To Explore the Value of Magnetic Resonance Imaging in Noninvasive Quantitative Evaluation of Graft Function After Simultaneous Pancreas-kidney Transplantation

Overview

Pancreatic kidney joint transplantation (SPK) has become the standard treatment for patients with diabetes complicated by end-stage renal disease. By simultaneously transplanting the pancreas and kidney, SPK can restore the patient\'s insulin secretion and kidney function after surgery, thereby significantly improving the patient\'s quality of life and extending life expectancy. However, the high incidence of postoperative complications, especially organ dysfunction, remains a major challenge. Complications of organ dysfunction include thrombosis of the pancreatic graft, acute rejection, graft pancreatitis, and acute tubular necrosis of the renal graft. Early diagnosis and monitoring of these complications are crucial for improving patient prognosis and extending the life of the transplanted organs. Although some methods are currently used in clinical practice to assess graft function, such as estimated glomerular filtration rate (eGFR) and Gates method of renal dynamic imaging, these methods still have significant limitations. For example, traditional biochemical markers for predicting kidney function are affected by many irrelevant factors and lack accuracy. While the Gates method is widely used, its long examination process, high cost, and radiation exposure limit its feasibility in routine postoperative application. In addition, although tissue biopsy is considered the gold standard for assessing renal pathology, its invasiveness and potential complications limit its application. In view of the shortcomings of traditional methods, there is an urgent need for a non-invasive, dynamic, and accurate detection method for the early diagnosis of post-SPK organ dysfunction. Magnetic resonance imaging (MRI) has shown great potential in the assessment of transplanted organ function due to its non-invasiveness, high soft tissue resolution, and multi-parametric analysis capabilities. MRI can not only display the anatomical structure of the transplanted organs in detail but also dynamically monitor the blood perfusion and tissue oxygenation levels of the transplanted organs through advanced imaging techniques, such as arterial spin labeling (ASL) and blood oxygenation level-dependent (BOLD) imaging. The application of these new technologies helps to discover complications early and provides important diagnostic information, thereby improving patient prognosis. Therefore, this study aims to explore the application of MRI in the functional assessment of transplanted organs after SPK, especially its potential in the early diagnosis and monitoring of organ dysfunction. Through this study, we hope to provide a new non-invasive diagnostic method for the early discovery of post-SPK complications, thereby improving the long-term prognosis of patients and filling the gap in current clinical practice.

Pancreatic transplantation refers to the process of transplanting a healthy pancreas, either whole or in segments, from a donor into a recipient to restore endocrine function. Pancreatic transplantation includes pancreas transplantation alone (PTA), pancreas after kidney transplantation (PAK), and simultaneous pancreas and kidney transplantation (SPK). SPK is a surgical procedure that transplants both the pancreas and kidney from a donor into a recipient simultaneously, aiming to restore normal insulin secretion and kidney function, particularly suitable for patients with diabetes complicated by end-stage renal disease. SPK simultaneous transplantation can effectively prevent and delay the complications of diabetes, significantly improve the quality of life for uremic diabetic patients, and extend their expected lifespan. Compared to other organ transplants, pancreatic transplantation has a higher incidence of complications.Common complications after SPK surgery include dysfunction of the transplanted organs, such as pancreatic and renal dysfunction. These complications may manifest as thrombosis of the pancreatic graft, acute rejection, graft pancreatitis, acute tubular necrosis, and rejection reactions in the renal graft. Therefore, early diagnosis and monitoring of these complications are crucial for improving patient outcomes and extending the life of the transplanted organs. Traditional assessment methods have limitations in predicting and detecting early graft dysfunction. For example, the combination of lipase peak and C-reactive protein has some effect in predicting early pancreatic graft dysfunction after SPK, but its predictive value for early renal transplant dysfunction is relatively poor in sensitivity. For post-transplant renal dysfunction, the clinically used indicator is the estimated glomerular filtration rate (eGFR), which is assessed in conjunction with uric acid, creatinine, and other indicators. However, these