Computer-Assisted Histologic Evaluation of Cardiac Allograft Rejection

心脏同种异体移植排斥反应的计算机辅助组织学评估

• 批准号: 10246527
• 负责人: Anant Madabhushi
• 金额: $ 79万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10246527
• 关键词: Acute; Address; Allografting; Antibodies; Archives; Area; Benign; Biological Markers; Biopsy; Biopsy Specimen; Cardiac; Cells; Classification; Clinical; Clinical Trials Design; Complication; Computer Assisted; Computer Vision Systems; Custom; Data; Derivation procedure; Development; Diagnosis; Diagnostic; Disabled Persons; Disease; Evaluation; Event; Flow Cytometry; Functional disorder; Gene Expression Profiling; Graft Rejection; Guidelines; Heart; Heart Transplantation; Heart-Lung Transplantation; Histologic; Histopathologic Grade; Histopathology; Human; Image; Image Analysis; Immune; Immune System Diseases; Immunofluorescence Immunologic; Immunologic Markers; Immunologics; In Situ; Injury; International; Interobserver Variability; Intervention; Journals; Legal patent; Lymphocyte; Machine Learning; Malignant neoplasm of lung; Mediating; Medical; Methods; Molecular; Monitor; Morphology; Organ Transplantation; Outcome; Paper; Pathologist; Patient-Focused Outcomes; Patients; Pattern; Performance; Population; Prevention; Prognosis; Prospective cohort; Protocols documentation; ROC Curve; Recurrence; Reference Standards; Research; Retrospective cohort; Risk; Sampling; Scheme; Services; Severities; Slide; Societies; Stains; Syndrome; Technology; Testing; Therapeutic; Tissue imaging; Tissues; Training; Transplant Recipients; Transplantation; allograft rejection; antibody-mediated rejection; base; biomarker discovery; biomarker validation; clinical predictors; clinically significant; cohort; diagnostic accuracy; feature detection; graft failure; heart allograft; heart imaging; improved; innovation; molecular marker; novel; overtreatment; pathology imaging; phenotypic data; population based; post-transplant; prospective; screening; success; tool; transplant centers; treatment as usual; treatment choice

Project Summary Though cardiac transplantation is a lifesaving intervention, cardiac allograft rejection (CAR) remains a relatively common and serious complication that confers an increased risk of acute graft failure and adverse patient outcomes. For three decades, endomyocardial biopsy (EMB) with histological grading, as recommended by the International Society of Heart and Lung Transplantation (ISHLT) has been the broadly applied standard for CAR diagnosis. However, it is widely appreciated that the ISHLT rejection grading standard lacks diagnostic accuracy and has limited ability to discern the mechanism of rejection. These limitations expose patients to risks of both over-treatment and under-treatment, and highlight the unmet need for more accurate and informative approaches to histopathologic analysis of EMB samples. Our team is a leader in computational pathology image analysis with over 200 papers and >30 issued patents in this area. We have already developed and evaluated a computer assisted histopathology grading evaluation (CACHE) scheme which (1) in N=205 patients, had an area under the receiver operating characteristic curve (AUC)=0.95 compared to two cardiac pathologists (mean AUC=0.74) in distinguishing normal from failing hearts and (2) could distinguish low and high ISHLT rejection grades in N=1109 patients with a performance that exceeds that of trained cardiac pathologists. Recognizing the frequent discordance between ISHLT rejection grade and the clinical trajectory of a rejection event, we will further develop and optimize CACHE to identify new “grade agnostic” morphologic biomarkers of clinically serious CAR. Our scientific premise is that morphologic biomarkers prioritized based on their correlation to patients’ clinical trajectories and underlying immunological disease mechanisms will generate an accurate, consistent and informative classifier for diagnosing allograft rejection. In service of this hypothesis, the proposed research will address three specific aims. In Aim 1, we will utilize computational image analysis to discover the morphologic biomarkers of rejection-related injury which are needed to develop a classifier capable of assessing the clinical trajectory of CAR. In Aim 2, we will provide mechanistic annotation of biomarkers identified in Aim 1 through correlation with in-situ immunologic markers using custom multi-parameter immunofluorescence panels. In Aim 3, we employ a multicenter, prospective cohort to validate the diagnostic and mechanistic accuracy of the new rejection classifier developed in Aims 1 and 2. Ultimately, development of a more accurate and mechanistically informative tool for morphologic diagnosis of CAR will improve patient outcomes by reducing over- and under- treatment and inspire applications in other organ transplants. Interestingly, development of a superior histologic diagnostic tool will empower development of alternative, biopsy-free diagnostic approaches that have been handicapped by the necessity of comparison with the flawed ISHLT rejection grade as a reference standard.

项目摘要 尽管心脏移植是一种救生干预措施，但心脏同种异体移植拒绝（CAR）仍然是相对的 常见和严重的并发症承认急性移植失败和不良患者的风险增加 结果。三十年来，按照组织学分级的心内膜活检（EMB），如 国际心脏和肺移植学会（ISHLT）已成为汽车广泛应用的标准 诊断。但是，人们普遍认为，ISHLT排斥分级标准缺乏诊断精度 并具有有限的识别拒绝机制的能力。这些限制使患者面临着两者的风险 过度治疗和治疗不足，并突出未满足的需求，以获得更准确和信息性的 对EMB样品的组织病理学分析的方法。我们的团队是计算病理图像的领导者 该领域的200多篇论文和> 30份专利的分析。我们已经开发并评估了 计算机辅助组织病理学分级评估（CACH）方案（1）在n = 205位患者中，有一个区域 在接收器操作特征曲线（AUC）下= 0.95，而两名心脏病理学家（平均 AUC = 0.74）在区分正常和（2）可以区分低和高的ISHLT排斥反应时 N = 1109例表现超过训练有素的心脏病理学家的患者的成绩。认识到 ISHLT排斥等级与拒绝事件的临床轨迹之间的频繁不一致，我们将进一步 开发和优化缓存，以识别临床上严重汽车的新的“级别不可知论”形态学。 我们的科学前提是形态生物标志物基于患者的临床相关性优先考虑 轨迹和潜在的免疫疾病机制将产生准确，一致和 诊断性分类者拒绝的信息分类器。为了为这一假设提供服务，拟议的研究将 解决三个具体目标。在AIM 1中，我们将利用计算图像分析来发现形态学 与排斥相关损伤的生物标志物，需要开发能够评估临床的分类器 汽车轨迹。在AIM 2中，我们将提供AIM 1中确定的生物标志物的机械注释 使用自定义多参数免疫荧光面板与原位免疫标记相关。目标 3，我们采用多中心，前瞻性队列来验证新的诊断和机械精度 拒绝分类器在目标1和2中开发。最终，开发更准确，更机械的 通过减少过度和不足，可以改善汽车形态诊断的信息工具 治疗并激发其他器官移植中的应用。有趣的是，发展出色的组织学 诊断工具将赋予开发替代，活检的无活检诊断方法的能力 与有缺陷的ISHLT排斥等级作为参考标准的必要比较所残障。