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 虽然心脏移植是一种挽救生命的干预措施，但心脏移植排斥反应（CAR）仍然是一个相对严重的问题。 一种常见的严重并发症，可增加急性移植物衰竭和患者不良反应的风险 结果。30年来，肌内膜活检（EMB）与组织学分级，如推荐的 国际心肺移植学会（ISHLT）已成为广泛应用的CAR标准 诊断.然而，普遍认识到ISHLT排斥分级标准缺乏诊断准确性 并且辨别排斥机制的能力有限。这些局限性使患者面临两种风险 过度治疗和治疗不足，并强调对更准确和更翔实的 EMB样本的组织病理学分析方法。我们的团队是计算病理学图像的领导者 在该领域拥有超过200篇论文和超过30项已授权专利。我们已经开发并评估了一个 计算机辅助组织病理学分级评价（CACHE）方案，其中（1）在N=205例患者中，有一个区域 与两名心脏病理学家相比，受试者工作特征曲线下面积（AUC）=0.95（平均值 AUC=0.74）区分正常和衰竭心脏，（2）可区分低和高ISHLT排斥反应 在N=1109例患者中进行分级，其性能超过经过培训的心脏病理学家。认识到 ISHLT排斥反应分级和排斥反应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CACHE，以识别临床严重CAR的新的“等级不可知”形态学生物标志物。 我们的科学前提是，形态学生物标志物根据其与患者临床表现的相关性进行优先排序。 轨迹和潜在的免疫疾病机制将产生一个准确的，一致的， 用于诊断同种异体移植排斥的信息分类器。为了满足这一假设，拟议的研究将 提出三个具体目标。在目标1中，我们将利用计算机图像分析来发现 排斥相关损伤的生物标志物，这是开发能够评估临床排斥反应的分类器所需的。 车的轨迹。在目标2中，我们将提供目标1中鉴定的生物标志物的机制注释， 使用定制的多参数免疫荧光面板与原位免疫标记物相关。在Aim中 3，我们采用多中心，前瞻性队列，以验证新的诊断和机制的准确性， 在目标1和2中开发的拒绝分类器。最终，开发一种更准确、更机械的 CAR形态学诊断的信息工具将通过减少过度和不足， 治疗和启发在其他器官移植中的应用。有意思的是，发展出上级组织学 诊断工具将有助于开发替代的、无活检的诊断方法， 由于必须与作为参考标准的有缺陷的ISHLT拒绝等级进行比较而受到阻碍。