A web-based pilot study of inter-pathologist reproducibility using the ISHLT 2004 working formulation for biopsy diagnosis of cardiac allograft rejection: The European experience

Deep learning-enabled classification of kidney allograft rejection on whole slide histopathologic images

Background

Diagnosis of kidney transplant rejection currently relies on manual histopathological assessment, which is subjective and susceptible to inter-observer variability, leading to limited reproducibility. We aim to develop a deep learning system for automated assessment of whole-slide images (WSIs) from kidney allograft biopsies to enable detection and subtyping of rejection and to predict the prognosis of rejection.

Method

We collected H&E-stained WSIs of kidney allograft biopsies at 400x magnification from January 2015 to September 2023 at two hospitals. These biopsy specimens were classified as T cell-mediated rejection, antibody-mediated rejection, and other lesions based on the consensus reached by two experienced transplant pathologists. To achieve feature extraction, feature aggregation, and global classification, we employed multi-instance learning and common convolution neural networks (CNNs). The performance of the developed models was evaluated using various metrics, including confusion matrix, receiver operating characteristic curves, the area under the curve (AUC), classification map, heat map, and pathologist-machine confrontations.

Results

In total, 906 WSIs from 302 kidney allograft biopsies were included for analysis. The model based on multi-instance learning enables detection and subtyping of rejection, named renal rejection artificial intelligence model (RRAIM), with the overall 3-category AUC of 0.798 in the independent test set, which is superior to that of three transplant pathologists under nearly routine assessment conditions. Moreover, the prognosis models accurately predicted graft loss within 1 year following rejection and treatment response for rejection, achieving AUC of 0.936 and 0.756, respectively.

Conclusion

We first developed deep-learning models utilizing multi-instance learning for the detection and subtyping of rejection and prediction of rejection prognosis in kidney allograft biopsies. These models performed well and may be useful in assisting the pathological diagnosis.

Intrinsically explainable deep learning architecture for semantic segmentation of histological structures in heart tissue

BACKGROUND

Analysis of structures contained in tissue samples and the relevant contextual information is of utmost importance to histopathologists during diagnosis. Cardiac biopsies require in-depth analysis of the relationships between biological structures. Statistical measures are insufficient for determining a model's viability and applicability in the diagnostic process. A deeper understanding of predictions is necessary in order to support histopathologists.

METHODS

We propose a method for providing supporting information in the form of segmentation of histological structures to histopathologists based on these principles. The proposed method utilizes nuclei type and density information in addition to standard image input provided at two different zoom levels for the semantic segmentation of blood vessels, inflammation, and endocardium in heart tissue.

RESULTS

The proposed method was able to reach state-of-the-art segmentation results. The overall quality and viability of the predictions was qualitatively evaluated by two pathologists and a histotechnologist.

CONCLUSIONS

The decision process of the proposed deep learning model utilizes the provided information sources correctly and simulates the decision process of histopathologists via the usage of a custom-designed attention gate that provides a combination of spatial and encoder attention mechanisms. The implementation is available at https://github.com/mathali/IEDL-segmentation-of-heart-tissue.

Cardiac transplant rejection assessment with 18F-FDG PET-CT: initial single-centre experience for diagnosis and management

BACKGROUND

Rejection is a major cause of mortality and morbidity in heart transplant (HTx) recipients. Current methods for diagnosing rejection have limitations. Imaging methods to map the entire left ventricle and reliably identify potential sites of rejection is lacking. Animal studies suggest FDG PET-CT (FDG PET) could have potential application in human HTx recipients.

METHODS

Between December 2020 and February 2022, all HTx recipients at Harefield Hospital, London, with definite or suspected rejection underwent FDG PET in addition to routine work-up.

RESULTS

Thirty HTx recipients (12 with definite and 18 with suspected rejection) underwent FDG PET scans. Overall, 12 of the 30 patients had FDG PET with increased myocardial avidity, of whom 2 died (17%). Eighteen patients of the 30 patients had FDG PET with no myocardial avidity and all are alive (100%, p = 0.15). All patients with definite rejection, scanned within 2 weeks of starting anti-rejection treatment, showed increased myocardial avidity. In 5 cases, FDG PET showed myocardial avidity beyond 6 weeks despite pulsed steroid treatment, suggesting unresolved myocardial rejection.

CONCLUSION

Preliminary findings suggest FDG PET may have a role in diagnosing cardiac transplant rejection. Future blinded studies are needed to help further validate this.

Cardiac magnetic resonance imaging in detection of progressive graft dysfunction in pediatric heart transplantation

BACKGROUND

Chronic graft failure (CGF) in pediatric heart transplant (PHT) is multifactorial and may present with findings of fibrosis and microvessel disease (MVD) on endomyocardial biopsy (EMB). There is no optimal CGF surveillance method. We evaluated associations between cardiac magnetic resonance imaging (CMR) and historical/EMB correlates of CGF to assess CMR's utility as a surveillance method.

METHODS

Retrospective analysis of PHT undergoing comprehensive CMR between September 2015 and January 2022 was performed. EMB within 6 months was graded for fibrosis (scale 0-5) and MVD (number of capillaries with stenotic wall thickening per field of view). Correlation analysis and logistic regression were performed.

RESULTS

Forty-seven PHT with median age at CMR of 15.7 years (11.6, 19.3) and time from transplant of 6.4 years (4.1, 11.0) were studied. Cardiac allograft vasculopathy (CAV) was present in 11/44 (22.0%) and historical rejection in 14/41 (34.2%). CAV was associated with higher global T2 (49.0 vs. 47.0 ms; p = 0.038) and peak T2 (57.0 vs. 53.0 ms; p = 0.013) on CMR. Historical rejection was associated with higher global T2 (49.0 vs. 47.0 ms; p = 0.007) and peak T2 (57.0 vs. 53.0 ms; p = 0.03) as well as global extracellular volume (31.0 vs. 26.3%; p = 0.03). Higher fibrosis score on EMB correlated with smaller indexed left ventricular mass (rho = -0.34; p = 0.019) and greater degree of MVD with lower indexed left ventricular end-diastolic volume (rho = -0.35; p = 0.017).

