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: 10462527
• 负责人: Mackenzie Anne Eagleson
• 金额: $ 8.99万
• 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/10462527
• 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.

项目摘要/摘要 对可移植肾脏的需求远远超过了可供移植的肾脏，目前有超过9万个候选肾脏 每年进行的移植手术不到22,000例，但在等待名单上。超过8000名患者被从 在等待录用期间，由于死亡或健康状况恶化，每年都有等待名单。尽管有这种迫切的需求 器官，近20%的被回收的肾脏最终被丢弃。最常被报道的原因是 丢弃是不利于供体活检的组织学检查。这些移植前的活组织检查是为了 评估器官的质量，并经常被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是有争议的，人们越来越担心。 关于来自活检解释的数据的可靠性和重复性，这是由于病理学家之间的 可变性。最近的证据表明，受者移植的结果只与供者的活组织检查有关。 由有经验的肾脏病理学家进行解释。然而，大多数移植中心没有更多的 而不是几个专业的肾脏病理学家；考虑到器官恢复通常发生在远程 医院在深夜或周末，活组织检查通常由随叫随到的病理学家解释，而不是 肾脏组织学方面的专门培训。这些提供者往往高估了慢性损伤的严重程度， 导致不适当地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，可以与人类相媲美或超过人类 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 实现以下目标的技术：(1)开发可靠和准确地预测后- 来自数字化采购活组织切片和供受者指标的移植移植物功能 移植受者科学登记(SRTR)数据集；(2)比较我们的 CNN对目前可用的捐赠者风险评分；以及(3)定性评估CNN的可采纳性、可接受性、 和临床医生的实用性。考虑到我们团队在机器学习方面的专业知识，这些目标是高度可行的 移植，以及SRTR数据的分析。 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真实- 移植后移植成功的时间估计，以帮助指导患者咨询。如果提议的目标是 我们CNN的反馈可以防止数以千计的肾脏被不当丢弃 通过在全国范围内增加移植手术的数量来降低等待名单上的死亡率。通过 在进行这项研究时，Eagleson博士将培养一套包括国家注册数据分析在内的技能， 定性方法和机器学习：正在迅速使用的重要现代技术 并将在她作为一名独立外科医生兼科学家的整个职业生涯中发挥很好的作用。