indicators are easily influenced by various unrelated factors such as medication and diet, thereby affecting the sensitivity of the predictive value. The Gates method of renal dynamic imaging is currently the only widely used method in clinical practice to assess split renal function. However, its long examination time, high cost, and the radiation dose imposed on patients limit its use in post-transplant patients. Currently, tissue biopsy is the gold standard for assessing renal pathology. However, renal biopsy is invasive and can lead to complications and kidney damage; moreover, the tissue sample obtained from renal biopsy is only a part of the kidney, which can result in sampling errors. The lack of reliable, accurate, and comprehensive test results hinders the diagnosis of early transplant renal dysfunction after SPK. Therefore, developing a non-invasive dynamic detection method for early transplant renal dysfunction after SPK is an urgent problem faced in clinical practice. Magnetic resonance imaging (MRI) has a wide range of applications in medical imaging. Due to its non-invasive nature, high soft tissue resolution, and multi-parametric analysis capabilities, it has become a key tool for assessing and diagnosing various diseases. MRI can provide high-resolution images of tissue structures and assess tissue functional status, blood perfusion, and metabolic activity through different imaging sequences, such as T1, T2-weighted imaging, diffusion-weighted imaging (DWI), and magnetization transfer imaging (MT). These characteristics and continuously updated new imaging sequences give MRI great potential in assessing kidney and pancreas diseases, especially in non-invasive assessment of tissue structure and functional status. In the assessment after SPK, MRI can not only display the anatomical structure of the transplanted organs in detail but also monitor the blood perfusion and functional status of the transplanted organs through multi-parametric imaging techniques. For example, arterial spin labeling (ASL) imaging can detect changes in microvascular perfusion; blood oxygenation level-dependent (BOLD) imaging can detect tissue oxygenation levels caused by vascular occlusion. The application of these new technologies helps to detect complications such as transplant organ dysfunction early and provides important diagnostic information, thereby improving patient outcomes. In recent years, with the advancement of precision medicine and the rapid development of artificial intelligence, the combination of radiomics and machine learning technologies has further enhanced the application potential of MRI. Radiomics, through high-throughput image feature extraction combined with machine learning models, can automate and improve the ability for precise diagnosis. These technologies have shown great potential especially in the follow-up and prognosis assessment after SPK, and by constructing accurate models, they can significantly improve the accuracy and reliability of assessments. Looking to the future, the clinical application prospects of MRI technology in non-invasive assessment after PKT are broad. Its potential to improve the quality of life and survival rate of transplanted organs is enormous. However, current challenges still exist, including the popularization and standardization of technology, data accumulation and analysis, and the necessity for multi-center clinical research. Despite this, with continuous technological development and research, the application of MRI in SPK assessment will become more extensive and in-depth. The purpose of this study is to explore the value of new imaging technologies in assessing the early graft function of patients who have undergone combined pancreas-kidney transplantation. By obtaining clinical, imaging, laboratory examination, and pathological data of patients after combined pancreas-kidney transplantation, and using imaging processing software for image analysis, we aim to explore the relationship between imaging parameters, body composition, and metabolic diseases with the early graft function of patients after combined pancreas-kidney transplantation. This will enable non-invasive diagnosis, therapeutic efficacy evaluation, and prognosis prediction for patients, thereby guiding clinical treatment and improving the survival rate and quality of life of patients who have undergone combined pancreas-kidney transplantation. This study is a prospective study. Inclusion criteria 1. Patients who have undergone combined pancreas-kidney transplantation and have been prescribed an MR examination; 2. Age/gender: no restrictions; 3. Patients who voluntarily participate in the clinical trial and sign a written informed consent form. Exclusion criteria 1. Patients with cardiac pacemakers, unknown materials, metal implants, neural stimulators, and those with claustrophobia, etc.; 2. Patients who cannot tolerate sufficient breath-holding for a