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
Comments (0)