Abstract
Whole-slide imaging (WSI) has wide spectrum of application in histopathology, especially in the study of cancer including papillary thyroid carcinoma. The main applications of WSI system include research, teaching, and assessment and recently pathology practices. The other major advantages of WSI over histological sections on glass slides are easier storage and sharing of information as well as adaptation of use in artificial intelligence. The applications of WSI depend on factors such as volume of services requiring WSI, physical factors (computer server, bandwidth limitation of networks, storages requirements for data), adaption of the WSI images with the laboratory workflow, personnel (IT expert, pathologist, technicians) adaptation to the WSI workflow, validation studies, ethics, and cost efficiency of the application(s).
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References
Abels E, Pantanowitz L, Aeffner F, Zarella MD, van der Laak J, Bui MM, Vemuri VN, Parwani AV, Gibbs J, Agosto-Arroyo E, Beck AH, Kozlowski C (2019) Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association. J Pathol 249:286–294. https://doi.org/10.1002/path.5331. PMID: 31355445
Bychkov A, Sampatanukul P, Shuangshoti S, Keelawat S (2016) TROP-2 immunohistochemistry: a highly accurate method in the differential diagnosis of papillary thyroid carcinoma. Pathology 48:425–433. https://doi.org/10.1016/j.pathol.2016.04.002. PMID: 27311870.
Salajegheh A, Dolan-Evans E, Sullivan E, Irani S, Rahman MA, Vosgha H, Gopalan V, Smith RA, Lam AK (2014) The expression profiles of the galectin gene family in primary and metastatic papillary thyroid carcinoma with particular emphasis on galectin-1 and galectin-3 expression. Exp Mol Pathol 96:212–218. https://doi.org/10.1016/j.yexmp.2014.02.003. PMID: 24530443.
Irani S, Salajegheh A, Gopalan V, Smith RA, Lam AK (2014) Expression profile of endothelin 1 and its receptor endothelin receptor A in papillary thyroid carcinoma and their correlations with clinicopathologic characteristics. Ann Diagn Pathol 18:43–48. https://doi.org/10.1016/j.anndiagpath.2013.11.001. PMID: 24332749.
Williams TA, Gomez-Sanchez CE, Rainey WE, Giordano TJ, Lam AK, Marker A, Mete O, Yamazaki Y, Zerbini MCN, Beuschlein F, Satoh F, Burrello J, Schneider H, Lenders JWM, Mulatero P, Castellano I, Knösel T, Papotti M, Saeger W, Sasano H, Reincke M (2021) International histopathology consensus for unilateral primary aldosteronism. J Clin Endocrinol Metab 106:42–54. https://doi.org/10.1210/clinem/dgaa484. PMID: 32717746
Aloqaily A, Polonia A, Campelos S, Alrefae N, Vale J, Caramelo A, Eloy C (2021) Digital versus optical diagnosis of follicular patterned thyroid lesions. Head Neck Pathol 15:537–543. https://doi.org/10.1007/s12105-020-01243-y. PMID: 33128731.
Jung CK, Bychkov A, Song DE, Kim JH, Zhu Y, Liu Z, Keelawat S, Lai CR, Hirokawa M, Kameyama K, Kakudo K (2021) Molecular correlates and nuclear features of encapsulated follicular-patterned thyroid neoplasms. Endocrinol Metab 36:123–133. https://doi.org/10.3803/EnM.2020.860. PMID: 33677934
Collins BT, Collins LE (2013) Assessment of malignancy for atypia of undetermined significance in thyroid fine-needle aspiration biopsy evaluated by whole-slide image analysis. Am J Clin Pathol 139:736–745. https://doi.org/10.1309/AJCPQU29GHXYSZRR. PMID: 23690115.
Gerhard R, Teixeira S, Gaspar da Rocha A, Schmitt F (2013) Thyroid fine-needle aspiration cytology: is there a place to virtual cytology? Diagn Cytopathol 41:793–798. https://doi.org/10.1002/dc.22958. PMID: 23441010.
