期刊
TRANSPLANTATION
卷 102, 期 8, 页码 1230-1239出版社
LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/TP.0000000000002189
关键词
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资金
- National Center for Advancing Translational Sciences of the National Institutes of Health [TL1TR001880]
- National Cancer Institute of the National Institutes of Health [R21CA179327-01, R21CA195152-0, U24CA199374-01]
- National Institute of Diabetes and Digestive and Kidney Diseases [R01DK098503-02]
- National Heart Lung and Blood Institute [R01-HL105993]
- DOD Prostate Cancer Synergistic Idea Development Award [PC120857]
- DOD Lung Cancer Idea Development New Investigator Award [LC130463]
- DOD Prostate Cancer Idea Development Award
- Case Comprehensive Cancer Center Pilot Grant
- VelaSano Grant from the Cleveland Clinic the Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering at Case Western Reserve University
- I-Corps@Ohio Program
- Philips Healthcare
- NIH
- PathCore Inc.
- Inspirata
- Thoratec Corporation
- Merck
- Glaxo-Smith-Kline
- NATIONAL CANCER INSTITUTE [R21CA179327, R01CA208236, U24CA199374, R21CA195152, R01CA216579, R01CA202752] Funding Source: NIH RePORTER
- NATIONAL CENTER FOR ADVANCING TRANSLATIONAL SCIENCES [TL1TR001880] Funding Source: NIH RePORTER
- NATIONAL HEART, LUNG, AND BLOOD INSTITUTE [R01HL105993, R01HL089847] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASES [R01DK098503] Funding Source: NIH RePORTER
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 problemsoften referred to as machine learningrepresent 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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