Computational Image Analysis of Renal Transplant Biopsies to Predict Graft Outcome

项目摘要 肾移植是治疗终末期肾病最有效的手段。它提供了卓越的 提高生活质量，显著提高透析患者的存活率。然而，对肾脏移植的需求已经 超过了可用器官的供应量。由于这一赤字，重要的是改善第一次- 通过智能管理为移植受者计时，从而优化供体器官分配和 减少二次移植的需要。在评估移植肾的健康状况时，时间是至关重要的。 重要性。能够准确预测迟发性移植物功能障碍并修改治疗策略 因此，在减少慢性排斥事件方面会有很大的影响。现有的临床方法，如 仅基于捐赠者人口统计和临床数据的肾脏捐赠者概况指数是最低的 以中等程度预测同种异体移植的结果。此外，目前的视觉、半定量移植活检评分 指标，例如Banff、马里兰综合病理指数和Remuzzi通常不能预测肾移植 功能。数字图像分析方法可以量化肾脏的慢性变化，这些变化不能通过视觉来完成， 可能为同种异体移植的长期结局提供线索。因此，为了解决智能肾脏的未得到满足的需求 移植管理，我们提出了一个全面的多模式框架，集成了高分辨率肾脏 移植活检数字全幻灯片图像(WSIS)，以及供者和受者的临床、人口统计和社会 健康数据的决定因素。利用这个框架，我们将把计算机视觉和可解释人工智能结合起来 智能(XAI)工具，可为数据驱动的长期自主诊断和预测模型提供支持 同种异体肾移植的管理作为他们前期工作的一部分，调查小组开发了一种 量化肾间质纤维化和肾小管萎缩的计算工具，这是肾移植中的一种慢性指标 活检，并证明了预测肾小球滤过率在以后的时间点 使用基于机器学习(ML)的图像特征进行活检的性能优于基于常规视觉的活检 评估。该工具将进行扩展，以纳入各种附加分析，包括Robust WSIS中肾间隔的分割，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转换器模型和XAI，以提高互操作性和 病理学家对ML派生预测的可及性。该管道在预测肾移植中的作用 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- 用于在数字病理学的其他领域对疾病进展进行可解释预测的基于方法。这个 工具将部署在基于云的平台上，并由重要的利益相关者进行可用性，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应用于临床。 工作流程。拟议的工作将是临床医生利用大量收集的 肾移植活检WSIS，并为改善移植肾功能的治疗决策提供信息。

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.