Chain K, Legesse T, Heath JE, Staats PN (2019) Digital image-assisted quantitative nuclear analysis improves diagnostic accuracy of thyroid fine-needle aspiration cytology. Cancer Cytopathol 127:501–513. https://doi.org/10.1002/cncy.22120. PMID: 31150162.
Elliott Range DD, Dov D, Kovalsky SZ, Henao R, Carin L, Cohen J (2020) Application of a machine learning algorithm to predict malignancy in thyroid cytopathology. Cancer Cytopathol 128:287–295. https://doi.org/10.1002/cncy.22238. PMID: 32012493.
Lin YJ, Chao TK, Khalil MA, Lee YC, Hong DZ, Wu JJ, Wang CW (2021) Deep learning fast screening approach on cytological whole slides for thyroid cancer diagnosis. Cancers (Basel) 13:3891. https://doi.org/10.3390/cancers13153891. PMID: 34359792
Girolami I, Marletta S, Pantanowitz L, Torresani E, Ghimenton C, Barbareschi M, Scarpa A, Brunelli M, Barresi V, Trimboli P, Eccher A (2020) Impact of image analysis and artificial intelligence in thyroid pathology, with particular reference to cytological aspects. Cytopathology 31:432–444. https://doi.org/10.1111/cyt.12828. PMID: 3224858
Ghossein R, Barletta JA, Bullock M, Johnson SJ, Kakudo K, Lam AK, Moonim MT, Poller DN, Tallini G, Tuttle RM, Xu B, Gill AJ (2021) Data set for reporting carcinoma of the thyroid: recommendations from the International Collaboration on Cancer Reporting. Hum Pathol 110:62–72. https://doi.org/10.1016/j.humpath.2020.08.009. PMID: 32920035
Xu B, Teplov A, Ibrahim K, Inoue T, Stueben B, Katabi N, Hameed M, Yagi Y, Ghossein R (2020) Detection and assessment of capsular invasion, vascular invasion and lymph node metastasis volume in thyroid carcinoma using microCT scanning of paraffin tissue blocks (3D whole block imaging): a proof of concept. Mod Pathol 33:2449–2457. https://doi.org/10.1038/s41379-020-0605-1. PMID: 32616872
Kumar N, Gupta R, Gupta S (2020) Whole slide imaging (WSI) in pathology: current perspectives and future directions. J Digit Imaging 33:1034–1040. https://doi.org/10.1007/s10278-020-00351-z. PMID: 32468487.
Feng M, Deng Y, Yang L, Jing Q, Zhang Z, Xu L, Wei X, Zhou Y, Wu D, Xiang F, Wang Y, Bao J, Bu H (2020) Automated quantitative analysis of Ki-67 staining and HE images recognition and registration based on whole tissue sections in breast carcinoma. Diagn Pathol 15:65. https://doi.org/10.1186/s13000-020-00957-5. PMID: 32471471
Satturwar SP, Pantanowitz JL, Manko CD, Seigh L, Monaco SE, Pantanowitz L (2020) Ki-67 proliferation index in neuroendocrine tumors: can augmented reality microscopy with image analysis improve scoring? Cancer Cytopathol 128:535–544. https://doi.org/10.1002/cncy.22272. PMID: 32401429.
Lam AK, Ishida H (2021) Pancreatic neuroendocrine neoplasms: clinicopathological features and pathological staging. Histol Histopathol 36:367–382. https://doi.org/10.14670/HH-18-288. PMID: 33305819.
Lam AK (2017) Update on adrenal tumours in 2017 World Health Organization (WHO) of endocrine tumours. Endocr Pathol 28:213–227. https://doi.org/10.1007/s12022-017-9484-5. PMID: 28477311.
Aeffner F, Zarella MD, Buchbinder N, Bui MM, Goodman MR, Hartman DJ, Lujan GM, Molani MA, Parwani AV, Lillard K, Turner OC, Vemuri VNP, Yuil-Valdes AG, Bowman D (2019) Introduction to digital image analysis in whole-slide imaging: a white paper from the Digital Pathology Association. J Pathol Inform 10:9. https://doi.org/10.4103/jpi.jpi_82_18. PMID: 30984469
Dzaparidze G, Kazachonok D, Laht K, Taelma H, Minajeva A (2020) Pathadin – the essential set of tools to start with whole slide analysis. Acta Histochem 122:151619. https://doi.org/10.1016/j.acthis.2020.151619. PMID:33066841.