CONCLUSION

Adverse ventricular remodeling and abnormal myocardial characteristics on CMR are present in PHT with CAV, historical rejection, as well as greater fibrosis and MVD on EMB. CMR has the potential use for screening of CGF.

Digital pathology in cardiac transplant diagnostics: from biopsies to algorithms

In the field of heart transplantation, the ability to accurately and promptly diagnose cardiac allograft rejection is crucial. This comprehensive review explores the transformative role of digital pathology and computational pathology, especially through machine learning, in this critical domain. These methodologies harness large datasets to extract subtle patterns and valuable information that extend beyond human perceptual capabilities, potentially enhancing diagnostic outcomes. Current research indicates that these computer-based systems could offer accuracy and performance matching, or even exceeding, that of expert pathologists, thereby introducing more objectivity and reducing observer variability. Despite promising results, several challenges such as limited sample sizes, diverse data sources, and the absence of standardized protocols pose significant barriers to the widespread adoption of these techniques. The future of digital pathology in heart transplantation diagnostics depends on utilizing larger, more diverse patient cohorts, standardizing data collection, processing, and evaluation protocols, and fostering collaborative research efforts. The integration of various data types, including clinical, demographic, and imaging information, could further refine diagnostic precision. As researchers address these challenges and promote collaborative efforts, digital pathology has the potential to become an integral part of clinical practice, ultimately improving patient care in heart transplantation.

Reproducibility of Rejection Grading in Uterus Transplantation: A Multicenter Study

Background

Diagnosis of rejection after uterus transplantation is based on histopathological examination of ectocervical biopsies. Inflammation at the stromal-epithelial interface is the backbone of the histopathological classification proposed by our group in 2017. However, the reproducibility of this grading scheme has not been tested, and it is unclear whether it covers the full morphological spectrum of rejection.

Methods

We present a multicenter study in which 5 pathologists from 4 uterus transplantation centers performed 2 rounds of grading on 145 and 48 cervical biopsies, respectively. Three of the centers provided biopsies. Additionally, the presence of perivascular stromal inflammation was recorded. During discussions after the first round, further histological lesions (venous endothelial inflammation and apoptosis) were identified for closer evaluation and added to the panel of lesions to score in the second round. All participants completed a questionnaire to explore current practices in handling and reporting uterus transplant biopsies.

Results

Cervical biopsies were commonly performed in all centers to monitor rejection. Intraobserver reproducibility of rejection grading (performed by 1 rater) was excellent, whereas interobserver reproducibility was moderate and did not improve in the second round. Reproducibility of perivascular stromal inflammation was moderate but unsatisfactory for venous endothelial inflammation and apoptosis. All lesions were more frequent in, but not restricted to, biopsies with rejection patterns.

Conclusions

Grading of rejection in cervical biopsies is reproducible and applicable to biopsies from different centers. Diagnosis of rejection may be improved by adding further histological lesions to the grading system; however, lesions require rigorous consensus definition.

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

In heart transplantation, the use of biomarkers to detect the risk of rejection has been evolving. In this setting, it is becoming less clear as to what is the most reliable test or combination of tests to detect rejection and assess the state of the alloimmune response. Therefore, a virtual expert panel was organized in heart and kidney transplantation to evaluate emerging diagnostics and how they may be best utilized to monitor and manage transplant patients. This manuscript covers the heart content of the conference and is a work product of the American Society of Transplantation's Thoracic and Critical Care Community of Practice. This paper reviews currently available and emerging diagnostic assays and defines the unmet needs for biomarkers in heart transplantation. Highlights of the in-depth discussions among conference participants that led to development of consensus statements are included. This conference should serve as a platform to further build consensus within the heart transplant community regarding the optimal framework to implement biomarkers into management protocols and to improve biomarker development, validation and clinical utility. Ultimately, these biomarkers and novel diagnostics should improve outcomes and optimize quality of life for our transplant patients.

Prediction of heart transplant rejection from routine pathology slides with self-supervised deep learning

Aims

One of the most important complications of heart transplantation is organ rejection, which is diagnosed on endomyocardial biopsies by pathologists. Computer-based systems could assist in the diagnostic process and potentially improve reproducibility. Here, we evaluated the feasibility of using deep learning in predicting the degree of cellular rejection from pathology slides as defined by the International Society for Heart and Lung Transplantation (ISHLT) grading system.

Methods and results

We collected 1079 histopathology slides from 325 patients from three transplant centres in Germany. We trained an attention-based deep neural network to predict rejection in the primary cohort and evaluated its performance using cross-validation and by deploying it to three cohorts. For binary prediction (rejection yes/no), the mean area under the receiver operating curve (AUROC) was 0.849 in the cross-validated experiment and 0.734, 0.729, and 0.716 in external validation cohorts. For a prediction of the ISHLT grade (0R, 1R, 2/3R), AUROCs were 0.835, 0.633, and 0.905 in the cross-validated experiment and 0.764, 0.597, and 0.913; 0.631, 0.633, and 0.682; and 0.722, 0.601, and 0.805 in the validation cohorts, respectively. The predictions of the artificial intelligence model were interpretable by human experts and highlighted plausible morphological patterns.

Conclusion

We conclude that artificial intelligence can detect patterns of cellular transplant rejection in routine pathology, even when trained on small cohorts.

