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Deep learning-based quantification of NAFLD/NASH progression in human liver biopsies.

Publication ,  Journal Article
Heinemann, F; Gross, P; Zeveleva, S; Qian, HS; Hill, J; Höfer, A; Jonigk, D; Diehl, AM; Abdelmalek, M; Lenter, MC; Pullen, SS; Guarnieri, P ...
Published in: Sci Rep
November 10, 2022

Non-alcoholic fatty liver disease (NAFLD) affects about 24% of the world's population. Progression of early stages of NAFLD can lead to the more advanced form non-alcoholic steatohepatitis (NASH), and ultimately to cirrhosis or liver cancer. The current gold standard for diagnosis and assessment of NAFLD/NASH is liver biopsy followed by microscopic analysis by a pathologist. The Kleiner score is frequently used for a semi-quantitative assessment of disease progression. In this scoring system the features of active injury (steatosis, inflammation, and ballooning) and a separated fibrosis score are quantified. The procedure is time consuming for pathologists, scores have limited resolution and are subject to variation. We developed an automated deep learning method that provides full reproducibility and higher resolution. The system was established with 296 human liver biopsies and tested on 171 human liver biopsies with pathologist ground truth scores. The method is inspired by the way pathologist's analyze liver biopsies. First, the biopsies are analyzed microscopically for the relevant histopathological features. Subsequently, histopathological features are aggregated to a per-biopsy score. Scores are in the identical numeric range as the pathologist's ballooning, inflammation, steatosis, and fibrosis scores, but on a continuous scale. Resulting scores followed a pathologist's ground truth (quadratic weighted Cohen's κ on the test set: for steatosis 0.66, for inflammation 0.24, for ballooning 0.43, for fibrosis 0.62, and for the NAFLD activity score (NAS) 0.52. Mean absolute errors on a test set: for steatosis 0.29, for inflammation 0.53, for ballooning 0.61, for fibrosis 0.78, and for the NAS 0.77).

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Published In

Sci Rep

DOI

EISSN

2045-2322

Publication Date

November 10, 2022

Volume

12

Issue

1

Start / End Page

19236

Location

England

Related Subject Headings

  • Severity of Illness Index
  • Reproducibility of Results
  • Non-alcoholic Fatty Liver Disease
  • Liver Cirrhosis
  • Liver
  • Inflammation
  • Humans
  • Fibrosis
  • Deep Learning
  • Biopsy
 

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Heinemann, F., Gross, P., Zeveleva, S., Qian, H. S., Hill, J., Höfer, A., … Stierstorfer, B. (2022). Deep learning-based quantification of NAFLD/NASH progression in human liver biopsies. Sci Rep, 12(1), 19236. https://doi.org/10.1038/s41598-022-23905-3
Heinemann, Fabian, Peter Gross, Svetlana Zeveleva, Hu Sheng Qian, Jon Hill, Anne Höfer, Danny Jonigk, et al. “Deep learning-based quantification of NAFLD/NASH progression in human liver biopsies.Sci Rep 12, no. 1 (November 10, 2022): 19236. https://doi.org/10.1038/s41598-022-23905-3.
Heinemann F, Gross P, Zeveleva S, Qian HS, Hill J, Höfer A, et al. Deep learning-based quantification of NAFLD/NASH progression in human liver biopsies. Sci Rep. 2022 Nov 10;12(1):19236.
Heinemann, Fabian, et al. “Deep learning-based quantification of NAFLD/NASH progression in human liver biopsies.Sci Rep, vol. 12, no. 1, Nov. 2022, p. 19236. Pubmed, doi:10.1038/s41598-022-23905-3.
Heinemann F, Gross P, Zeveleva S, Qian HS, Hill J, Höfer A, Jonigk D, Diehl AM, Abdelmalek M, Lenter MC, Pullen SS, Guarnieri P, Stierstorfer B. Deep learning-based quantification of NAFLD/NASH progression in human liver biopsies. Sci Rep. 2022 Nov 10;12(1):19236.

Published In

Sci Rep

DOI

EISSN

2045-2322

Publication Date

November 10, 2022

Volume

12

Issue

1

Start / End Page

19236

Location

England

Related Subject Headings

  • Severity of Illness Index
  • Reproducibility of Results
  • Non-alcoholic Fatty Liver Disease
  • Liver Cirrhosis
  • Liver
  • Inflammation
  • Humans
  • Fibrosis
  • Deep Learning
  • Biopsy