We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
An automated computational image analysis pipeline for histological grading of cardiac allograft rejection.
- Authors
Peyster, Eliot G; Arabyarmohammadi, Sara; Janowczyk, Andrew; Azarianpour-Esfahani, Sepideh; Sekulic, Miroslav; Cassol, Clarissa; Blower, Luke; Parwani, Anil; Lal, Priti; Feldman, Michael D; Margulies, Kenneth B; Madabhushi, Anant
- Abstract
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.
- Subjects
IMAGE analysis; MACHINE learning; HEART transplantation; MEDICAL imaging systems; GRAFT rejection
- Publication
European Heart Journal, 2021, Vol 42, Issue 24, p2356
- ISSN
0195-668X
- Publication type
Article
- DOI
10.1093/eurheartj/ehab241