which limits its routine clinical application.Currently, renal
biopsy is the gold standard for evaluating renal pathology and renal fibrosis, but it is
invasive and carries the risk of serious complications; and the sampled tissue is only a
small part of the kidney, which is prone to sampling bias. The lack of reliable,
comprehensive test results has hindered the research of new anti-fibrotic drugs and delayed
the clinical application of effective new drugs. Therefore, the development of a non-invasive
dynamic detection method for renal insufficiency and renal fibrosis in vivo is an urgent
clinical problem to be solved.
With the continuous development and update of technology, imaging provides a new way to
non-invasively evaluate renal fibrosis. Due to the high resolution of soft tissue and the
ability to perform multi-parameter analysis, magnetic resonance has developed the diagnosis
of renal insufficiency and renal fibrosis from macroscopic simple biomorphological changes to
microscopically complex pathophysiological changes. Many imaging techniques measure renal
dysfunction and renal fibrosis by assessing the impact of fibrosis on the functional status,
physical properties, and molecular properties of the kidney. For example, diffusion weighted
imaging (DWI) can detect the renal fibrosis process. Changes in the movement of water
molecules caused by deposition of extracellular matrix components, infiltration of
inflammatory cells and fibroblasts, and renal tubular atrophy; arterial spin labeling (ASL)
imaging can detect changes in microvascular perfusion; blood oxygen level dependence (blood
oxygen level) oxygenation level-dependent (BOLD) imaging can detect the decrease in tissue
oxygenation levels caused by vascular occlusion; magnetic resonance elastography (MRE) can
detect the increase in kidney tissue stiffness caused by fibrosis; magnetization transfer
imaging, MT) can detect the content of macromolecules such as collagen, etc.
In recent years, in the context of precision medicine, artificial intelligence technologies
such as radiomics and machine learning are rapidly becoming very promising auxiliary tools in
the imaging assessment of renal fibrosis. It can extract and learn features in images with
high throughput, make greater use of information in medical images that cannot be recognized
by the human eye, and achieve disease diagnosis, prognosis assessment, and efficacy
prediction by building models. However, most of the current research is in the preliminary
stage, and there are still few studies on the assessment of renal insufficiency and renal
fibrosis. I believe that with the continuous improvement of algorithms and the optimization
of models, the progress of radiomics and machine learning will be great. To a certain extent,
it promotes the development of personalized medicine and precision medicine for patients with
renal insufficiency and renal fibrosis.
This study aims to explore the value of new imaging technologies in the evaluation of
patients with renal insufficiency and renal fibrosis, including transplanted renal
insufficiency. By obtaining clinical, imaging, laboratory examination and pathological data
of patients with renal insufficiency and renal fibrosis, we will use Image processing
software analyzes images to explore the relationship between image parameters, body
composition and metabolic diseases and the degree of renal insufficiency and renal fibrosis
in patients to achieve non-invasive diagnosis, efficacy evaluation and prognosis prediction
of renal insufficiency and renal fibrosis. etc., thereby guiding clinical treatment and
improving the survival rate and quality of life of patients with renal insufficiency.
Research steps
1. Collection of imaging data: Include patients with the above criteria, communicate with
them and make them informed before signing an informed consent form. It is recommended
that for MR examinations prescribed by the attending physician, patients should fast for
8 hours and water for 4 hours before the examination. After the examination, the
patient's imaging data information should be organized, and information such as image ID
and examination item type should be recorded.
2. Image data processing: Use the PACS system and GE workstation to copy the image data in
DICOM format, use image processing software to conduct qualitative and quantitative
analysis, and record relevant parameter values.
3. Clinical data collection: Query the list of patients through the Radiation Information
System (RIS) of the Radiology Department of Tongji Hospital, and collect clinical data,
laboratory test data such as serum creatinine, glomerular filtration rate eGFR, etc.,
and pathological data such as the degree of renal fibrosis. conduct case screening based
on the exclusion criteria of this study, and record the patient's medical history in
detail, including gender, age, height, weight, blood pressure, past medical history,
etc., and laboratory test data including blood routine, blood biochemistry, creatinine,
urea, and uric acid. , bicarbonate, glomerular filtration rate eGFR, urinary protein,
urinary protein to creatinine ratio, etc., pathological data, pathological type,
classification and grading score, renal fibrosis degree score, etc. The treatment
situation includes treatment plan, medication, treatment time, etc., Disease follow-up
information and other information.
4. Group patients with renal insufficiency and renal fibrosis through laboratory test data
such as serum creatinine, glomerular filtration rate eGFR, etc., pathological data such
as renal fibrosis degree or score, or treatment efficacy, and compare the groups.
Differences in relevant imaging parameters, exploring the ability of different imaging
technologies to evaluate renal insufficiency and renal fibrosis, aiming to realize the
application of imaging in non-invasive diagnosis, efficacy evaluation, and prognosis
prediction of renal insufficiency and renal fibrosis, thereby guiding clinical practice
decision making.
7. Possible risks and preventive measures
Possible risks:
This study requires access to patient imaging examination data and electronic medical record
data, and there may be risks of leakage of patient privacy and other information.
Precautions:
The imaging data and electronic medical record data used in this study are all data stored by
the hospital, which can only be viewed by medical workers except the patients themselves and
are used to guide the diagnosis and treatment of diseases and are not used for any commercial
activities. The data recorded and used by this institute (including imaging images) do not
contain any identifier that can identify the patient. Therefore, the patient's personal
information can be effectively protected.
8. Data collection and statistical analysis The data are mainly image parameters analyzed by
image processing software. After completing the collection of clinical information,
laboratory data, and prognostic data, they are sorted according to the grouping situation and
analyzed using statistical software such as SPSS.
