Hepatic elastin content is predictive of adverse outcome in advanced fibrotic liver disease.

AIMS: The aim of this study was to determine if elastin content in needle core native liver biopsies was predictive of clinical outcome in patients with chronic hepatitis C virus-related chronic liver disease. METHODS AND RESULTS: Elastin contents in liver biopsies were determined by image analysis, technically validated in an independent centre, and correlated with outcome in patients with advanced (Ishak stage ≥5) chronic hepatitis C virus-related chronic liver disease. Elastin was robustly quantified in an operator-independent and laboratory-independent manner, with very strong correlation of elastin staining measured with two methods of image classification (rs = 0.873, P < 0.00001). Elastin content (but not absolute scar content or Ishak stage) was predictive for future clinical outcomes. In a cohort of patients without sustained virological response, the median hepatic elastin content was 3.4%, and 17 patients (57%) progressed to a liver-related clinical outcome; 11 of the 15 patients (73%) with a hepatic elastin content of >3.4% progressed to a clinical outcome, as compared with only six of 15 (40%) with an elastin content of <3.4%. The difference in time to outcome was significant. CONCLUSIONS: We describe a simple and reproducible method for elastin quantification in liver biopsies that provides potentially valuable prognostic information to inform clinical management.

Integration of geoscience frameworks into digital pathology analysis permits quantification of microarchitectural relationships in histological landscapes.

Although gold-standard histological assessment is subjective it remains central to diagnosis and clinical trial protocols and is crucial for the evaluation of any preclinical disease model. Objectivity and reproducibility are enhanced by quantitative analysis of histological images but current methods require application-specific algorithm training and fail to extract understanding from the histological context of observable features. We reinterpret histopathological images as disease landscapes to describe a generalisable framework defining topographic relationships in tissue using geoscience approaches. The framework requires no user-dependent training to operate on all image datasets in a classifier-agnostic manner but is adaptable and scalable, able to quantify occult abnormalities, derive mechanistic insights, and define a new feature class for machine-learning diagnostic classification. We demonstrate application to inflammatory, fibrotic and neoplastic disease in multiple organs, including the detection and quantification of occult lobular enlargement in the liver secondary to hilar obstruction. We anticipate this approach will provide a robust class of histological data for trial stratification or endpoints, provide quantitative endorsement of experimental models of disease, and could be incorporated within advanced approaches to clinical diagnostic pathology.