LiverQuant: An Improved Method for Quantitative Analysis of Liver Pathology.

Abstract

Current means to quantify cells, gene expression, and fibrosis of liver histological slides are not standardized in the research community and typically rely upon data acquired from a selection of random regions identified in each slide. As such, analyses are subject to selection bias as well as limited subsets of available data elements throughout the slide. A whole-slide analysis of cells and fibrosis would provide for a more accurate and complete quantitative analysis, along with minimization of intra- and inter-experimental variables. Herein, we present LiverQuant, a method for quantifying whole-slide scans of digitized histologic images to render a more comprehensive analysis of presented data elements. After loading images and preparing the project in the QuPath program, researchers are provided with one to two scripts per analysis that generate an average intensity threshold for their staining, automated tissue annotation, and downstream detection of their anticipated cellular matrices. When compared with two standard methodologies for histological quantification, LiverQuant had two significant advantages: increased speed and a 50-fold greater tissue area coverage. Using publicly available open-source code (GitHub), LiverQuant improves the reliability and reproducibility of experimental results while reducing the time scientists require to perform bulk analysis of liver histology. This analytical process is readily adaptable by most laboratories, requires minimal optimization, and its principles and code can be optimized for use in other organs. Graphical overview.