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Failing to Make the Grade: Conventional Cardiac Allograft Rejection Grading Criteria Are Inadequate for Predicting Rejection Severity.
Circ Heart Fail. 2024 Feb;17(2):e010950. doi: 10.1161/CIRCHEARTFAILURE.123.010950. Epub 2024 Feb 13.
2
Advanced Morphologic Analysis for Diagnosing Allograft Rejection: The Case of Cardiac Transplant Rejection.
Transplantation. 2018 Aug;102(8):1230-1239. doi: 10.1097/TP.0000000000002189.
3
An automated computational image analysis pipeline for histological grading of cardiac allograft rejection.
Eur Heart J. 2021 Jun 21;42(24):2356-2369. doi: 10.1093/eurheartj/ehab241.
4
Elevated serum concentrations of cardiac troponin T in acute allograft rejection after human heart transplantation.
J Am Coll Cardiol. 1998 Aug;32(2):405-12. doi: 10.1016/s0735-1097(98)00257-5.
5
Computational Analysis of Routine Biopsies Improves Diagnosis and Prediction of Cardiac Allograft Vasculopathy.
Circulation. 2022 May 24;145(21):1563-1577. doi: 10.1161/CIRCULATIONAHA.121.058459. Epub 2022 Apr 11.
6
The challenge of endomyocardial biopsy interpretation in assessing cardiac allograft rejection.
Curr Opin Cardiol. 1997 Mar;12(2):146-52. doi: 10.1097/00001573-199703000-00009.
9
Native T Mapping in the Diagnosis of Cardiac Allograft Rejection: A Prospective Histologically Validated Study.
JACC Cardiovasc Imaging. 2019 Aug;12(8 Pt 2):1618-1628. doi: 10.1016/j.jcmg.2018.10.027. Epub 2019 Jan 16.
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Deep learning-enabled assessment of cardiac allograft rejection from endomyocardial biopsies.
Nat Med. 2022 Mar;28(3):575-582. doi: 10.1038/s41591-022-01709-2. Epub 2022 Mar 21.

本文引用的文献

1
Computational Analysis of Routine Biopsies Improves Diagnosis and Prediction of Cardiac Allograft Vasculopathy.
Circulation. 2022 May 24;145(21):1563-1577. doi: 10.1161/CIRCULATIONAHA.121.058459. Epub 2022 Apr 11.
2
Deep learning-enabled assessment of cardiac allograft rejection from endomyocardial biopsies.
Nat Med. 2022 Mar;28(3):575-582. doi: 10.1038/s41591-022-01709-2. Epub 2022 Mar 21.
3
A survey on graph-based deep learning for computational histopathology.
Comput Med Imaging Graph. 2022 Jan;95:102027. doi: 10.1016/j.compmedimag.2021.102027. Epub 2021 Dec 21.
4
An automated computational image analysis pipeline for histological grading of cardiac allograft rejection.
Eur Heart J. 2021 Jun 21;42(24):2356-2369. doi: 10.1093/eurheartj/ehab241.
6
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. doi: 10.1016/j.jacbts.2020.01.015. eCollection 2020 Apr.
7
HistoQC: An Open-Source Quality Control Tool for Digital Pathology Slides.
JCO Clin Cancer Inform. 2019 Apr;3:1-7. doi: 10.1200/CCI.18.00157.
8
Advanced Morphologic Analysis for Diagnosing Allograft Rejection: The Case of Cardiac Transplant Rejection.
Transplantation. 2018 Aug;102(8):1230-1239. doi: 10.1097/TP.0000000000002189.
9
The Search for a Gold Standard to Detect Rejection in Heart Transplant Patients: Are We There Yet?
Circulation. 2017 Mar 7;135(10):936-938. doi: 10.1161/CIRCULATIONAHA.117.026752.

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