Validation of donor fraction cell-free DNA with biopsy-proven cardiac allograft rejection in children and adults

OBJECTIVES

Donor-specific cell-free DNA shows promise as a noninvasive marker for allograft rejection, but as yet has not been validated in both adult and pediatric recipients. The study objective was to validate donor fraction cell-free DNA as a noninvasive test to assess for risk of acute cellular rejection and antibody-mediated rejection after heart transplantation in pediatric and adult recipients.

METHODS

Pediatric and adult heart transplant recipients were enrolled from 7 participating sites and followed for 12 months or more with plasma samples collected immediately before all endomyocardial biopsies. Donor fraction cell-free DNA was extracted, and quantitative genotyping was performed. Blinded donor fraction cell-free DNA and clinical data were analyzed and compared with a previously determined threshold of 0.14%. Sensitivity, specificity, negative predictive value, positive predictive value, and receiver operating characteristic curves were calculated.

RESULTS

A total of 987 samples from 144 subjects were collected. After applying predefined clinical and technical exclusions, 745 samples from 130 subjects produced 54 rejection samples associated with the composite outcome of acute cellular rejection grade 2R or greater and pathologic antibody-mediated rejection 2 or greater and 323 healthy samples. For all participants, donor fraction cell-free DNA at a threshold of 0.14% had a sensitivity of 67%, a specificity of 79%, a positive predictive value of 34%, and a negative predictive value of 94% with an area under the curve of 0.78 for detecting rejection. When analyzed independently, these results held true for both pediatric and adult cohorts at the same threshold of 0.14% (negative predictive value 92% and 95%, respectively).

CONCLUSIONS

Donor fraction cell-free DNA at a threshold of 0.14% can be used to assess for risk of rejection after heart transplantation in both pediatric and adult patients with excellent negative predictive value.

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

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.

Relationship between donor fraction cell-free DNA and clinical rejection in heart transplantation

BACKGROUND

Clinical rejection (CR) defined as decision to treat clinically suspected rejection with change in immunotherapy based on clinical presentation with or without diagnostic biopsy findings is an important part of care in heart transplantation. We sought to assess the utility of donor fraction cell-free DNA (DF cfDNA) in CR and the utility of serial DF cfDNA in CR patients in predicting outcomes of clinical interest.

METHODS

Patients with heart transplantation were enrolled in two sequential, multi-center, prospective observational studies. Blood samples were collected for surveillance or clinical events. Clinicians were blinded to the results of DF cfDNA.

RESULTS

A total of 835 samples from 269 subjects (57% pediatric) were included for this analysis, including 28 samples associated with CR were analyzed. Median DF cfDNA was 0.43 (IQR 0.15, 1.36)% for CR and 0.10 (IQR 0.07, 0.16)% for healthy controls (p < .0001). At cutoff value of 0.13%, the area under curve (AUC) was 0.82, sensitivity of 0.86, specificity of 0.67, and negative predictive value of 0.99. There was serial decline in DF cfDNA post-therapy, however, those with cardiovascular events (cardiac arrest, need for mechanical support or death) showed significantly higher levels of DF cfDNA on Day 0 (2.11 vs 0.31%) and Day 14 (0.51 vs 0.22%) compared to those who did not have such an event (p < .0001).

CONCLUSION

DF cfDNA has excellent agreement with clinical rejection and, importantly, serial measurement of DF cfDNA predict clinically significant outcomes post treatment for rejection in these patients.

Rejection surveillance in pediatric heart transplant recipients: Critical reflection on the role of frequent and long-term routine surveillance endomyocardial biopsies and comprehensive review of non-invasive rejection screening tools

BACKGROUND

Despite significant medical advances in the field of pediatric heart transplantation (HT), acute rejection remains an important cause of morbidity and mortality. Endomyocardial biopsy (EMB) remains the gold-standard method for diagnosing rejection but is an invasive, expensive, and stressful process. Given the potential adverse consequences of rejection, routine post-transplant rejection surveillance protocols incorporating EMB are widely employed to detect asymptomatic rejection. Each center employs their own specific routine rejection surveillance protocol, with no consensus on the optimal approach and with high inter-center variability. The utility of high-frequency and long-term routine surveillance biopsies (RSB) in pediatric HT has been called into question.

METHODS

Sources for this comprehensive review were primarily identified through searches in biomedical databases including MEDLINE and Embase.

RESULTS

The available literature suggests that the diagnostic yield of RSB is low beyond the first year post-HT and that a reduction in RSB intensity from high-frequency to low-frequency can be done safely with no impact on early and mid-term survival. Though there are emerging non-invasive methods of detecting asymptomatic rejection, the evidence is not yet strong enough for any test to replace EMB.

CONCLUSION

Overall, pediatric HT centers in North America should likely be doing fewer RSB than are currently performed. Risk factors for rejection should be considered when designing the optimal rejection surveillance strategy. Noninvasive testing including emerging biomarkers may have a complementary role to aid in safely reducing the need for RSB.

Deep learning-enabled assessment of cardiac allograft rejection from endomyocardial biopsies

Endomyocardial biopsy (EMB) screening represents the standard of care for detecting allograft rejections after heart transplant. Manual interpretation of EMBs is affected by substantial interobserver and intraobserver variability, which often leads to inappropriate treatment with immunosuppressive drugs, unnecessary follow-up biopsies and poor transplant outcomes. Here we present a deep learning-based artificial intelligence (AI) system for automated assessment of gigapixel whole-slide images obtained from EMBs, which simultaneously addresses detection, subtyping and grading of allograft rejection. To assess model performance, we curated a large dataset from the United States, as well as independent test cohorts from Turkey and Switzerland, which includes large-scale variability across populations, sample preparations and slide scanning instrumentation. The model detects allograft rejection with an area under the receiver operating characteristic curve (AUC) of 0.962; assesses the cellular and antibody-mediated rejection type with AUCs of 0.958 and 0.874, respectively; detects Quilty B lesions, benign mimics of rejection, with an AUC of 0.939; and differentiates between low-grade and high-grade rejections with an AUC of 0.833. In a human reader study, the AI system showed non-inferior performance to conventional assessment and reduced interobserver variability and assessment time. This robust evaluation of cardiac allograft rejection paves the way for clinical trials to establish the efficacy of AI-assisted EMB assessment and its potential for improving heart transplant outcomes.

