Chronic cholestasis detection by a novel tool: automated analysis of cytokeratin 7-stained liver specimens

Autor: Sjöblom, Nelli, Boyd, Sonja, Manninen, Anniina, Knuuttila, Anna, Blom, Sami, Färkkilä, Martti, Arola, Johanna
Přispěvatelé: HUSLAB, Department of Pathology, University of Helsinki, Helsinki University Hospital Area, Centre of Excellence in Complex Disease Genetics, HUS Abdominal Center, Department of Medicine, Clinicum, Gastroenterologian yksikkö
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Diagnostic Pathology
Diagnostic Pathology, Vol 16, Iss 1, Pp 1-12 (2021)
ISSN: 1746-1596
Popis: Background The objective was to build a novel method for automated image analysis to locate and quantify the number of cytokeratin 7 (K7)-positive hepatocytes reflecting cholestasis by applying deep learning neural networks (AI model) in a cohort of 210 liver specimens. We aimed to study the correlation between the AI model’s results and disease progression. The cohort of liver biopsies which served as a model of chronic cholestatic liver disease comprised of patients diagnosed with primary sclerosing cholangitis (PSC). Methods In a cohort of patients with PSC identified from the PSC registry of the University Hospital of Helsinki, their K7-stained liver biopsy specimens were scored by a pathologist (human K7 score) and then digitally analyzed for K7-positive hepatocytes (K7%area). The digital analysis was by a K7-AI model created in an Aiforia Technologies cloud platform. For validation, values were human K7 score, stage of disease (Metavir and Nakunuma fibrosis score), and plasma liver enzymes indicating clinical cholestasis, all subjected to correlation analysis. Results The K7-AI model results (K7%area) correlated with the human K7 score (0.896; p
Databáze: OpenAIRE
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