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

使用卷积神经网络根据移植前活检和临床生物标志物预测移植后肾功能

• 批准号: 10315165
• 负责人: Mackenzie Anne Eagleson
• 金额: $ 8.65万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10315165
• 关键词: Adopted; Adoption; Advisory Committees; Algorithms; Allografting; Biometry; Biopsy; Cessation of life; Chronic; Clinical; Counseling; Country; Data; Data Analyses; Data Set; Decision Making; Deterioration; Doctor of Philosophy; Donor person; Environment; Epidemiology; Evaluation; Feedback; Funding; Glomerular Filtration Rate; Health; Histologic; Histology; Hospitals; Hour; Human; Image; Institution; Instruction; Interview; Kidney; Kidney Transplantation; Knowledge; Learning; Lesion; Life; Link; Logistics; Machine Learning; Maryland; Medicine; Mentors; Modeling; Modernization; Operative Surgical Procedures; Organ; Outcome; Pathologic; Pathologist; Pathology; Patients; Performance; Persons; Physicians; Positioning Attribute; Provider; Public Health Schools; Qualitative Methods; Recovery; Renal function; Reporting; Reproducibility; Research; Research Personnel; Risk; Savings; Scientist; Severities; Slide; Specialist; Standardization; Statistical Computing; Supervision; Surgeon; Techniques; Time; Training; Transplant Recipients; Transplant Surgeon; Transplantation; Transplantation Surgery; United States National Institutes of Health; Validation; Visual; Waiting Lists; adaptive learning; advanced analytics; base; career; clinical biomarkers; clinical investigation; clinical practice; convolutional neural network; data registry; design; experience; graft function; implantation; improved; indexing; kidney biopsy; levan; mortality; new technology; next generation; post-transplant; prevent; prognostic significance; prognostic value; rapid technique; skills; success; task analysis; tool; transplant centers; transplant registry

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.

项目概要/摘要 目前有超过 90,000 名候选者，对可移植肾脏的需求远远超过其供应量 已列入候补名单，但每年进行的移植手术少于 22,000 例。超过 8,000 名患者被转移出医院 每年都会因等待录取期间死亡或健康状况恶化而进入候补名单。尽管存在这一迫切需要 器官，近 20% 的回收肾脏最终被丢弃。最常报告的原因 丢弃对供体活检的组织学不利。进行这些移植前活检是为了 评估器官的质量，通常用作预测植入后同种异体移植性能的工具。 不幸的是，活检结果的预后意义存在争议，人们越来越担心 关于病理学家间活检解读数据的可靠性和可重复性 可变性。最近的证据表明，受者移植物的结果仅与供体活检相关 由经验丰富的肾脏病理学家进行解释。然而，大多数移植中心都没有更多的 比少数专门的肾脏病理学家更专业；鉴于器官恢复通常发生在偏远地区 在深夜或周末的医院，活检通常由值班的病理学家进行解释，而无需 肾脏组织学专门培训。这些提供者往往高估了慢性病变的严重程度， 导致不当丢弃原本可以接受的器官。 卷积神经网络（CNN）是一种机器学习技术，可以等于或超过人类 以自动化、客观的方式执行视觉分析任务。我们建议利用这一新的 技术来实现以下目标：（1）开发一种可靠且准确地预测后的 CNN 移植物功能来自数字化采购活检载片以及供者和受者指标 移植受者科学登记处 (SRTR) 数据集； (2) 比较我们的预测精度 CNN 目前可用的捐赠者风险评分； (3) 定性评估 CNN 的采用性、可接受性， 和临床医生的实用性。鉴于我们团队在机器学习、肾病等方面的专业知识，这些目标是高度可行的 移植和SRTR数据分析。 我们假设我们可以建立一个 CNN，为移植医生提供准确的术前实时信息。 移植后移植成功的时间估计有助于指导患者咨询。如果拟议的目标是 如果实现的话，我们 CNN 的反馈可以防止不当丢弃数千个肾脏和 通过增加全国范围内进行的移植手术数量来降低候补死亡率。经过 在进行这项研究时，伊格尔森博士将培养一套技能，包括国家登记数据分析、 定性方法和机器学习：正在迅速使用的重要现代技术 整个医学领域，并将在她作为独立外科医生科学家的整个职业生涯中为她提供良好的服务。