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

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.

Detection and Quantification of Myocardial Fibrosis Using Stain-Free Infrared Spectroscopic Imaging

CONTEXT.—

Myocardial fibrosis underpins a number of cardiovascular conditions and is difficult to identify with standard histologic techniques. Challenges include imaging, defining an objective threshold for classifying fibrosis as mild or severe, as well as understanding the molecular basis for these changes.

OBJECTIVE.—

To develop a novel, rapid, label-free approach to accurately measure and quantify the extent of fibrosis in cardiac tissue using infrared spectroscopic imaging.

DESIGN.—

We performed infrared spectroscopic imaging and combined that with advanced machine learning-based algorithms to assess fibrosis in 15 samples from patients belonging to the following 3 classes: (1) nonpathologic (control) donor hearts; (2) patients receiving transplant; and (3) tissue from patients undergoing implantation of ventricular assist device.

RESULTS.—

Our results show excellent sensitivity and accuracy for detecting myocardial fibrosis as demonstrated by high area under the curve of 0.998 in the receiver-operating characteristic curve measured from infrared imaging. Fibrosis of various morphologic subtypes are then demonstrated with virtually generated picrosirius red images, which show good visual and quantitative agreement (correlation coefficient = 0.92, ρ = 7.76 × 10-15) with stained images of the same sections. Underlying molecular composition of the different subtypes were investigated with infrared spectra showing reproducible differences presumably arising from differences in collagen subtypes and/or crosslinking.

CONCLUSIONS.—

Infrared imaging can be a powerful tool in studying myocardial fibrosis and gleaning insights into the underlying chemical changes that accompany it. Emerging methods suggest that the proposed approach is compatible with conventional optical microscopy and its consistency makes it translatable to the clinical setting for real-time diagnoses as well as for objective and quantitative research.

The Molecular Microscope (MMDXR ) interpretation of thoracic and abdominal allograft biopsies: Putting things in perspective for the clinician

The Molecular Microscope System (MMDX^R ) has significant potential to enhance biopsy interpretation. However, discussions of MMDx do not acknowledge the basic accuracy of histology readings, and the ability of pathology as a stand-alone tool to guide patient management. MMDx overstates its ability to automatically correct for problems in biopsy readings. Assertions of accuracy approaching 99% are not supported by "real world" data. The high rate of discrepancies between MMDx® and standard biopsy readings can be attributed to the summation of many factors other than histology interpretation, including molecular noise, assay thresholding, limited sensitivity of microarray technology for low expression genes, errors in classifier development, narrow data interpretation, and lack of spatial context. It is not widely recognized that molecular signatures are not disease-specific and are affected by the stage of disease as well as the extent of tissue injury. The effect of sampling error on MMDx performance is significantly under-estimated, particularly in heart and lung biopsies. Therefore, MMDx reports should always be interpreted in the context of conventional biopsy readings. The clinical picture and conventional biopsy reading should be allowed to over-ride the molecular interpretation when there is concern that confounding factors are at play.

[Pathology of heart transplantation: Where are we now?]

Pathology is still the gold standard for the diagnosis of rejection in heart transplantation. During the last decade, molecular pathology has emerged as a powerful tool for the understanding of the processes implicated in allograft rejection. Transcriptomic analysis of the allograft may also help the pathologist for diagnosis and accurate classification of rejection. This review will describe the recent advances and perspectives of molecular pathology in the field of heart transplantation.

Cell-free DNA donor fraction analysis in pediatric and adult heart transplant patients by multiplexed allele-specific quantitative PCR: Validation of a rapid and highly sensitive clinical test for stratification of rejection probability

Lifelong noninvasive rejection monitoring in heart transplant patients is a critical clinical need historically poorly met in adults and unavailable for children and infants. Cell-free DNA (cfDNA) donor-specific fraction (DF), a direct marker of selective donor organ injury, is a promising analytical target. Methodological differences in sample processing and DF determination profoundly affect quality and sensitivity of cfDNA analyses, requiring specialized optimization for low cfDNA levels typical of transplant patients. Using next-generation sequencing, we previously correlated elevated DF with acute cellular and antibody-mediated rejection (ACR and AMR) in pediatric and adult heart transplant patients. However, next-generation sequencing is limited by cost, TAT, and sensitivity, leading us to clinically validate a rapid, highly sensitive, quantitative genotyping test, myTAI_HEART^®, addressing these limitations. To assure pre-analytical quality and consider interrelated cfDNA measures, plasma preparation was optimized and total cfDNA (TCF) concentration, DNA fragmentation, and DF quantification were validated in parallel for integration into myTAI_HEART reporting. Analytical validations employed individual and reconstructed mixtures of human blood-derived genomic DNA (gDNA), cfDNA, and gDNA sheared to apoptotic length. Precision, linearity, and limits of blank/detection/quantification were established for TCF concentration, DNA fragmentation ratio, and DF determinations. For DF, multiplexed high-fidelity amplification followed by quantitative genotyping of 94 SNP targets was applied to 1168 samples to evaluate donor options in staged simulations, demonstrating DF call equivalency with/without donor genotype. Clinical validation studies using 158 matched endomyocardial biopsy-plasma pairs from 76 pediatric and adult heart transplant recipients selected a DF cutoff (0.32%) producing 100% NPV for ≥2R ACR. This supports the assay’s conservative intended use of stratifying low versus increased probability of ≥2R ACR. myTAI_HEART is clinically validated for heart transplant recipients ≥2 months old and ≥8 days post-transplant, expanding opportunity for noninvasive transplant rejection assessment to infants and children and to all recipients >1 week post-transplant.

