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Mouse Model of Alloimmune-induced Vascular Rejection and Transplant Arteriosclerosis
Published on: May 17, 2015
An automated computational image analysis pipeline for histological grading of cardiac allograft rejection
Eliot G Peyster1, Sara Arabyarmohammadi2, Andrew Janowczyk3
1Cardiovascular Institute, University of Pennsylvania, 3400 Civic Center Blvd, Smilow TRC 11th floor, Philadelphia, PA 19104, USA.
Insights
Computational histological analysis using the CACHE-Grader pipeline shows expert-level accuracy in grading cellular rejection in heart allografts. This AI tool matches pathologist performance, improving diagnostic consistency in transplant medicine.
Area of Science:
- Cardiology
- Pathology
- Artificial Intelligence in Medicine
Background:
- Allograft rejection poses a significant challenge in heart transplantation.
- Current diagnostic standards rely on endomyocardial biopsy with histological grading, but suffer from poor inter-pathologist agreement, leading to clinical uncertainty.
Purpose of the Study:
- To develop and validate a computational histology pipeline, the CACHE-Grader, for grading cellular rejection in heart allografts.
- To demonstrate that the CACHE-Grader's performance is comparable to that of expert human pathologists.
Main Methods:
- Trained the CACHE-Grader pipeline on 2472 endomyocardial biopsy slides from three major US transplant centers.
- Utilized an interpretable, biologically inspired approach with 154 quantitative histological features.
- Compared CACHE-Grader interpretations against independent pathologists and the 'grade of record' to test for non-inferiority.
Main Results:
- Human pathologists achieved 60.7% agreement with the grade of record and 61.5% pair-wise agreement.
- The CACHE-Grader achieved 65.9% agreement with the grade of record and 62.6% agreement with human graders, meeting non-inferiority thresholds.
- The CACHE-Grader demonstrated superior sensitivity for high-grade rejection (74.4% vs. 39.5%) and resilience to inter-center variations.
Conclusions:
- The CACHE-grader pipeline provides expert-quality cellular rejection grading, performing within the range of inter-grader variability among human pathologists.
- This computational approach offers a consistent and potentially more sensitive method for diagnosing rejection in heart transplant recipients.
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.
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