Artificial Intelligence in Toxicological Pathology: Quantitative Evaluation of Compound-Induced Follicular Cell Hypertrophy in Rat Thyroid Gland Using Deep Learning Models

1. Huisinga, M, Bertrand, L, Chamanza, R, et al. Adversity considerations for thyroid follicular cell hypertrophy and hyperplasia in nonclinical toxicity studies: results from the 6th ESTP International Expert Workshop. Toxicol Pathol. 2020;48(8):920–938. doi:10.1177/0192623320972009
Google Scholar | SAGE Journals | ISI2. Brändli-Baiocco, A, Balme, E, Bruder, M, et al. Nonproliferative and proliferative lesions of the rat and mouse endocrine system. J Toxicol Pathol. 2018;31(3 suppl):1S–95S. doi:10.1293/tox.31.1S
Google Scholar | Crossref | Medline3. Asaoka, Y, Togashi, Y, Mutsuga, M, Imura, N, Miyoshi, T, Miyamoto, Y. Histopathological image analysis of chemical-induced hepatocellular hypertrophy in mice. Exp Toxicol Pathol. 2016;68(4):233–239. doi:10.1016/j.etp.2015.12.005
Google Scholar | Crossref | Medline | ISI4. Sutcliffe, C, Harvey, PW. Endocrine disruption of thyroid function: chemicals, mechanisms, and toxicopathology. In: Darbre, PD , ed. Endocrine Disruption and Human Health. Academic Press; 2015. doi:10.1016/B978-0-12-801139-3.00011-9
Google Scholar | Crossref5. Papineni, S, Marty, MS, Rasoulpour, RJ, LeBaron, MJ, Pottenger, LH, Eisenbrandt, DL. Mode of action and human relevance of pronamide-induced rat thyroid tumors. Regul Toxicol Pharmacol. 2015;71(3):541–551. doi:10.1016/j.yrtph.2015.02.012
Google Scholar | Crossref | Medline6. Yamaguchi, T, Maeda, M, Ogata, K, Abe, J, Utsumi, T, Kimura, K. The effects on the endocrine system under hepatotoxicity induction by phenobarbital and di(2-ethylhexyl)phthalate in intact juvenile male rats. J Toxicol Sci. 2019;44(7):459–469. doi:10.2131/jts.44.459
Google Scholar | Crossref | Medline7. McClain, RM, Levin, AA, Posch, R, Downing, JC. The effect of phenobarbital on the metabolism and excretion of thyroxine in rats. Toxicol Appl Pharmacol. 1989;99(2):216–228. doi:10.1016/0041-008X(89)90004-5
Google Scholar | Crossref | Medline8. Zabka, TS, Fielden, MR, Garrido, R, et al. Characterization of xenobiotic-induced hepatocellular enzyme induction in rats: anticipated thyroid effects and unique pituitary gland findings. Toxicol Pathol. 2011;39(4):664–677. doi:10.1177/0192623311406934
Google Scholar | SAGE Journals | ISI9. Hall, AP, Elcombe, CR, Foster, JR, et al. Liver hypertrophy: a review of adaptive (adverse and non-adverse) changes-conclusions from the 3rd International ESTP Expert Workshop. Toxicol Pathol. 2012;40(7):971–994. doi:10.1177/0192623312448935
Google Scholar | SAGE Journals | ISI10. Vickers, AEM, Heale, J, Sinclair, JR, Morris, S, Rowe, JM, Fisher, RL. Thyroid organotypic rat and human cultures used to investigate drug effects on thyroid function, hormone synthesis and release pathways. Toxicol Appl Pharmacol. 2012;260(1):81–88. doi:10.1016/j.taap.2012.01.029
Google Scholar | Crossref | Medline11. EPA US . Guidance for Thyroid Assays in Pregnant Animals, Fetuses and Postnatal Animals, and Adult Animals. EPA US; Published online 2005.