Multi-modal imaging of the pediatric heart transplant recipient

The assessment of pediatric patients after orthotropic heart transplantation (OHT) relies heavily on non-invasive imaging. Because of the potential risks associated with cardiac catheterization, expanding the role of non-invasive imaging is appealing. Echocardiography is fast, widely available, and can provide an accurate assessment of chamber sizes and function. Advanced echocardiographic methods, such as myocardial deformation, have potential to assess for acute rejection or cardiac allograft vasculopathy (CAV). While not currently part of routine care, cardiac magnetic resonance imaging (CMR) and computed tomography may potentially aid in the detection of graft complications following OHT. In particular, CMR tissue characterization holds promise for diagnosing rejection, while quantitative perfusion and myocardial late gadolinium enhancement may have a role in the detection of CAV. This review will evaluate standard and novel methods for non-invasive assessment of pediatric patients after OHT.

The Landscape of Digital Pathology in Transplantation: From the Beginning to the Virtual E-Slide

Background

Digital pathology has progressed over the last two decades, with many clinical and nonclinical applications. Transplantation pathology is a highly specialized field in which the majority of practicing pathologists do not have sufficient expertise to handle critical needs. In this context, digital pathology has proven to be useful as it allows for timely access to expert second-opinion teleconsultation. The aim of this study was to review the experience of the application of digital pathology to the field of transplantation.

Methods

Papers on this topic were retrieved using PubMed as a search engine. Inclusion criteria were the presence of transplantation setting and the use of any type of digital image with or without the use of image analysis tools; the search was restricted to English language papers published in the 25 years until December 31, 2018.

Results

Literature regarding digital transplant pathology is mostly about the digital interpretation of posttransplant biopsies (75 vs. 19), with 15/75 (20%) articles focusing on agreement/reproducibility. Several papers concentrated on the correlation between biopsy features assessed by digital image analysis (DIA) and clinical outcome (45/75, 60%). Whole-slide imaging (WSI) only appeared in recent publications, starting from 2011 (13/75, 17.3%). Papers dealing with preimplantation biopsy are less numerous, the majority (13/19, 68.4%) of which focus on diagnostic agreement between digital microscopy and light microscopy (LM), with WSI technology being used in only a small quota of papers (4/19, 21.1%).

Conclusions

Overall, published studies show good concordance between digital microscopy and LM modalities for diagnosis. DIA has the potential to increase diagnostic reproducibility and facilitate the identification and quantification of histological parameters. Thus, with advancing technology such as faster scanning times, better image resolution, and novel image algorithms, it is likely that WSI will eventually replace LM.

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

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.

The authors review “machine learning“ and “automated image feature detection“ to improve the diagnostic accuracy of endomyocardial biopsies to diagnose cardiac allograft rejection.

Complications of Cardiac Transplantation

PURPOSE OF REVIEW

Despite the improvement in medical therapy for heart failure and the advancements in mechanical circulatory support, heart transplantation (HT) still remains the best therapeutic option to improve survival and quality of life in patients with advanced heart failure. Nevertheless, HT recipients are exposed to the risk of several potential complications that may impair their outcomes. In this article, we aim to provide a practical and scholarly framework for clinicians approaching heart transplant medicine, as well as a concise update for the experienced readers on the most relevant post-HT complications.

RECENT FINDINGS

While recognizing that most of the treatments herein discussed are based more on experience than on solid scientific evidence, significant step forward has been made in particular in the recognition and management of primary graft dysfunction, antibody-mediated rejection, and renal dysfunction. Complications after HT may vary according to the time from surgery and can be related to graft function and pathology or to diseases and dysfunctions occurring in other organs or systems, mainly as side effects of immunosuppressive drugs and progression of pre-existing conditions. Future research needs to focus on improving precision diagnostics of causes of graft dysfunction and on reaching an optimal and customized balance between efficacy and toxicities of immunosuppressive strategies.

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

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.

Antibody-mediated rejection in heart transplantation: new developments and old uncertainties

PURPOSE OF REVIEW

Antibody-mediated rejection (AMR) currently represents one of the main problems for clinical management of heart transplant because of its diagnostic complexity and poor evidences supporting treatments.

RECENT FINDINGS

Disorder-based diagnosis is a cornerstone in defining AMR. The limitations of the current classification have been partially overcome by novel studies improving the description of the immune-pathological graft abnormalities, and by new molecular approaches allowing a better understanding of the mechanisms behind AMR and of its relationship with cellular rejection and chronic vasculopathy. In-depth characterization of donor-specific antibodies showed to provide additional prognostic information and guide for treatment. Clinical relevance of AMR is bound to appropriate detection of graft dysfunction. In addition to traditional longitudinal evaluation by echocardiogram, cardiac magnetic resonance and detection of cell-free DNA may represent novel sensitive markers for graft injury that could prompt treatment before dysfunction becomes clinically manifest.

SUMMARY

Despite improvements in the diagnostic process, therapeutic strategies made little progress in addition to the consolidation of practices supported by limited evidences. Novel complement inhibitors appear promising in changing this scenario. Nevertheless, collaborative multicenter studies are needed to develop standardized approaches tailored to the highly variable clinical and laboratory features of AMR.

