Elsevier

EBioMedicine

Volume 60, October 2020, 103029
EBioMedicine

Research paper
Deep learning quantification of percent steatosis in donor liver biopsy frozen sections

https://doi.org/10.1016/j.ebiom.2020.103029Get rights and content
Under a Creative Commons license
open access

Abstract

Background

Pathologist evaluation of donor liver biopsies provides information for accepting or discarding potential donor livers. Due to the urgent nature of the decision process, this is regularly performed using frozen sectioning at the time of biopsy. The percent steatosis in a donor liver biopsy correlates with transplant outcome, however there is significant inter- and intra-observer variability in quantifying steatosis, compounded by frozen section artifact. We hypothesized that a deep learning model could identify and quantify steatosis in donor liver biopsies.

Methods

We developed a deep learning convolutional neural network that generates a steatosis probability map from an input whole slide image (WSI) of a hematoxylin and eosin-stained frozen section, and subsequently calculates the percent steatosis. Ninety-six WSI of frozen donor liver sections from our transplant pathology service were annotated for steatosis and used to train (n = 30 WSI) and test (n = 66 WSI) the deep learning model.

Findings

The model had good correlation and agreement with the annotation in both the training set (r of 0.88, intraclass correlation coefficient [ICC] of 0.88) and novel input test sets (r = 0.85 and ICC=0.85). These measurements were superior to the estimates of the on-service pathologist at the time of initial evaluation (r = 0.52 and ICC=0.52 for the training set, and r = 0.74 and ICC=0.72 for the test set).

Interpretation

Use of this deep learning algorithm could be incorporated into routine pathology workflows for fast, accurate, and reproducible donor liver evaluation.

Funding

Mid-America Transplant Society

Keywords

Liver transplantation
Biopsy
Steatosis
Deep learning
Convolutional neural network
Image analysis
Digital pathology

Cited by (0)

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These authors contributed equally