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.
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 Type
OBSERVATIONAL
Enrollment
500
ESKD
The patient reaches CKD stage 5 and the glomerular filtration rate is less than 15 ml/min
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
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