The XVth Banff Conference on Allograft Pathology the Banff Workshop Heart Report: Improving the diagnostic yield from endomyocardial biopsies and Quilty effect revisited

Duong, Jean-Paul; Fedrigo, Marny; Fishbein, Gregory A.; Leone, Ornella; Neil, Desley; Marboe, Charles C.; Peyster, Eliot; Thüsen, Jan von der; Loupy, Alexandre; Mengel, Michael; Revelo, Mônica P.; Adam, Benjamin; Bruneval, Patrick; Angelini, Annalisa; Miller, Dylan V.; Berry, Gerald J.

doi:10.1111/ajt.16083

Cited by 19 publications

(15 citation statements)

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“…The present work joins a small but growing body of research deploying computer-assisted histology workflows within cardiovascular medicine. [27][28][29][30]52,53 Recent efforts have focused on diagnostic pipelines, evaluating cardiac tissue to better describe disease states as they currently exist. Specifically within cardiac transplant, several studies have used quantitative analyses to examine transplant EMBs in recent years.…”

Section: Discussionmentioning

confidence: 99%

“…To date, no study has attempted to pursue a rigorous analysis of the histological data contained within routine EMB tissues as a means of generating better, more personalized CAV risk assessments. This can be considered an unmet need 26 and an emerging opportunity, 27 given the promise computational digital pathology analysis has shown for providing clinically valuable predictions in heart transplant medicine [28][29][30] and beyond. [31][32][33][34][35] In this proof-of-concept study, we perform computational image analysis of digitized EMB histology slides using an interpretable, handcrafted method to discover and measure novel histological biomarkers associated with the development of CAV.…”

Section: Clinical Perspectivementioning

confidence: 99%

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confidence: 99%

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

et al. 2022

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Background: Cardiac allograft vasculopathy (CAV) is a leading cause of morbidity and mortality for heart transplant recipients. While clinical risk factors for CAV have been established, no personalized prognostic test exists to confidently identify patients at high vs. low risk of developing aggressive CAV. The aim of this investigation was to leverage computational methods for analyzing digital pathology images from routine endomyocardial biopsies (EMB) to develop a precision medicine tool for predicting CAV years before overt clinical presentation. Methods: Clinical data from 1-year post-transplant was collected on 302 transplant recipients from the University of Pennsylvania, including 53 'early CAV' patients and 249 'no-CAV' controls. This data was used to generate a 'clinical model' (ClinCAV-Pr) for predicting future CAV development. From this cohort, n=183 archived EMBs were collected for CD31 and modified trichrome staining and then digitally scanned. These included 1-year post-transplant EMBs from 50 'early CAV' patients and 82 no-CAV patients, as well as 51 EMBs from 'disease control' patients obtained at the time of definitive coronary angiography confirming CAV. Using biologically-inspired, hand-crafted features extracted from digitized EMBs, quantitative histologic models for differentiating no-CAV from disease controls (HistoCAV-Dx), and for predicting future CAV from 1-year post-transplant EMBs were developed (HistoCAV-Pr). The performance of histologic and clinical models for predicting future CAV (i.e. HistoCAV-Pr and ClinCAV-Pr, respectively) were compared in a held-out validation set, before being combined to assess the added predictive value of an integrated predictive model (iCAV-Pr). Results: ClinCAV-Pr achieved modest performance on the independent test set, with area under the receiver operating curve (AUROC) of 0.70. The HistoCAV-Dx model for diagnosing CAV achieved excellent discrimination, with an AUROC of 0.91, while HistoCAV-Pr model for predicting CAV achieved good performance with an AUROC of 0.80. The integrated iCAV-Pr model achieved excellent predictive performance, with an AUROC of 0.93 on the held-out test set. Conclusions: Prediction of future CAV development is greatly improved by incorporation of computationally extracted histologic features. These results suggest morphologic details contained within regularly obtained biopsy tissue have the potential to enhance precision and personalization of treatment plans for post-heart transplant patients.

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Section: Discussionmentioning

confidence: 99%

Section: Clinical Perspectivementioning

confidence: 99%

mentioning

confidence: 99%

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

et al. 2022

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“…Heart transplantation (HTx) remains the most valuable therapeutic option for patients with end-stage heart failure [ 1 , 2 , 3 ]. Despite major advances in immunosuppression, allograft rejection remains an important complication after heart transplantation, and it is associated with increased morbidity and mortality [ 4 , 5 , 6 ]. While the prevalence of acute cellular rejection (ACR) decreased over time from the initiation of the transplantation programs due to the improvements in immunosuppression [ 7 , 8 ], antibody-mediated rejection (AMR) is recognized as a major risk factor for all-cause mortality [ 9 , 10 ], cardiovascular mortality [ 11 , 12 ], and various types of allograft injury, including systolic dysfunction [ 5 , 13 ], restrictive physiology [ 14 ], and cardiac allograft vasculopathy [ 15 , 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

A Review of Biomarkers of Cardiac Allograft Rejection: Toward an Integrated Diagnosis of Rejection

2022

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Despite major advances in immunosuppression, allograft rejection remains an important complication after heart transplantation, and it is associated with increased morbidity and mortality. The gold standard invasive strategy to monitor and diagnose cardiac allograft rejection, based on the pathologic evaluation of endomyocardial biopsies, suffers from many limitations including the low prevalence of rejection, sample bias, high inter-observer variability, and international working formulations based on arbitrary cut-offs that simplify the landscape of rejection. The development of innovative diagnostic and prognostic strategies—integrating conventional histology, molecular profiling of allograft biopsy, and the discovery of new tissue or circulating biomarkers—is one of the major challenges of translational medicine in solid organ transplantation, and particularly in heart transplantation. Major advances in the field of biomarkers of rejection have paved the way for a paradigm shift in the monitoring and diagnosis of cardiac allograft rejection. We review the recent developments in the field, including non-invasive biomarkers to minimize the number of protocol endomyocardial biopsies and tissue biomarkers as companion tools of pathology to refine the diagnosis of cardiac rejection. Finally, we discuss the potential role of these biomarkers to provide an integrated bio-histomolecular diagnosis of cardiac allograft rejection.

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“…21 , 22 In the context of CAR, computational methods provide an opportunity to improve grading consistency and sensitivity via comprehensive and quantitative morphological assessments of EMB specimens. 23 The potential of these methods for transplant EMB analysis has recently been endorsed by the Banff Foundation for Allograft Pathology, 24 though no definitive demonstration of diagnostic performance with translational potential has yet been published.…”

Section: Introductionmentioning

confidence: 99%

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

Peyster

Arabyarmohammadi

Janowczyk

et al. 2021

European Heart Journal

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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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The XVth Banff Conference on Allograft Pathology the Banff Workshop Heart Report: Improving the diagnostic yield from endomyocardial biopsies and Quilty effect revisited

Cited by 19 publications

References 48 publications

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

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

A Review of Biomarkers of Cardiac Allograft Rejection: Toward an Integrated Diagnosis of Rejection

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

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