Department of Safety Assessment, Genentech, South San Francisco, CA, USA.
Regan Path/Tox Services, Ashland, OH, USA.
Toxicol Pathol. 2020 Feb;48(2):350-361. doi: 10.1177/0192623319877871. Epub 2019 Oct 8.
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.
由于卵巢毒性通常是癌症治疗的一个安全关注点，因此在临床前药物开发的早期阶段识别卵巢病变非常重要，特别是当目标患者人群包括有生育能力的女性时。病理学家对苏木精和伊红（H&E）染色组织的显微镜评估是评估毒性研究中器官的当前金标准。然而，数字病理学和先进的图像分析正越来越频繁地被探索，并更广泛地应用于毒理学病理学中的组织评估。我们在这项工作中的目标是开发一种自动化方法，该方法能够快速对标准 H&E 染色载玻片上的大鼠卵巢黄体进行计数，其准确性可与病理学家的金标准评估相媲美。在此，我们描述了一种由深度学习网络生成并在 5 项大鼠毒性研究中测试的算法，这些研究既包括以前已诊断出对卵巢黄体数量有影响的研究，也包括以前未诊断出有影响的研究。我们的算法不仅可以在所有研究中准确地对黄体进行计数，而且还可以揭示具有和不具有生殖毒性的研究之间的明显趋势。我们的方法可能是一种广泛应用的工具，可以帮助一般毒性研究的分析。