Google Scholar12. Schafer, KA, Eighmy, J, Fikes, JD, et al. Use of severity grades to characterize histopathologic changes. Toxicol Pathol. 2018;46(3):256–265. doi:10.1177/0192623318761348
Google Scholar | SAGE Journals | ISI13. Ettlin, RA . Toxicologic pathology in the 21st century. Toxicol Pathol. 2013;41(5):689–708. doi:10.1177/0192623312466192
Google Scholar | SAGE Journals | ISI14. Garrido, R, Zabka, TS, Tao, J, Fielden, M, Fretland, A, Albassam, M. Quantitative histological assessment of xenobiotic-induced liver enzyme induction and pituitary-thyroid axis stimulation in rats using whole-slide automated image analysis. J Histochem Cytochem. 2013;61(5):362–371. doi:10.1369/0022155413482926
Google Scholar | SAGE Journals15. Aeffner, F, Zarella, MD, Buchbinder, N, et al. Introduction to digital image analysis in whole-slide imaging: a white paper from the digital pathology association. J Pathol Inform. 2019;10:9. doi:10.4103/jpi.jpi_82_18
Google Scholar | Crossref | Medline16. Girolami, I, Marletta, S, Pantanowitz, L, et al. Impact of image analysis and artificial intelligence in thyroid pathology, with particular reference to cytological aspects. Cytopathology. 2020;31(5):432–444. doi:10.1111/cyt.12828
Google Scholar | Crossref | Medline17. Turner, OC, Knight, B, Zuraw, A, Litjens, G, Rudmann, DG. Mini review: the last mile—opportunities and challenges for machine learning in digital toxicologic pathology. Toxicol Pathol. 2021;49(4):714–719. doi:10.1177/0192623321990375
Google Scholar | SAGE Journals | ISI18. Turner, OC, Aeffner, F, Bangari, DS, et al. Society of Toxicologic Pathology Digital Pathology and Image Analysis Special Interest Group Article*: Opinion on the application of artificial intelligence and machine learning to digital toxicologic pathology. Toxicol Pathol. 2020;48(2):277–294. doi:10.1177/0192623319881401
Google Scholar | SAGE Journals | ISI19. Saravanan, C, Schumacher, V, Brown, D, et al. Meeting report: tissue-based image analysis. Toxicol Pathol. 2017;45(7):983–1003. doi:10.1177/0192623317737468
Google Scholar | SAGE Journals | ISI20. Aeffner, F, Wilson, K, Bolon, B, et al. Commentary: roles for pathologists in a high-throughput image analysis team. Toxicol Pathol. 2016;44(6):825–834. doi:10.1177/0192623316653492
Google Scholar | SAGE Journals | ISI21. Food and Drug Administration. IntelliSite pathology solution. Updated April 17, 2017. Accessed June 5, 2021. From U.S. Food and Drug Administrations web site: https://www.fda.gov/drugs/resources-information-approved-drugs/intellisite-pathology-solution-pips-philips-medical-systems
Google Scholar22. Bera, K, Schalper, KA, Rimm, DL, Velcheti, V, Madabhushi, A. Artificial intelligence in digital pathology—new tools for diagnosis and precision oncology. Nat Rev Clin Oncol. 2019;16(11):703–715. doi:10.1038/s41571-019-0252-y
Google Scholar | Crossref | Medline23. Ruehl-Fehlert, C, Kittel, B, Morawietz, G, et al. Revised guides for organ sampling and trimming in rats and mice—Part 1. Exp Toxicol Pathol. 2003;55(2-3):91–106. doi:10.1078/0940-2993-00311
Google Scholar | Crossref | Medline | ISI24. Ronneberger, O, Fischer, P, Brox, T. U-net: Convolutional networks for biomedical image segmentation. In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 2015. doi:10.1007/978-3-319-24574-4_28
Google Scholar | Crossref25. Maddocks, S, Jenkins, R. Quantitative PCR: things to consider. Underst PCR. Published online 2017. doi:10.1016/B978-0-12-802683-0.00004-6
Google Scholar | Crossref26. Livak, KJ, Schmittgen, TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2-ΔΔCT method. Methods. 2001;25(4):402–408. doi:10.1006/meth.2001.1262
Google Scholar | Crossref | Medline | ISI27. Pischon, H, Mason, D, Lawrenz, B, et al. Artificial intelligence in toxicologic pathology: quantitative evaluation of compound-induced hepatocellular hypertrophy in rats. Toxicol Pathol. 2021;49(4):928–937. doi:10.1177/0192623320983244
Google Scholar | SAGE Journals | ISI28. Chen, M, Zhang, B, Topatana, W, et al. Classification and mutation prediction based on histopathology H&E images in liver cancer using deep learning. npj Precis Oncol. 2020;4(1):14. doi:10.1038/s41698-020-0120-3
Google Scholar | Crossref | Medline29. Nakazato, M, Chung, HK, Ulianich, L, Grassadonia, A, Suzuki, K, Kohn, LD. Thyroglobulin repression of thyroid transcription factor 1 (TTF-1) gene expression is mediated by decreased DNA binding of nuclear factor 1 proteins which control constitutive TTF-1 expression. Mol Cell Biol. 2000;20(22):8499–8512. doi:10.1128/mcb.20.22.8499-8512.2000
Google Scholar | Crossref | Medline30. Ohara, A, Yamada, F, Fukuda, T, Suzuki, N, Sumida, K. Specific alteration of gene expression profile in rats by treatment with thyroid toxicants that inhibit thyroid hormone synthesis. J Appl Toxicol. 2018;38(12):1529–1537. doi:10.1002/jat.3693
Google Scholar | Crossref | Medline31. Yoshihara, A, Luo, Y, Ishido, Y, et al. Inhibitory effects of methimazole and propylthiouracil on iodotyrosine deiodinase 1 in thyrocytes. Endocr J. 2019;66(4):349–357. doi:10.1507/endocrj.EJ18-0380
Google Scholar | Crossref | Medline32. Nam, S, Chong, Y, Jung, CK, et al. Introduction to digital pathology and computer-aided pathology. J Pathol Transl Med. 2020;54(2):125–134. doi:10.4132/jptm.2019.12.31
Google Scholar | Crossref | Medline33. Aeffner, F, Wilson, K, Martin, NT, et al. The gold standard paradox in digital image analysis: manual versus automated scoring as ground truth. Arch Pathol Lab Med. 2017;141(9):1267–1275. doi:10.5858/arpa.2016-0386-RA
Google Scholar | Crossref | Medline

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