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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要尽管心脏移植是一种挽救生命的干预措施，但心脏同种异体移植排斥（CAR）仍然是一种相对常见且严重的并发症，会增加急性移植失败和不良患者的风险三十年来，按照组织学分级的建议进行心内膜心肌活检（EMB）。国际心肺移植学会 (ISHLT) 已成为广泛应用的 CAR 标准然而，人们普遍认为 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，这些限制使患者面临两种风险。过度治疗和治疗不足，并强调对更准确和信息更丰富的需求尚未得到满足我们的团队是计算病理学图像领域的领导者。我们已经开发并评估了该领域的 200 多篇论文和超过 30 项已发布的专利。计算机辅助组织病理学分级评估 (CACHE) 方案 (1) 在 N=205 名患者中，有一个面积与两位心脏病病理学家相比，受试者工作特征曲线 (AUC)=0.95（平均 AUC=0.74) 可以区分正常心脏和衰竭心脏，并且 (2) 可以区分低和高 ISHLT 排斥反应 N=1109 名患者的评分超过了受过训练的心脏病病理学家的评分。 ISHLT 排斥等级与排斥事件的临床轨迹之间经常存在不一致，我们将进一步开发和优化 CACHE，以识别临床严重 CAR 的新“级别不可知”形态生物标志物。我们的科学前提是，形态学生物标志物根据其与患者临床症状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、一致和用于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，拟议的研究将在目标 1 中，我们将利用计算图像分析来发现形态学。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在目标 2 中，我们将通过目标 1 提供生物标志物的机制注释。使用定制多参数免疫荧光面板与原位免疫标记物进行关联。 3，我们采用多中心前瞻性队列来验证新方法的诊断和机械准确性目标 1 和目标 2 中开发的拒绝分类器。最终，开发出更准确、更机械的分类器 CAR 形态学诊断的信息工具将通过减少过度和不足来改善患者的治疗结果治疗并激发其他器官移植的应用。诊断工具将有助于开发替代的、免活检的诊断方法由于必须与调味 ISHLT 剔除等级作为参考标准进行比较，因此存在缺陷。