Automated Computational Detection of Interstitial Fibrosis, Tubular Atrophy, and
Authors Ginley B, Jen KY, Han SS, Rodrigues L, Jain S, Fogo AB, Zuckerman J, Walavalkar
V, Miecznikowski JC, Wen Y, Yen F, Yun D, Moon KC, Rosenberg A, Parikh C, Sarder
Submitted By Sanjay Jain on 5/5/2021
Status Published
Journal Journal of the American Society of Nephrology : JASN
Year 2021
Date Published 2/1/2021
Volume : Pages Not Specified : Not Specified
PubMed Reference 33622976
Abstract Interstitial fibrosis, tubular atrophy (IFTA), and glomerulosclerosis are
indicators of irrecoverable kidney injury. Modern machine learning (ML) tools
have enabled robust, automated identification of image structures that can be
comparable with analysis by human experts. ML algorithms were developed and
tested for the ability to replicate the detection and quantification of IFTA and
glomerulosclerosis that renal pathologists perform., A renal pathologist
annotated renal biopsy specimens from 116 whole-slide images (WSIs) for IFTA and
glomerulosclerosis. A total of 79 WSIs were used for training different
configurations of a convolutional neural network (CNN), and 17 and 20 WSIs were
used as internal and external testing cases, respectively. The best model was
compared against the input of four renal pathologists on 20 new testing slides.
Further, for 87 testing biopsy specimens, IFTA and glomerulosclerosis
measurements made by pathologists and the CNN were correlated to patient outcome
using classic statistical tools., The best average performance across all image
classes came from a DeepLab version 2 network trained at 40× magnification. IFTA
and glomerulosclerosis percentages derived from this CNN achieved high levels of
agreement with four renal pathologists. The pathologist- and CNN-based analyses
of IFTA and glomerulosclerosis showed statistically significant and equivalent
correlation with all patient-outcome variables., ML algorithms can be trained to
replicate the IFTA and glomerulosclerosis assessment performed by renal
pathologists. This suggests computational methods may be able to provide a
standardized approach to evaluate the extent of chronic kidney injury in
situations in which renal-pathologist time is restricted or unavailable.