ReviewTransplantation2018
Advanced Morphologic Analysis for Diagnosing Allograft Rejection: The Case of Cardiac Transplant Rejection.
Review in Transplantation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- CLARISA: Connexin-43 Lateralization Automated ROI-Based Image Signal Analyzer.International journal of molecular sciences · 2026Article
- Treatment of antibody-mediated rejection in heart transplantation: from empirical combination to multi-target precision intervention.Frontiers in immunology · 2026Review
- Exploring Chronic Rejection in Organ Transplantation Through Computational Modeling.Results and problems in cell differentiation · 2026Review
- Computational pathology assessments of cardiac stromal remodeling: Clinical correlates and prognostic implications in heart transplantation.JHLT open · 2025Article
- Computational Pathology Assessments of Cardiac Stromal Remodeling: Clinical Correlates and Prognostic Implications in Heart Transplantation.Research square · 2024Article
- Failing to Make the Grade: Conventional Cardiac Allograft Rejection Grading Criteria Are Inadequate for Predicting Rejection Severity.Circulation. Heart failure · 2024Article
- The evolving use of biomarkers in heart transplantation: Consensus of an expert panel.American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2023Review
- Non-invasive detection of cardiac allograft rejection among heart transplant recipients using an electrocardiogram based deep learning model.European heart journal. Digital health · 2023Article
- The promise of machine learning applications in solid organ transplantation.NPJ digital medicine · 2022Review
- Computational Analysis of Routine Biopsies Improves Diagnosis and Prediction of Cardiac Allograft Vasculopathy.Circulation · 2022Article
- Deep learning-enabled assessment of cardiac allograft rejection from endomyocardial biopsies.Nature medicine · 2022Article
- Markers of Immune Function in Heart Transplantation: Implications for Immunosuppression and Screening for Rejection.Current heart failure reports · 2021Review
- Deep Neural Network-Aided Histopathological Analysis of Myocardial Injury.Frontiers in cardiovascular medicine · 2021Article
- In Situ Immune Profiling of Heart Transplant Biopsies Improves Diagnostic Accuracy and Rejection Risk Stratification.JACC. Basic to translational science · 2020Article
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.