Publications by authors named "Eliot G Peyster"

8 Publications

  • Page 1 of 1

Is it Time for Trials on Preventing Immune-Mediated Myocardial Damage?

JACC Basic Transl Sci 2021 Jun 28;6(6):543-545. Epub 2021 Jun 28.

Cardiovascular Research Institute, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

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http://dx.doi.org/10.1016/j.jacbts.2021.05.005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8246058PMC
June 2021

An automated computational image analysis pipeline for histological grading of cardiac allograft rejection.

Eur Heart J 2021 06;42(24):2356-2369

Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Nord Hall Suite 500, Cleveland, OH 44106, USA.

Aim: Allograft rejection is a serious concern in heart transplant medicine. Though endomyocardial biopsy with histological grading is the diagnostic standard for rejection, poor inter-pathologist agreement creates significant clinical uncertainty. The aim of this investigation is to demonstrate that cellular rejection grades generated via computational histological analysis are on-par with those provided by expert pathologists.

Methods And Results: The study cohort consisted of 2472 endomyocardial biopsy slides originating from three major US transplant centres. The 'Computer-Assisted Cardiac Histologic Evaluation (CACHE)-Grader' pipeline was trained using an interpretable, biologically inspired, 'hand-crafted' feature extraction approach. From a menu of 154 quantitative histological features relating the density and orientation of lymphocytes, myocytes, and stroma, a model was developed to reproduce the 4-grade clinical standard for cellular rejection diagnosis. CACHE-grader interpretations were compared with independent pathologists and the 'grade of record', testing for non-inferiority (δ = 6%). Study pathologists achieved a 60.7% agreement [95% confidence interval (CI): 55.2-66.0%] with the grade of record, and pair-wise agreement among all human graders was 61.5% (95% CI: 57.0-65.8%). The CACHE-Grader met the threshold for non-inferiority, achieving a 65.9% agreement (95% CI: 63.4-68.3%) with the grade of record and a 62.6% agreement (95% CI: 60.3-64.8%) with all human graders. The CACHE-Grader demonstrated nearly identical performance in internal and external validation sets (66.1% vs. 65.8%), resilience to inter-centre variations in tissue processing/digitization, and superior sensitivity for high-grade rejection (74.4% vs. 39.5%, P < 0.001).

Conclusion: These results show that the CACHE-grader pipeline, derived using intuitive morphological features, can provide expert-quality rejection grading, performing within the range of inter-grader variability seen among human pathologists.
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http://dx.doi.org/10.1093/eurheartj/ehab241DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8216729PMC
June 2021

In Situ Immune Profiling of Heart Transplant Biopsies Improves Diagnostic Accuracy and Rejection Risk Stratification.

JACC Basic Transl Sci 2020 Apr 1;5(4):328-340. Epub 2020 Apr 1.

Cardiovascular Research Institute, University of Pennsylvania, Philadelphia, Pennsylvania.

Recognizing that guideline-directed histologic grading of endomyocardial biopsy tissue samples for rejection surveillance has limited diagnostic accuracy, quantitative, in situ characterization was performed of several important immune cell types in a retrospective cohort of clinical endomyocardial tissue samples. Differences between cases were identified and were grouped by histologic grade versus clinical rejection trajectory, with significantly increased programmed death ligand 1+, forkhead box P3+, and cluster of differentiation 68+ cells suppressed in clinically evident rejections, especially cases with marked clinical-histologic discordance. Programmed death ligand 1+, forkhead box P3+, and cluster of differentiation 68+ cell proportions are also significantly higher in "never-rejection" when compared with "future-rejection." These findings suggest that in situ immune modulators regulate the severity of cardiac allograft rejection.
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http://dx.doi.org/10.1016/j.jacbts.2020.01.015DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7188920PMC
April 2020

Tunable and Reversible Substrate Stiffness Reveals a Dynamic Mechanosensitivity of Cardiomyocytes.

ACS Appl Mater Interfaces 2019 Jun 30;11(23):20603-20614. Epub 2019 May 30.

Department of Physics , Bryn Mawr College , Bryn Mawr , Pennsylvania 19010 , United States.

New directions in material applications have allowed for the fresh insight into the coordination of biophysical cues and regulators. Although the role of the mechanical microenvironment on cell responses and mechanics is often studied, most analyses only consider static environments and behavior, however, cells and tissues are themselves dynamic materials that adapt in myriad ways to alterations in their environment. Here, we introduce an approach, through the addition of magnetic inclusions into a soft poly(dimethylsiloxane) elastomer, to fabricate a substrate that can be stiffened nearly instantaneously in the presence of cells through the use of a magnetic gradient to investigate short-term cellular responses to dynamic stiffening or softening. This substrate allows us to observe time-dependent changes, such as spreading, stress fiber formation, Yes-associated protein translocation, and sarcomere organization. The identification of temporal dynamic changes on a short time scale suggests that this technology can be more broadly applied to study targeted mechanisms of diverse biologic processes, including cell division, differentiation, tissue repair, pathological adaptations, and cell-death pathways. Our method provides a unique in vitro platform for studying the dynamic cell behavior by better mimicking more complex and realistic microenvironments. This platform will be amenable to future studies aimed at elucidating the mechanisms underlying mechanical sensing and signaling that influence cellular behaviors and interactions.
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http://dx.doi.org/10.1021/acsami.9b02446DOI Listing
June 2019

A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue.