Urinary Proteomics in Predicting Heart Transplantation Outcomes (uPROPHET)-Rationale and database description

OBJECTIVES

Urinary Proteomics in Predicting Heart Transplantation Outcomes (uPROPHET; NCT03152422) aims: (i) to construct new multidimensional urinary proteomic (UP) classifiers that after heart transplantation (HTx) help in detecting graft vasculopathy, monitoring immune system activity and graft performance, and in adjusting immunosuppression; (ii) to sequence UP peptide fragments and to identify key proteins mediating HTx-related complications; (iii) to validate UP classifiers by demonstrating analogy between UP profiles and tissue proteomic signatures (TP) in diseased explanted hearts, to be compared with normal donor hearts; (iv) and to identify new drug targets. This article describes the uPROPHET database construction, follow-up strategies and baseline characteristics of the HTx patients.

METHODS

HTx patients enrolled at the University Hospital Gasthuisberg (Leuven) collected mid-morning urine samples. Cardiac biopsies were obtained at HTx. UP and TP methods and the statistical work flow in pursuit of the research objectives are described in detail in the Data supplement.

RESULTS

Of 352 participants in the UP study (24.4% women), 38.9%, 40.3%, 5.7% and 15.1% had ischemic, dilated, hypertrophic or other cardiomyopathy. The median interval between HTx and first UP assessment (baseline) was 7.8 years. At baseline, mean values were 56.5 years for age, 25.2 kg/m2 for body mass index, 142.3/84.8 mm Hg and 124.2/79.8 mm Hg for office and 24-h ambulatory systolic/diastolic pressure, and 58.6 mL/min/1.73 m2 for the estimated glomerular filtration rate. Of all patients, 37.2% and 6.5% had a history of mild (grade = 1B) or severe (grade ≥ 2) cellular rejection. Anti-body mediated rejection had occurred in 6.2% patients. The number of follow-up urine samples available for future analyses totals over 950. The TP study currently includes biopsies from 7 healthy donors and 15, 14, and 3 patients with ischemic, dilated, and hypertrophic cardiomyopathy.

CONCLUSIONS

uPROPHET constitutes a solid resources for UP and TP research in the field of HTx and has the ambition to lay the foundation for the clinical application of UP in risk stratification in HTx patients.

Serum exosomal protein profiling for the non-invasive detection of cardiac allograft rejection

BACKGROUND

Exosomes are cell-derived circulating vesicles that play an important role in cell-cell communication. Exosomes are actively assembled and carry messenger RNAs, microRNAs and proteins. The "gold standard" for cardiac allograft surveillance is endomyocardial biopsy (EMB), an invasive technique with a distinct complication profile. The development of novel, non-invasive methods for the early diagnosis of allograft rejection is warranted. We hypothesized that the exosomal proteome is altered in acute rejection, allowing for a distinction between non-rejection and rejection episodes.

METHODS

Serum samples were collected from heart transplant (HTx) recipients with no rejection, acute cellular rejection (ACR) and antibody-mediated rejection (AMR). Liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis of serum exosome was performed using a mass spectrometer (Orbitrap Fusion Tribrid).

RESULTS

Principal component analysis (PCA) revealed a clustering of 3 groups: (1) control and heart failure (HF); (2) HTx without rejection; and (3) ACR and AMR. A total of 45 proteins were identified that could distinguish between groups (q < 0.05). Comparison of serum exosomal proteins from control, HF and non-rejection HTx revealed 17 differentially expressed proteins in at least 1 group (q < 0.05). Finally, comparisons of non-rejection HTx, ACR and AMR serum exosomes revealed 15 differentially expressed proteins in at least 1 group (q < 0.05). Of these 15 proteins, 8 proteins are known to play a role in the immune response. Of note, the majority of proteins identified were associated with complement activation, adaptive immunity such as immunoglobulin components and coagulation.

CONCLUSIONS

Characterizing of circulating exosomal proteome in different cardiac disease states reveals unique protein expression patterns indicative of the respective pathologies. Our data suggest that HTx and allograft rejection alter the circulating exosomal protein content. Exosomal protein analysis could be a novel approach to detect and monitor acute transplant rejection and lead to the development of predictive and prognostic biomarkers.

The Empirical Foundations of Telepathology: Evidence of Feasibility and Intermediate Effects

INTRODUCTION

Telepathology evolved from video microscopy (i.e., "television microscopy") research in the early 1950s to video microscopy used in basic research in the biological sciences to a basic diagnostic tool in telemedicine clinical applications. Its genesis can be traced to pioneering feasibility studies regarding the importance of color and other image-based parameters for rendering diagnoses and a series of studies assessing concordance of virtual slide and light microscopy diagnoses. This article documents the empirical foundations of telepathology.

METHODS

A selective review of the research literature during the past decade (2005-2016) was conducted using robust research design and adequate sample size as criteria for inclusion.

CONCLUSIONS

The evidence regarding feasibility/acceptance of telepathology and related information technology applications has been well documented for several decades. The majority of evidentiary studies focused on intermediate outcomes, as indicated by comparability between telepathology and conventional light microscopy. A consistent trend of concordance between the two modalities was observed in terms of diagnostic accuracy and reliability. Additional benefits include use of telepathology and whole slide imaging for teaching, research, and outreach to resource-limited countries. Challenges still exist, however, in terms of use of telepathology as an effective diagnostic modality in clinical practice.

