ArticleDiagnostic pathology2018
Focused scores enable reliable discrimination of small differences in steatosis.
Article in Diagnostic pathology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Digital Pathology Tailored for Assessment of Liver Biopsies.Biomedicines · 2025Article
- Mechanisms underpinning the effect of exercise on the non-alcoholic fatty liver disease: review.EXCLI journal · 2025Review
- [Challenges of automation in quantitative evaluation of liver biopsies : Automatic quantification of liver steatosis].Pathologie (Heidelberg, Germany) · 2024Article
- Tolerance of repeated toxic injuries of murine livers is associated with steatosis and inflammation.Cell death & disease · 2023Article
- Application of digital pathology and machine learning in the liver, kidney and lung diseases.Journal of pathology informatics · 2023Review
- Automated computerized image analysis for the user-independent evaluation of disease severity in preclinical models of NAFLD/NASH.Laboratory investigation; a journal of technical methods and pathology · 2020Article
- Whole Slide Imaging and Its Applications to Histopathological Studies of Liver Disorders.Frontiers in medicine · 2019Review
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Authors and funding
8 authors.
Funding
Abstract
backgroundAutomated image analysis enables quantitative measurement of steatosis in histological images. However, spatial heterogeneity of steatosis can make quantitative steatosis scores unreliable. To improve the reliability, we have developed novel scores that are "focused" on steatotic tissue areas.
methodsFocused scores use concepts of tile-based hotspot analysis in order to compute statistics about steatotic tissue areas in an objective way. We evaluated focused scores on three data sets of images of rodent liver sections exhibiting different amounts of dietary-induced steatosis. The same evaluation was conducted with the standard steatosis score computed by most image analysis methods.
resultsThe standard score reliably discriminated large differences in steatosis (intraclass correlation coefficient ICC = 0.86), but failed to discriminate small (ICC = 0.54) and very small (ICC = 0.14) differences. With an appropriate tile size, mean-based focused scores reliably discriminated large (ICC = 0.92), small (ICC = 0.86) and very small (ICC = 0.83) differences. Focused scores based on high percentiles showed promise in further improving the discrimination of very small differences (ICC = 0.93).
conclusionsFocused scores enable reliable discrimination of small differences in steatosis in histological images. They are conceptually simple and straightforward to use in research studies.
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