Jahn SW, Plass M, Moinfar F (2020) Digital pathology: advantages, limitations and emerging perspectives. J Clin Med 9:3697. https://doi.org/10.3390/jcm9113697. PMID:33217963
Koelzer VH, Grobholz R, Zlobec I, Janowczyk A, Swiss Digital Pathology Consortium (SDiPath) (2021) Update on the current opinion, status and future development of digital pathology in Switzerland in light of COVID-19. J Clin Pathol. https://doi.org/10.1136/jclinpath-2021-207768. PMID: 34518361
Lujan GM, Savage J, Shana’ah A, Yearsley M, Thomas D, Allenby P, Otero J, Limbach AL, Cui X, Scarl RT, Hardy T, Sheldon J, Plaza JA, Whitaker B, Frankel W, Parwani AV, Li Z (2021) Digital pathology initiatives and experience of a large academic institution during the coronavirus disease 2019 (COVID-19) pandemic. Arch Pathol Lab Med 145:1051–1061. https://doi.org/10.5858/arpa.2020-0715-SA. PMID: 33946103.
Li Y, Chen P, Li Z, Su H, Yang L, Zhong D (2020) Rule-based automatic diagnosis of thyroid nodules from intraoperative frozen sections using deep learning. Artif Intell Med 108:101918. https://doi.org/10.1016/j.artmed.2020.101918. PMID: 32972671.
Chen P, Shi X, Liang Y, Li Y, Yang L, Gader PD (2020) Interactive thyroid whole slide image diagnostic system using deep representation. Comput Methods Prog Biomed 195:105630. https://doi.org/10.1016/j.cmpb.2020.105630. PMID: 32634647.
Yu H, Gao F, Jiang L, Ma S (2017) Development of a whole slide imaging system on smartphones and evaluation with frozen section samples. JMIR Mhealth Uhealth 5:e132. https://doi.org/10.2196/mhealth.8242. PMID: 28916508.
Unternaehrer J, Grobholz R, Janowczyk A, Zlobec I, Swiss Digital Pathology Consortium (SDiPath) (2020) Current opinion, status and future development of digital pathology in Switzerland. J Clin Pathol 73:341–346. https://doi.org/10.1136/jclinpath-2019-206155. PMID: 31857377.
Van Es SL, Kumar RK, Pryor WM, Salisbury EL, Velan GM (2015) Cytopathology whole slide images and adaptive tutorials for postgraduate pathology trainees: a randomized crossover trial. Hum Pathol 46:1297–1305. https://doi.org/10.1016/j.humpath.2015.05.009. PMID: 26093936.
Van Es SL, Kumar RK, Pryor WM, Salisbury EL, Velan GM (2016) Cytopathology whole slide images and adaptive tutorials for senior medical students: a randomized crossover trial. Diagn Pathol 11:1. https://doi.org/10.1186/s13000-016-0452-z. PMID: 26746436
Gopalan V, Kasem K, Pillai S, Olveda D, Ariana A, Leung M, Lam AKY (2018) Evaluation of multidisciplinary strategies and traditional approaches in teaching pathology in medical students. Pathol Int 68:459–466. https://doi.org/10.1111/pin.12706. PMID: 30043440
Niazi MKK, Parwani AV, Gurcan MN (2019) Digital pathology and artificial intelligence. Lancet Oncol 20:e253–e261. https://doi.org/10.1016/S1470-2045(19)30154-8. PMID: 31044723.
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Lam, A.K., Bai, A., Leung, M. (2022). Whole-Slide Imaging: Updates and Applications in Papillary Thyroid Carcinoma. In: Lam, A.K. (eds) Papillary Thyroid Carcinoma. Methods in Molecular Biology, vol 2534. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2505-7_14
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DOI: https://doi.org/10.1007/978-1-0716-2505-7_14
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