Using Automated Image Analysis Algorithms to Distinguish Normal, Aberrant, and Degenerate Mitotic Figures Induced by Eg5 Inhibition

Abstract

Modulation of the cell cycle may underlie the toxicologic or pharmacologic responses of a potential therapeutic agent and contributes to decisions on its preclinical and clinical safety and efficacy. The descriptive and quantitative assessment of normal, aberrant, and degenerate mitotic figures in tissue sections is an important end point characterizing the effect of xenobiotics on the cell cycle. Historically, pathologists used manual counting and special staining visualization techniques such as immunohistochemistry for quantification of normal, aberrant, and degenerate mitotic figures. We designed an automated image analysis algorithm for measuring these mitotic figures in hematoxylin and eosin (H&E)-stained sections. Algorithm validation methods used data generated from a subcutaneous human transitional cell carcinoma xenograft model in nude rats treated with the cell cycle inhibitor Eg5. In these studies, we scanned and digitized H&E-stained xenografts and applied a complex ruleset of sequential mathematical filters and shape discriminators for classification of cell populations demonstrating normal, aberrant, or degenerate mitotic figures. The resultant classification system enabled the representations of three identifiable degrees of morphological change associated with tumor differentiation and compound effects. The numbers of mitotic figure variants and mitotic indices data generated corresponded to a manual assessment by a pathologist and supported automated algorithm verification and application for both efficacy and toxicity studies.