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Ovarian Toxicity Assessment in Histopathological Images Using Deep Learning
Fangyao Hu1, Leah Schutt1, Cleopatra Kozlowski1
1Department of Safety Assessment, Genentech, South San Francisco, CA, USA.
Abstract:
As ovarian toxicity is often a safety concern for cancer therapeutics, identification of ovarian pathology is important in early stages of preclinical drug development, particularly when the intended patient population include women of child-bearing potential. Microscopic evaluation by pathologists of hematoxylin and eosin (H&E)-stained tissues is the current gold standard for the assessment of organs in toxicity studies. However, digital pathology and advanced image analysis are being explored with greater frequency and broader applicability to tissue evaluations in toxicologic pathology. Our objective in this work was to develop an automated method that rapidly enumerates rat ovarian corpora lutea on standard H&E-stained slides with comparable accuracy to the gold standard assessment by a pathologist. Herein, we describe an algorithm generated by a deep learning network and tested on 5 rat toxicity studies, which included studies that both had and had not previously been diagnosed with effects on number of ovarian corpora lutea. Our algorithm could not only enumerate corpora lutea accurately in all studies but also revealed distinct trends for studies with and without reproductive toxicity. Our method could be a widely applied tool to aid analysis in general toxicity studies.
Insights
An automated deep learning method accurately counts rat ovarian corpora lutea in toxicity studies. This digital pathology tool aids in early detection of reproductive toxicity, improving drug safety assessments.
Area of Science:
- Toxicologic Pathology
- Digital Pathology
- Computational Biology
Background:
- Ovarian toxicity is a critical safety concern in preclinical cancer therapeutic development.
- Accurate identification of ovarian pathology is essential for drugs targeting women of child-bearing potential.
- Traditional histopathological assessment using hematoxylin and eosin (H&E) staining is the current standard but can be labor-intensive.
Purpose of the Study:
- To develop an automated deep learning algorithm for rapid and accurate enumeration of rat ovarian corpora lutea.
- To validate the algorithm's performance against pathologist-based assessment on H&E-stained slides.
- To assess the algorithm's utility in identifying reproductive toxicity trends in preclinical studies.
Main Methods:
- Development of a deep learning network algorithm for automated counting of corpora lutea.
- Testing the algorithm on H&E-stained ovarian tissues from five rat toxicity studies.
- Comparative analysis of algorithm counts versus pathologist evaluations.
Main Results:
- The deep learning algorithm accurately enumerated corpora lutea across all tested studies.
- The method demonstrated comparable accuracy to manual pathologist assessment.
- The algorithm successfully identified distinct trends in studies with and without observed reproductive toxicity.
Conclusions:
- Automated deep learning offers a rapid and accurate method for quantifying rat ovarian corpora lutea.
- This digital pathology approach can significantly aid toxicologic pathology assessments in preclinical drug development.
- The tool has the potential to enhance the efficiency and reliability of reproductive toxicity evaluations.
