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Published on: April 19, 2018
Using Automated Image Analysis Algorithms to Distinguish Normal, Aberrant, and Degenerate Mitotic Figures Induced by
Alison L Bigley1, Stephanie K Klein1, Barry Davies2
1AstraZeneca, IMED, Pathology Sciences, Alderley Park, Cheshire East, UK.
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.
Insights
An automated image analysis algorithm quantifies mitotic figures in H&E-stained sections, aiding in assessing therapeutic agent safety and efficacy. This method supports preclinical and clinical decisions by providing accurate cell cycle assessment.
Area of Science:
- Toxicology
- Pathology
- Computational Biology
Background:
- Cell cycle modulation is critical for evaluating therapeutic agent safety and efficacy.
- Assessing mitotic figures in tissue sections is key to understanding xenobiotic effects on the cell cycle.
- Traditional methods for mitotic figure quantification rely on manual counting and specialized staining.
Purpose of the Study:
- To develop an automated image analysis algorithm for quantifying mitotic figures in hematoxylin and eosin (H&E)-stained sections.
- To validate the algorithm using data from a xenograft model treated with a cell cycle inhibitor.
- To enable objective assessment of tumor differentiation and compound effects on cell cycle progression.
Main Methods:
- Designed an automated image analysis algorithm for H&E-stained sections.
- Utilized a human transitional cell carcinoma xenograft model in nude rats treated with the Eg5 inhibitor.
- Applied sequential mathematical filters and shape discriminators for classifying mitotic figures.
- Scanned and digitized H&E-stained xenografts for analysis.
Main Results:
- The algorithm successfully classified cell populations into normal, aberrant, or degenerate mitotic figures.
- Generated data on mitotic figure variants and mitotic indices correlated with manual pathologist assessment.
- The classification system represented three degrees of morphological change linked to tumor differentiation and compound effects.
Conclusions:
- The automated algorithm provides a reliable method for quantifying mitotic figures in H&E-stained sections.
- This tool supports both efficacy and toxicity studies by enabling accurate cell cycle assessment.
- The developed system aids in preclinical and clinical decision-making regarding therapeutic agents.