PLoS One 2018 3;13(4):e0192726. Epub 2018 Apr 3.

Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States of America.

Over 26 million people worldwide suffer from heart failure annually. When the cause of heart failure cannot be identified, endomyocardial biopsy (EMB) represents the gold-standard for the evaluation of disease. However, manual EMB interpretation has high inter-rater variability. Deep convolutional neural networks (CNNs) have been successfully applied to detect cancer, diabetic retinopathy, and dermatologic lesions from images. In this study, we develop a CNN classifier to detect clinical heart failure from H&E stained whole-slide images from a total of 209 patients, 104 patients were used for training and the remaining 105 patients for independent testing. The CNN was able to identify patients with heart failure or severe pathology with a 99% sensitivity and 94% specificity on the test set, outperforming conventional feature-engineering approaches. Importantly, the CNN outperformed two expert pathologists by nearly 20%. Our results suggest that deep learning analytics of EMB can be used to predict cardiac outcome.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0192726PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5882098PMC
July 2018

Advanced Morphologic Analysis for Diagnosing Allograft Rejection: The Case of Cardiac Transplant Rejection.

Transplantation 2018 08;102(8):1230-1239

Cardiovascular Research Institute, University of Pennsylvania, Philadelphia, PA.

Allograft rejection remains a significant concern after all solid organ transplants. Although qualitative morphologic analysis with histologic grading of biopsy samples is the main tool employed for diagnosing allograft rejection, this standard has significant limitations in precision and accuracy that affect patient care. The use of endomyocardial biopsy to diagnose cardiac allograft rejection illustrates the significant shortcomings of current approaches for diagnosing allograft rejection. Despite disappointing interobserver variability, concerns about discordance with clinical trajectories, attempts at revising the histologic criteria and efforts to establish new diagnostic tools with imaging and gene expression profiling, no method has yet supplanted endomyocardial biopsy as the diagnostic gold standard. In this context, automated approaches to complex data analysis problems-often referred to as "machine learning"-represent promising strategies to improve overall diagnostic accuracy. By focusing on cardiac allograft rejection, where tissue sampling is relatively frequent, this review highlights the limitations of the current approach to diagnosing allograft rejection, introduces the basic methodology behind machine learning and automated image feature detection, and highlights the initial successes of these approaches within cardiovascular medicine.
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http://dx.doi.org/10.1097/TP.0000000000002189DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6059998PMC
August 2018

Atherosclerotic plaque inflammation varies between vascular sites and correlates with response to inhibition of lipoprotein-associated phospholipase A2.

J Am Heart Assoc 2015 Feb 11;4(2). Epub 2015 Feb 11.

Hospital of the University of Pennsylvania, Philadelphia, PA (R.S.F., D.H., E.G.P., E.R.M., S.K., R.L.W.).

Background: Despite systemic exposure to risk factors, the circulatory system develops varying patterns of atherosclerosis for unclear reasons. In a porcine model, we investigated the relationship between site-specific lesion development and inflammatory pathways involved in the coronary arteries (CORs) and distal abdominal aortas (AAs).

Methods And Results: Diabetes mellitus (DM) and hypercholesterolemia (HC) were induced in 37 pigs with 3 healthy controls. Site-specific plaque development was studied by comparing plaque severity, macrophage infiltration, and inflammatory gene expression between CORs and AAs of 17 DM/HC pigs. To assess the role of lipoprotein-associated phospholipase A2 (Lp-PLA2) in plaque development, 20 DM/HC pigs were treated with the Lp-PLA2 inhibitor darapladib and compared with the 17 DM/HC untreated pigs. DM/HC caused site-specific differences in plaque severity. In the AAs, normalized plaque area was 4.4-fold higher (P<0.001) and there were more fibroatheromas (9 of the 17 animals had a fibroatheroma in the AA and not the COR, P=0.004), while normalized macrophage staining area was 1.5-fold higher (P=0.011) compared with CORs. DM/HC caused differential expression of 8 of 87 atherosclerotic genes studied, including 3 important in inflammation with higher expression in the CORs. Darapladib-induced attenuation of normalized plaque area was site-specific, as CORs responded 2.9-fold more than AAs (P=0.045).

Conclusions: While plaque severity was worse in the AAs, inflammatory genes and inflammatory pathways that use Lp-PLA2 were more important in the CORs. Our results suggest fundamental differences in inflammation between vascular sites, an important finding for the development of novel anti-inflammatory therapeutics.
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http://dx.doi.org/10.1161/JAHA.114.001477DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4345873PMC
February 2015
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