期刊
EUROPEAN HEART JOURNAL
卷 42, 期 24, 页码 2356-2369出版社
OXFORD UNIV PRESS
DOI: 10.1093/eurheartj/ehab241
关键词
Image analysis; Machine learning; Digital pathology; Heart transplant; Allograft rejection
资金
- National Cancer Institute of the National Institutes of Health [1U24CA199374-01, R01CA202752-01A1, R01CA208236-01A1, R01 CA216579-01A1, R01 CA220581-01A1, 1U01 CA239055-01, 1U01CA248226-01, P30 CA16058]
- National Institute for Biomedical Imaging and Bioengineering [1R43EB028736-01]
- National Center for Advancing Translational Sciences of the National Institutes of Health [TL1TR001880, KL2TR001879]
- National Heart, Lung and Blood Institute [R01HL151277-01A1]
- Gund Family Fund at the University of Pennsylvania
- National Center for Research Resources [1 C06 RR12463-01]
- United States Department of Veterans Affairs Biomedical Laboratory Research and Development Service [IBX004121A]
- The Department of Defense Breast Cancer Research Program Breakthrough Level 1 Award [W81XWH-19-1-0668]
- Department of Defense Prostate Cancer Idea Development Award [W81XWH-15-10558]
- Department of Defense Lung Cancer Investigator-Initiated Translational Research Award [W81XWH-18-1-0440]
- Department of Defense Peer Reviewed Cancer Research Program [W81XWH-16-10329]
- Ohio Third Frontier Technology Validation Fund
- Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering
- Clinical and Translational Science Award Program (CTSA) at Case Western Reserve University
- Ohio State University Comprehensive Cancer Center Comparative Pathology & Digital Imaging Shared Resource
The study demonstrates that a computational histological analysis tool, CACHE-Grader, can provide expert-quality rejection grading that is on par with human pathologists, showing nearly identical performance in both internal and external validation sets. The tool has the ability to accurately reproduce the clinical standard for cellular rejection diagnosis and has superior sensitivity for high-grade 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 (delta = 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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