complete MR examination. Withdrawal criteria from the trial: 1. Serious adverse events; 2. Poor compliance; 3. The research subject requests to withdraw; 4. The researcher deems it necessary to terminate the subject\'s participation in this study. The calculation of the sample size is based on the cross-sectional sample size formula, with a test power set at 0.9 and a Type I error set at 0.05. Moreover, this study examines multiple factors, including more than 10 functional MRI sequence parameters (reflecting the oxygen content and blood perfusion of the renal cortex and medulla), body composition including (subcutaneous fat, visceral fat, intermuscular fat, perirenal fat, renal sinus fat, muscle mass, and their radiological densities, etc.), basic indicators of the patient\'s sex, age, height, weight, BMI, waist circumference, blood pressure, etc.; patient medical history including diabetes, hypertension, hyperuricemia, autoimmune diseases, smoking history, drinking history, whether there have been kidney stones, urinary tract obstruction, whether nephrotoxic drugs have been used, whether surgery has been performed, whether there have been infections, etc.; laboratory indicators including urea, creatinine, uric acid, bicarbonate, urinary protein content, urinary protein creatinine ratio, and other routine blood and biochemical indicators; renal pathological results including pathological type, staging, the extent of accumulation in vessels and parenchyma, degree of fibrosis, etc.; a total of about 40 influencing factors are considered comprehensively. According to the requirements of statistical multivariate analysis, the sample size should be at least 10 times the number of variables. Considering a dropout rate of about 20%, the final calculated sample size is 500 cases. Collection of imaging data: Include patients who meet the above criteria, communicate with them to ensure their understanding, and have them sign an informed consent form. Recommend that their attending physician prescribe an MR examination, and the patient must fast for 8 hours and abstain from water for 4 hours before the examination. After the examination, organize the patient\'s imaging data information, recording imaging ID, type of examination, etc. Processing of imaging data: Through the PACS system, copy the DICOM format imaging data using the GE workstation, and use imaging processing software for qualitative and quantitative analysis, recording relevant parameter values. Collection of clinical data: Query the list of included patients through the radiology information system (RIS) of Tongji Hospital\'s radiology department, collect clinical data, laboratory examination data such as blood creatinine, estimated glomerular filtration rate eGFR, etc., pathological data such as the degree or score of renal fibrosis, etc., combine with the exclusion criteria of this study for case selection, and record in detail the patient\'s medical history including sex, age, height, weight, blood pressure, past medical history, etc., laboratory examination data including routine blood tests, blood biochemistry, creatinine, urea, uric acid, bicarbonate, estimated glomerular filtration rate eGFR, urinary protein, urinary protein creatinine ratio, etc., pathological data including pathological type, typing and grading score, renal fibrosis degree score, etc., treatment situation including treatment plan medication, treatment time, etc., disease follow-up, and other information.Group combined pancreas-kidney transplant patients based on laboratory examination data such as blood creatinine, estimated glomerular filtration rate eGFR, etc. Potential Risks: In this study, the use of patient imaging examination materials and electronic medical records may pose a risk of leakage of personal privacy and other information. Preventive Measures: The imaging materials and electronic medical records involved in this study are stored by the hospital and are only accessible to medical workers for guiding the diagnosis and treatment of diseases, not for any commercial purposes. The materials (including imaging images) recorded and used in this study do not contain any identifiers that can recognize the patient\'s identity, thus effectively protecting the patient\'s personal information. The data mainly consists of imaging parameters analyzed by image processing software. After the collection of clinical information, laboratory data, and prognosis materials is completed, they are organized according to the grouping situation. The data is then statistically analyzed using statistical software such as SPSS. It is important to ensure that all data handling and analysis comply with relevant data protection regulations and ethical guidelines to minimize the risk of privacy breaches. Additionally, obtaining informed consent from patients and maintaining strict access controls to the data can further enhance the protection of patient privacy.