In Clinical transplantation ; h5-index 26.0
BACKGROUND : Computer-assisted scoring is gaining prominence in the evaluation of renal histology; however, much of the focus has been on identifying larger objects such as glomeruli. Total inflammation impacts graft outcome, and its quantification requires tools to identify objects at the cellular level or smaller. The goal of the current study was to use CD45 stained slides coupled with image analysis tools to quantify the amount of non-glomerular inflammation within the cortex.
METHODS : Sixty renal transplant whole slide images were used for digital image analysis. Multiple thresholding methods using pixel intensity and object size were used to identify inflammation in the cortex. Additionally, convolutional neural networks were used to separate glomeruli from other objects in the cortex. This combined measure of inflammation was then correlated with rescored Banff total inflammation classification and outcomes.
RESULTS : Identification of glomeruli on biopsies had high fidelity (mean pixelwise dice coefficient of .858). Continuous total inflammation scores correlated well with Banff rescoring (maximum Pearson correlation .824). A separate set of thresholds resulted in a significant correlation with alloimmune graft loss.
CONCLUSIONS : Automated scoring of inflammation showed a high correlation with Banff scoring. Digital image analysis provides a powerful tool for analysis of renal pathology, not only because it is reproducible and can be automated, but also because it provides much more granular data for studies. This article is protected by copyright. All rights reserved.
Smith Byron, Grande Joseph, Ryan Maggie, Smith Maxwell, Denic Aleksandar, Hermsen Meyke, Park Walter, Kremers Walter, Stegall Mark
2022-Oct-19
artificial intelligence, convolutional neural networks, image analysis, kidney pathology, kidney transplant
