Automatic Wide-Field Optical Coherence Tomography for Assessment of Transplant Kidney Viability

用于评估移植肾活力的自动广域光学相干断层扫描

• 批准号: 10700999
• 负责人: Steven Potter
• 金额: $ 62.63万
• 依托单位: UNIVERSITY OF MASSACHUSETTS AMHERST
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10700999
• 关键词: 3-Dimensional; Acute Kidney Failure; Algorithms; Anatomy; Area; Biological; Biopsy; Cessation of life; Clinic; Clinical; Clinical Research; Collaborations; Data; Device or Instrument Development; Devices; Diabetes Mellitus; Diameter; Discrimination; Donor person; End stage renal failure; Failure; Generations; Goals; Guidelines; Healthcare; Histologic; Histology; Histopathology; Human; Hypertension; Image; Imaging Device; Imaging technology; Immunologics; In Situ; Intelligence; Ischemia; Kidney; Kidney Diseases; Kidney Transplantation; Location; Maps; Measures; Medical History; Medical center; Methods; Morphology; Nephrology; Oklahoma; Operating Rooms; Operative Surgical Procedures; Optical Coherence Tomography; Organ Donor; Organ Procurements; Organ Transplantation; Outcome; Pathologic; Pathology; Patients; Procedures; Process; Quality of life; Renal function; Resolution; Risk; Robotics; Sampling; Scanning; Site; Specificity; Surface; System; Technology; Testing; Time; Tissues; Training; Translating; Transplantation; Tubular formation; Uncertainty; Validation; Visual; Waiting Lists; Weight; algorithm development; care burden; cohort; deep learning; deep learning model; delayed graft function; diagnostic criteria; functional status; graft failure; graft function; high resolution imaging; high risk; human imaging; image processing; imaging study; implantation; improved; indexing; kidney imaging; novel; post-transplant; predictive modeling; prognostic; prospective; robot assistance; screening; tool; virtual

Summary There is a worldwide shortage of kidneys for transplantation due mainly to the fact that there is no reliable means to determine the viability of kidneys available for transplant. There are unmet clinical needs to reduce the >8,000 deaths that occur each year from failures of finding viable kidney matches for transplant, to reduce the nearly 4- year time on donor waitlists, and to reduce the number of failed transplants. The current process for screening deceased donor kidneys uses two methods: 1) pathological scores based on anatomical features from a biopsy (tubules, glomeruli, etc.) and 2) the Kidney Donor Profile Index (KDPI) derived from the donor’s medical history (hypertension, diabetes, weight, etc.). However, clinical research indicates that those current methods have limited discriminative power. Optical coherence tomography (OCT) is an imaging technology that can obtain high-resolution, non-invasive, cross-sectional images of biological tissues in situ and in real time. We have demonstrated that OCT can provide non-invasive, real-time, histopathological information of the kidney that is impossible to obtain using any other known procedure. We have demonstrated that OCT imaging of human kidney histopathology both prior to and following their transplant can be used to predict post-transplant renal function. Furthermore, these preliminary trials have demonstrated that OCT imaging of human donor kidneys with a hand-held unit in the operating room is safe and that the entire kidney can be evaluated within a relatively short period of time. From a recently finished clinical study with 169 human transplant kidneys, we found that in the expanded criteria donor (ECD) kidneys (or marginal kidneys), increased tubular lumen diameter was able to predict delayed graft function (DGF) prior to implantation. In this proposal, we will develop a novel OCT device with intelligent scanning and deep learning to evaluate donor kidney viability before transplant. By scanning the whole kidney surface, our device intends to eliminate the uncertainty created by the biopsy/KDPI paradigm. The OCT imaging studies will be correlated with post-transplant renal function in order to establish imaging algorithms and guidelines for OCT imaging of kidneys prior to their transplant. Our central hypothesis is that more comprehensive morphological parameters as measured by OCT can be used to determine post-transplantation renal function. The specific aims of this proposal are: 1) Develop robot-assisted automatic 3D scanning OCT imaging device to image human kidneys prior to their transplantation. 2) Develop deep-learning-based image processing algorithms to quantitatively assess the OCT parameters as indicators of the functional status of kidneys, using histology scoring system as the gold standard. 3) Derive the diagnostic criteria for assessing transplant kidney function and perform prospective clinical studies to assess the accuracy of predicting post- transplant function using OCT.

总结