Elevated ST2 Distinguishes Incidences of Pediatric Heart and Small Bowel Transplant Rejection

Elevated serum soluble (s) suppressor of tumorigenicity-2 is observed during cardiovascular and inflammatory bowel diseases. To ascertain whether modulated ST2 levels signify heart (HTx) or small bowel transplant (SBTx) rejection, we quantified sST2 in serially obtained pediatric HTx (n = 41) and SBTx recipient (n = 18) sera. At times of biopsy-diagnosed HTx rejection (cellular and/or antibody-mediated), serum sST2 was elevated compared to rejection-free time points (1714 ± 329 vs. 546.5 ± 141.6 pg/mL; p = 0.0002). SBTx recipients also displayed increased serum sST2 during incidences of rejection (7536 ± 1561 vs. 2662 ± 543.8 pg/mL; p = 0.0347). Receiver operator characteristic (ROC) analysis showed that serum sST2 > 600 pg/mL could discriminate time points of HTx rejection and nonrejection (area under the curve [AUC] = 0.724 ± 0.053; p = 0.0003). ROC analysis of SBTx measures revealed a similar discriminative capacity (AUC = 0.6921 ± 0.0820; p = 0.0349). Quantitative evaluation of both HTx and SBTx biopsies revealed that rejection significantly increased allograft ST2 expression. Pathway and Network Analysis of biopsy data pinpointed ST2 in the dominant pathway modulated by rejection and predicted tumor necrosis factor-α and IL-1β as upstream activators. In total, our data indicate that alloimmune-associated pro-inflammatory cytokines increase ST2 during rejection. They also demonstrate that routine serum sST2 quantification, potentially combined with other biomarkers, should be investigated further to aid in the noninvasive diagnosis of rejection.

Use of [18F]FDG Positron Emission Tomography to Monitor the Development of Cardiac Allograft Rejection

BACKGROUND

Positron emission tomography (PET) has the potential to be a specific, sensitive and quantitative diagnostic test for transplant rejection. To test this hypothesis, we evaluated F-labeled fluorodeoxyglucose ([F]FDG) and N-labeled ammonia ([N]NH3) small animal PET imaging in a well-established murine cardiac rejection model.

METHODS

Heterotopic transplants were performed using minor major histocompatibility complex-mismatched B6.C-H2 donor hearts in C57BL/6(H-2) recipients. C57BL/6 donor hearts into C57BL/6 recipients served as isograft controls. [F]FDG PET imaging was performed weekly between posttransplant days 7 and 42, and the percent injected dose was computed for each graft. [N]NH3 imaging was performed to evaluate myocardial perfusion.

RESULTS

There was a significant increase in [F]FDG uptake in allografts from day 14 to day 21 (1.6% to 5.2%; P < 0.001) and uptake in allografts was significantly increased on posttransplant days 21 (5.2% vs 0.9%; P = 0.005) and 28 (4.8% vs 0.9%; P = 0.006) compared to isograft controls. Furthermore, [F]FDG uptake correlated with an increase in rejection grade within allografts between days 14 and 28 after transplantation. Finally, the uptake of [N]NH3 was significantly lower relative to the native heart in allografts with chronic vasculopathy compared to isograft controls on day 28 (P = 0.01).

CONCLUSIONS

PET imaging with [F]FDG can be used after transplantation to monitor the evolution of rejection. Decreased uptake of [N]NH3 in rejecting allografts may be reflective of decreased myocardial blood flow. These data suggest that combined [F]FDG and [N]NH3 PET imaging could be used as a noninvasive, quantitative technique for serial monitoring of allograft rejection and has potential application in human transplant recipients.

Biomarkers of acute rejection following cardiac transplantation

Cardiac transplantation can be a life-saving treatment for selected patients with heart failure. However, despite advances in immunosuppressive therapy, acute allograft rejection remains a significant cause of morbidity and mortality. The current 'gold standard' for rejection surveillance is endomyocardial biopsy, which aims to identify episodes of rejection prior to development of clinical manifestations. This is an invasive technique with a risk of false-positive and false-negative results. Consequently, a wide variety of noninvasive alternatives have been investigated for their potential role as biomarkers of rejection. This article reviews the evidence behind proposed alternatives such as imaging techniques, electrophysiological parameters and peripheral blood markers, and highlights the potential future role for biomarkers in cardiac transplantation as an adjunct to biopsy.

Immunohistochemical quantification of inflammatory cells in endomyocardial biopsy fragments after heart transplantation: a new potential method to improve the diagnosis of rejection after heart transplantation

Inconsistencies in cardiac rejection grading systems corroborate the concept that the evaluation of inflammatory intensity and myocyte damage seems to be subjective. We studied in 36 patients the potential role of the immunohistochemical (IHC) counting of inflammatory cells in endomyocardial biopsy (EMB) as an objective tool, testing the hypothesis of correlation between the International Society for Heart and Lung Transplantation 2004 rejection and IHC counting of inflammatory cells. We observed a progressive increment in CD68+ cells/mm(2) (P = .000) and CD3+ cells/mm(2) (P = .000) with higher rejection grade. A strong correlation between the grade of cellular rejection and both CD68+ cells/mm(2) and CD3+ cells/mm(2) was obtained (P = .000). One patient with CD3+ and CD68+ cells/mm(2) above the upper limit of the 95% confidence interval for cells/mm(2) found in rejection grade 1R evolved to rejection grade 2R without treatment. In patients with 2R that did not respond to treatment the values of CD68+ or CD3+ cells were higher than the overall median values for rejection grade 2R. For diagnosis of rejection needing treatment, the CD68+ and CD3+ cells/mm(2) areas under the receiver operating characteristic curves were 0.956 and 0.934, respectively. IHC counting of mononuclear inflammatory infiltrate in EMB seems to have additive potential role in evaluation of EMB for the diagnosis and prognosis of rejection episodes.

