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: 10687842
• 负责人: Anant Madabhushi
• 金额: $ 76.7万
• 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/10687842
• 关键词: Acute; 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; 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; Life; Lung Transplantation; 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; Recommendation; 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; biomarker discovery; biomarker identification; biomarker validation; case history; clinical predictors; clinically significant; cohort; diagnostic accuracy; diagnostic strategy; diagnostic tool; empowerment; feature detection; graft failure; heart allograft; heart imaging; improved; innovation; molecular marker; novel; overtreatment; pathology imaging; phenotypic data; 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)仍然是一种相对 常见和严重的并发症，增加急性移植物衰竭和患者不良反应的风险 结果。三十年来，心内膜心肌活检(EMB)与组织学分级，由美国心脏协会推荐 国际心肺移植学会(ISHLT)已成为CAR的广泛应用标准 诊断。然而，人们普遍认识到，ISHLT拒绝分级标准缺乏诊断准确性 并且辨别排斥机制的能力有限。这些限制使患者面临两者的风险 过度治疗和治疗不足，并强调对更准确和更有信息量的未得到满足的需求 子宫肌瘤标本的组织病理学分析方法。我们的团队在计算病理图像领域处于领先地位 分析了该领域200多篇论文和30项专利。我们已经开发和评估了一个 计算机辅助组织病理学分级评估(CACHE)方案：(1)在N=205例患者中，有一个区域 根据受试者工作特征曲线(AUC)=0.95与两位心脏病理学家(平均值 AUC=0.74)区分正常和衰竭心脏；(2)可以区分低度和高度ISHLT排斥反应 N=1109名患者的评分超过了训练有素的心脏病理学家。认识到 ISHLT排斥级别和排斥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以确定临床危重患者新的“等级不可知”的形态生物标记物。 我们的科学前提是，形态生物标记物根据它们与患者临床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准确、一致和 诊断同种异体移植排斥反应的信息分类器。为了支持这一假设，拟议的研究将 解决三个具体目标。在目标1中，我们将利用计算图像分析来发现形态 排斥反应相关损伤的生物标记物，需要开发一种能够评估临床的分类器 汽车的轨迹。在目标2中，我们将提供目标1中确定的生物标志物的机械注释 使用定制多参数免疫荧光仪表板与原位免疫标记物的相关性。在AIM 3，我们采用多中心的前瞻性队列来验证新的诊断和机械准确性 在AIMS 1和AIMS 2中开发了拒绝分类器。最终，开发了一种更准确和更机械的分类器 CAR形态诊断的信息化工具将通过减少过度和不足来改善患者的预后 治疗和启发在其他器官移植中的应用。有趣的是，一种高级组织学的发展 诊断工具将推动开发替代的、无需活检的诊断方法，这些方法已经 由于必须与有缺陷的ISHLT拒绝等级作为参考标准进行比较而受到阻碍。

项目成果

The evolving use of biomarkers in heart transplantation: Consensus of an expert panel.

• DOI: 10.1016/j.ajt.2023.02.025
• 发表时间: 2023-06-01
• 期刊: American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons
• 作者: Kobashigawa, Jon;Hall, Shelley;Peyster, Eliot
• 通讯作者: Peyster, Eliot

Computational Analysis of Routine Biopsies Improves Diagnosis and Prediction of Cardiac Allograft Vasculopathy.

• DOI: 10.1161/circulationaha.121.058459
• 发表时间: 2022-05-24
• 期刊: CIRCULATION
• 影响因子: 37.8
• 作者: Peyster, Eliot G.;Janowczyk, Andrew;Swamidoss, Abigail;Kethireddy, Samhith;Feldman, Michael D.;Margulies, Kenneth B.
• 通讯作者: Margulies, Kenneth B.