Predicting post-transplant kidney function from pre-transplant biopsy and clinical biomarkers using a convolutional neural network

点击翻译按钮获取中文摘要

PROJECT SUMMARY/ABSTRACT The need for transplantable kidneys far exceeds their availability with over 90,000 candidates currently waitlisted but less than 22,000 transplants performed annually. Over 8,000 patients are removed from the waitlist each year due to death or deterioration in health while awaiting an offer. Despite this critical need for organs, nearly 20% of recovered kidneys are ultimately discarded. The most commonly reported reason for discard is unfavorable histology on donor biopsy. These pre-transplant biopsies are performed in order to assess the quality of the organ and are often used as a tool to predict post-implantation allograft performance. Unfortunately, the prognostic significance of biopsy findings is controversial and there is growing concern regarding the reliability and reproducibility of data derived from biopsy interpretation due to inter-pathologist variability. Recent evidence demonstrates that recipient graft outcomes correlate only with donor biopsy interpretation performed by an experienced renal pathologist. However, most transplant centers have no more than a handful of dedicated expert renal pathologists; given that organ recovery often occurs at remote hospitals late at night or on weekends, biopsies are usually interpreted by on-call pathologists without dedicated training in renal histology. These providers tend to overestimate the severity of chronic lesions, resulting in the inappropriate discard of otherwise acceptable organs. Convolutional neural networks (CNNs), a machine learning technique, can equal or exceed human performance in visual analysis tasks in an automated, objective fashion. We propose to leverage this new technology to accomplish the following aims: (1) To develop a CNN that reliably and accurately predicts post- transplant graft function from digitized procurement biopsy slides and donor and recipient metrics in the Scientific Registry of Transplant Recipients (SRTR) dataset; (2) To compare the predictive accuracy of our CNN to currently available donor risk scores; and (3) To qualitatively evaluate CNN adoptability, acceptability, and utility by clinicians. These aims are highly feasible given our group's expertise in machine learning, kidney transplantation, and analysis of SRTR data. We hypothesize that we can build a CNN that provides transplant physicians with accurate pre-operative real- time estimates of post-transplant graft success to help guide patient counseling. If the proposed aims are achieved, feedback from our CNN could prevent the inappropriate discard of thousands of kidneys and decrease waitlist mortality by increasing the number of transplants performed across the country. By conducting this research, Dr. Eagleson will cultivate a skillset that includes national registry data analysis, qualitative methods, and machine learning: important modern techniques that are rapidly becoming used throughout medicine and will serve her well throughout her career as an independent surgeon-scientist.