The 2013 International Society for Heart and Lung Transplantation Working Formulation for the standardization of nomenclature in the pathologic diagnosis of antibody-mediated rejection in heart transplantation

During the last 25 years, antibody-mediated rejection of the cardiac allograft has evolved from a relatively obscure concept to a recognized clinical complication in the management of heart transplant patients. Herein we report the consensus findings from a series of meetings held between 2010-2012 to develop a Working Formulation for the pathologic diagnosis, grading, and reporting of cardiac antibody-mediated rejection. The diagnostic criteria for its morphologic and immunopathologic components are enumerated, illustrated, and described in detail. Numerous challenges and unresolved clinical, immunologic, and pathologic questions remain to which a Working Formulation may facilitate answers.

Utility of C4d immunostaining in the first year after pediatric and young adult heart transplantation

BACKGROUND

C4d assessment of endomyocardial biopsies (EMBs) after heart transplantation (HTx) has been widely adopted to aid in the diagnosis of antibody-mediated rejection (AMR), yet it remains unclear whether or not to assess all patients routinely and with what frequency/duration. In this study we sought to evaluate the utility of routine C4d immunostaining in the first year after pediatric and young adult HTx.

METHODS

We reviewed pre-transplant alloantibody and clinical data, including serial EMB reports, on all 51 patients who received HTx at our center since we instituted routine C4d staining of all first-year EMBs. C4d was considered positive if diffuse capillary staining (≥ 2(+)) was present. Rare/focal capillary staining or absence of staining was considered negative.

RESULTS

Twenty-six of 406 first-year EMBs (6%) were C4d(+) in 6 (12%) patients. Sixty-five percent of all C4d(+) EMBs occurred by 30 days post-transplant. Five of 6 patients had pre-transplant donor-specific antibody (DSA) ≥ 4,000 MFI. The sixth patient had neither pre-transplant anti-HLA antibodies nor a positive donor-specific cytotoxicity crossmatch (DSXM), but there was clinical concern for AMR. Among the entire cohort, 5 of 10 patients with pre-transplant DSA ≥ 4,000 MFI and/or a positive DSXM were C4d(+) compared with only 1 of 41 without (50% vs 2%; p = 0.001).

CONCLUSIONS

In the first year after HTx, C4d(+) occurred early and only in children and young adults with pre-transplant DSA or with clinical suspicion of AMR. Although our data suggest that assessment limited to the first 90 days post-transplant in patients with pre-transplant DSA ≥ 4,000 MFI may be appropriate in the absence of clinical concern for AMR, further research is needed to determine the optimum strategy for post-transplant surveillance.

The history of pathology informatics: A global perspective

Pathology informatics has evolved to varying levels around the world. The history of pathology informatics in different countries is a tale with many dimensions. At first glance, it is the familiar story of individuals solving problems that arise in their clinical practice to enhance efficiency, better manage (e.g., digitize) laboratory information, as well as exploit emerging information technologies. Under the surface, however, lie powerful resource, regulatory, and societal forces that helped shape our discipline into what it is today. In this monograph, for the first time in the history of our discipline, we collectively perform a global review of the field of pathology informatics. In doing so, we illustrate how general far-reaching trends such as the advent of computers, the Internet and digital imaging have affected pathology informatics in the world at large. Major drivers in the field included the need for pathologists to comply with national standards for health information technology and telepathology applications to meet the scarcity of pathology services and trained people in certain countries. Following trials by a multitude of investigators, not all of them successful, it is apparent that innovation alone did not assure the success of many informatics tools and solutions. Common, ongoing barriers to the widespread adoption of informatics devices include poor information technology infrastructure in undeveloped areas, the cost of technology, and regulatory issues. This review offers a deeper understanding of how pathology informatics historically developed and provides insights into what the promising future might hold.

Molecular transplantation pathology: the interface between molecules and histopathology

PURPOSE OF REVIEW

In the last decade, high-throughput molecular screening methods have revolutionized the transplantation research. This article reviews the new knowledge that has emerged from transplant patient sample-derived 'omics data by examining the interface between molecular signals and allograft pathology.

RECENT FINDINGS

State-of-the-art molecular studies have shed light on the biology of organ transplant diseases and provided several potential molecular tests with diagnostic, prognostic, and theranostic applications for the implementation of personalized medicine in transplantation. By comprehensive molecular profiling of patient samples, we have learned numerous new insights into the effector mechanisms and parenchymal response during allograft diseases. It has become evident that molecular profiles are coordinated and move in patterns similar to histopathology lesions, and therefore lack qualitative specificity. However, molecular tests can empower precision diagnosis and prognostication through their objective and quantitative manner when they are integrated in a holistic approach with histopathology and clinical factors of patients.

SUMMARY

Despite clever science and large amounts of public money invested in transplant 'omics studies, multiparametric molecular testing has not yet been translated to patient care. There are serious challenges in the implementation of transplant molecular diagnostics that have increased frustration in transplant community. We appeal for a full collaboration between pathologists and researchers to accelerate transition from research to clinical practice in transplantation.

Genetic and genomic approaches to the detection of heart transplant rejection

Since Christiaan Barnard performed the first heart transplant in 1967, over 100,000 heart transplants have been performed worldwide. As was true then, rejection remains the major threat to the function and survival of the allograft. The development of the endomyocardial biopsy as a means to monitor for rejection has allowed heart transplantation to thrive as a therapy for patients with end-stage heart disease. The need for a noninvasive method of rejection surveillance led to the development of the first genetic test for allograft rejection, the AlloMap^®. In this article, after presenting the pathological and clinical features of cardiac allograft rejection, the authors discuss the development and application of gene-expression testing for the detection of cardiac allograft rejection. We then explore emerging 'omic' approaches that will be the rejection detection methods of the future.