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High-Throughput, Machine Learning-Based Quantification of Steatosis, Inflammation, Ballooning, and Fibrosis in Biopsies from Patients with Nonalcoholic Fatty Liver Disease

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Forlano, Roberta, Mullish, Benjamin H., Giannakeas, Nikolaos, Maurice, James B., Angkathunyakul, Napat, Lloyd, Josephine, Tzallas, Alexandros T., Tsipouras, Markos, Yee, Michael, Thursz, Mark R., Goldin, Robert D., Manousou, Pinelopi·2020·Clinical Gastroenterology and Hepatology
DOI 10.1016/j.cgh.2019.12.025PMC PMC7397508
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Abstract

BACKGROUND & AIMS: Liver biopsy is the reference standard for staging and grading nonalcoholic fatty liver disease (NAFLD), but histologic scoring systems are semiquantitative with marked interobserver and intraobserver variation. We used machine learning to develop fully automated software for quantification of steatosis, inflammation, ballooning, and fibrosis in biopsy specimens from patients with NAFLD and validated the technology in a separate group of patients. METHODS: We collected data from 246 consecutive patients with biopsy-proven NAFLD and followed up in London from January 2010 through December 2016. Biopsy specimens from the first 100 patients were used to derive the algorithm and biopsy specimens from the following 146 were used to validate it. Biopsy specimens were scored independently by pathologists using the Nonalcoholic Steatohepatitis Clinical Research Network criteria and digitalized. Areas of steatosis, inflammation, ballooning, and fibrosis were annotated on biopsy specimens by 2 hepatobiliary histopathologists to facilitate machine learning. Images of biopsies from the derivation and validation sets then were analyzed by the algorithm to compute percentages of fat, inflammation, ballooning, and fibrosis, as well as the collagen proportionate area, and compared with findings from pathologists' manual annotations and conventional scoring systems. RESULTS: In the derivation group, results from manual annotation and the software had an interclass correlation coefficient (ICC) of 0.97 for steatosis (95% CI, 0.95-0.99; P < .001); ICC of 0.96 for inflammation (95% CI, 0.9-0.98; P < .001); ICC of 0.94 for ballooning (95% CI, 0.87-0.98; P < .001); and ICC of 0.92 for fibrosis (95% CI, 0.88-0.96; P = .001). Percentages of fat, inflammation, ballooning, and the collagen proportionate area from the derivation group were confirmed in the validation cohort. The software identified histologic features of NAFLD with levels of interobserver and intraobserver agreement ranging from 0.95 to 0.99; this value was higher than that of semiquantitative scoring systems, which ranged from 0.58 to 0.88. In a subgroup of paired liver biopsy specimens, quantitative analysis was more sensitive in detecting differences compared with the nonalcoholic steatohepatitis Clinical Research Network scoring system. CONCLUSIONS: We used machine learning to develop software to rapidly and objectively analyze liver biopsy specimens for histologic features of NAFLD. The results from the software correlate with those from histopathologists, with high levels of interobserver and intraobserver agreement. Findings were validated in a separate group of patients. This tool might be used for objective assessment of response to therapy for NAFLD in practice and clinical trials.

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Research works printed but not linked: 2

  1. Calvaruso, 2009, Computer-assisted image analysis of liver collagen: relationship to Ishak scoring and hepatic venous pressure gradient, Hepatology, 49, 1236, 10.1002/hep.22745 · DOI
  2. Hall, 2013, Hepatic steatosis estimated microscopically versus digital image analysis, Liver Int, 33, 926, 10.1111/liv.12162 · DOI

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Authors
Forlano, R., Mullish, B. H., Giannakeas, N., Maurice, J. B., Angkathunyakul, N., Lloyd, J., Tzallas, A. T., Tsipouras, M., Yee, M., Thursz, M. R., Goldin, R. D., & Manousou, P.
Affiliations
  • University of Ioannina (GR)
  • University of Western Macedonia (GR)
  • Imperial College Healthcare NHS Trust (GB)
Corresponding author
Manousou, Pinelopi
ORCID
Venue
Clinical Gastroenterology and Hepatology
Publisher
Elsevier BV
Published
2020-08
Volume
18
Issue
9
Pages
2081-2090.e9
ISSN
1542-3565 (ISSN-L)
DOI
10.1016/j.cgh.2019.12.025
PMC
PMC7397508
OpenAlex
W2997774926
Cited by
142
References
31
Citations by year
2026: 15 · 2025: 17 · 2024: 24 · 2023: 27 · 2022: 30 · 2021: 24 · 2020: 5
FWCI
11.23
Type
journal article
Language
en
Open access
Licence
Locations
  • Clinical Gastroenterology and Hepatology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Spiral (Imperial College London) (submittedVersion, cc-by)
Funders
  • Medical Research Council
  • European Association for the Study of the Liver
  • National Institute for Health Research
Topics
  • Health Sciences › Medicine › Hepatology › Liver Disease Diagnosis and Treatment
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords
non-alcoholic fatty liver disease, liver biopsy, machine learning, steatosis, liver inflammation, ballooning, liver fibrosis, collagen proportionate area, histologic scoring system, interobserver agreement, histopathology image analysis
MeSH terms
Machine Learning, Biopsy, Fibrosis, Humans, Inflammation, Liver, Liver Cirrhosis, Severity of Illness Index, Non-alcoholic Fatty Liver Disease

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