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.
Abstract

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