Computational Image Analysis of Renal Transplant Biopsies to Predict Graft Outcome

肾移植活检的计算图像分析以预测移植结果

• 批准号: 10733292
• 负责人: Kuang-Yu Jen
• 金额: $ 60.5万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-06 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10733292
• 关键词: Address; Agreement; Allografting; Area; Artificial Intelligence; Atrophic; Attention; Automation; Benchmarking; Biopsy; Categories; Chronic; Clinical; Clinical Data; Collection; Computer Vision Systems; Data; Data Analyses; Diagnosis; Diagnostic; Dialysis procedure; Disease Progression; End stage renal failure; Event; Feedback; Fibrosis; Future; Glomerular Filtration Rate; Goals; Image; Image Analysis; Injury to Kidney; Intelligence; Internet; Kidney; Kidney Transplantation; Learning; Machine Learning; Maryland; Measures; Methods; Modality; Modeling; Organ; Organ Donor; Outcome; Pathologist; Pathology; Patients; Pattern; Performance; Play; Protocols documentation; Quality of life; Research Personnel; Resolution; Semantics; Surgeon; Testing; Time; Training; Transplant Recipients; Transplant Surgeon; Transplantation; Tubular formation; Vascular Diseases; Visual; Work; analytical method; cloud based; cohort; computerized tools; data-driven model; deep learning model; demographics; digital; digital imaging; digital pathology; empowerment; field study; glomerulosclerosis; graft dysfunction; graft function; health assessment; health data; implantation; improved; improved outcome; indexing; interoperability; interstitial; kidney allograft; kidney biopsy; machine learning method; multi-task learning; multimodality; novel; organ allocation; outcome prediction; post-transplant; prognostic model; recruit; second transplant; social health determinants; standard of care; tool; transplant centers; treatment strategy; usability; user-friendly; web-based tool; whole slide imaging

Project Summary Kidney transplantation is the most effective modality for treating end stage kidney disease. It provides superior quality of life and significantly improves survival over dialysis. However, the demand for kidney transplants has surpassed the supply of usable organs. Because of this deficit, it is important to improve the outcomes of first- time transplant recipients through intelligent management, thereby optimizing donor organ allocation and reducing the need for secondary transplants. In assessing the health of a renal allograft, time is of critical importance. Being able to precisely predict delayed graft dysfunction and modifying treatment strategies accordingly would be greatly impactful in decreasing chronic rejection events. Existing clinical methods, such as the Kidney Donor Profile Index, which are based solely on donor demographics and clinical data, are minimally to moderately predictive of allograft outcomes. Further, current visual, semi-quantitative transplant biopsy scoring metrics, e.g., Banff, the Maryland Aggregate Pathology Index, and Remuzzi are often not predictive of renal graft function. Digital image analytical methods that quantify chronic changes in kidney that cannot be done visually, may offer clues to long-term allograft outcome. Therefore, to address the unmet need of intelligent renal transplant management, we propose a comprehensive multimodal framework, integrating high-resolution renal transplant biopsy digital whole-slide images (WSIs), and donor and recipient clinical, demographic, and social determinants of health data. Using this framework we will combine computer vision and explainable artificial intelligence (XAI) tools to derive autonomous diagnostic and prognostic models for data-driven, long-term management of renal allografts. As part of their preliminary work, the investigator team has developed a computational tool to quantify interstitial fibrosis and tubular atrophy, a chronicity measure in renal transplant biopsies, and demonstrated that the prediction of estimated glomerular filtration rate at a later time-point after biopsy using machine learning (ML)-derived image features outperforms those based on routine visual assessment. This tool will be expanded to incorporate a variety of additional analyses including robust segmentation of renal compartments in WSIs, leveraging pathologist guided attention to train deep-learning models, state-of-the-art transformer models for multi-task learning, and XAI to increase interoperability and accessibility of ML-derived predictions to pathologists. The performance of this pipeline to predict renal allograft function in a future time-point will be compared with existing methods used in a clinical setting as well as ML- based methods used for explainable prediction of disease progression in other areas of digital pathology. The tool will be deployed on a cloud-based platform and the usability by important stakeholders, namely, transplant renal pathologists, nephrologists, and surgeons will be studied with a goal to eventually include the tool in clinical workflows. The proposed work will be an invaluable asset for clinicians to take advantage of large collections of renal transplant biopsy WSIs and inform treatment decisions towards improving renal allograft function.

项目概要 肾移植是治疗终末期肾病最有效的方法。它提供了优越的 生活质量并显着提高透析的生存率。然而，肾移植的需求却不断增加。 超过了可用器官的供应。由于这种缺陷，改善第一阶段的结果非常重要 通过智能管理定时移植受者，从而优化供体器官分配和 减少二次移植的需要。在评估同种异体肾移植物的健康状况时，时间至关重要 重要性。能够精确预测迟发性移植物功能障碍并修改治疗策略 因此，对于减少慢性排斥反应事件将产生巨大影响。现有的临床方法，如 肾脏捐赠者概况指数仅基于捐赠者人口统计数据和临床数据，最低限度是 适度预测同种异体移植结果。此外，目前的视觉、半定量移植活检评分 指标，例如 Banff、马里兰州综合病理学指数和 Remuzzi 通常不能预测肾移植 功能。数字图像分析方法可以量化肉眼无法完成的肾脏慢性变化， 可能为长期同种异体移植结果提供线索。因此，为了解决智能肾未满足的需求 移植管理，我们提出了一个全面的多模式框架，整合了高分辨率肾脏 移植活检数字化全切片图像 (WSI)，以及供者和受者的临床、人口统计和社会特征 健康数据的决定因素。使用这个框架，我们将结合计算机视觉和可解释的人工 智能 (XAI) 工具，用于导出数据驱动的长期自主诊断和预测模型 同种异体肾移植的管理。作为前期工作的一部分，研究小组开发了一个 量化间质纤维化和肾小管萎缩（肾移植的慢性测量）的计算工具 活检，并证明了预测肾小球滤过率在稍后时间点的预测 使用机器学习 (ML) 衍生的图像特征进行的活检优于基于常规视觉的活检 评估。该工具将扩展以纳入各种附加分析，包括稳健的分析 WSI 中肾区的分割，利用病理学家引导的注意力来训练深度学习 模型、用于多任务学习的最先进的 Transformer 模型以及用于提高互操作性和 病理学家可以使用机器学习得出的预测。该管道预测肾同种异体移植物的性能 未来时间点的功能将与临床环境中使用的现有方法以及 ML- 用于数字病理学其他领域疾病进展的可解释预测的基于方法。这 工具将部署在基于云的平台上，并由重要利益相关者使用，即移植 肾脏病理学家、肾病学家和外科医生将进行研究，目标是最终将该工具纳入临床 工作流程。拟议的工作对于临床医生来说将是一笔无价的资产，可以利用大量的数据 肾移植活检 WSI 并为改善肾同种异体移植功能的治疗决策提供信息。