DIGITAL PATHOLOGY IN OF MEDICAL LABORATORY PRACTICE. REVIEWT
DOI:
https://doi.org/10.11603/mie.1996-1960.2020.3.11608Abstract
Background. Digital pathology is an integral technological element of the research and diagnostic environment of Laboratories and plays an essential role in modern clinical practice. Implementation of the whole-slide digital images provided the ability to observe and share images between pathologists and specialists in other specialties.
Materials and methods. Results. The development of appropriate software and solutions for the storage and exchange of digital slides has determined the widespread use of digital pathology in the educational process in the training of cytopathologists, pathologists and molecular pathologists. The integration of digital drugs into the working processes of the pathology laboratory, improved machine learning algorithms have expanded the possibilities for the analysis of histological drugs, evaluation of the expression of biomarkers, interpretation of their clinical significance. At the same time, advances in machine learning have identified the synergy of artificial intelligence and digital pathology.
Conclusions. The synergy of artificial intelligence and digital pathology in diagnostics of cancers illuminates the possibility of integrating pathohistological data with the medical history, laboratory data, radiological examination and molecular genetic testing. These provide opportunities for advanced diagnostics and tailored treatment in line with personalized medicine goals.
References
Bilousova L. I., Zhyteneva N. V. (2014). Didakticheskie aspekty ispol'zovaniya tekhnologii vizualizatsii v uchebnom protsesse obshcheobrazovatel'noi shkoly (Didactic aspects using technology of vizualization in educational process of secondary school) // Іnformatsіinі tekhnologii і zasobi navchannya (Information Technologies and Learning Tools), 40 (2). URL: http://journal.iitta.gov.ua/index.php/itlt/ article/view/1017.
Acs B., Hartman J. (2020). Next generation pathology: artificial intelligence enhances histopathology practice. Pathol., 250 (1), 7-8. doi: 10.1002/path.5343.
Al-Quteimat O. M., Amer A. M. (2020). The impact of the COVID-19 pandemic on cancer patients. Am. J. Clin Oncol., 43 (6), 452-455. doi: 10.1097/ COC.0000000000000712.
Leung S. C. Y., Nielsen T. O., Zabaglo L. A., ... Dowsett M. (2019). Analytical validation of a standardised scoring protocol for Ki67 immunohistochemistry on breast cancer excision whole sections: an international multicenter collaboration. Histopathology, 75 (2), 225235. https://doi.org/10.1111/his.13880.
Dlamini Z., Francies F. Z., Hull R., Marima R. (2020). Artificial intelligence (AI) and big data in cancer and precision oncology. Comput. Struct. Biotechnol. J., 18, 2300-2311. doi.org/10.1016/j.csbj.2020.08.019.
Colling R., Pitman H., Oien K., CM-Path AI in Histopathology Working Group, ... Verrill C. (2019). Artificial intelligence in digital pathology: a roadmap to routine use in clinical practice. J. Pathol., 249 (2), 143-150. doi: 10.1002/path.5310.
Coudray N., Ocampo P. S., Sakellaropoulos, T., ... Tsirigos, A. (2018). Classification and mutation prediction from non-small cell lung cancer histopathologyimages using deep learning. Nat. Med., 24, 1559-1567. doi. org/10.1038/s41591-018-0177-5.
Campanella G., Hanna M. G., Geneslaw L., Miraflor A., Werneck V., Silva K., ...Fuchs T. J. (2019). Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat Med., 25 (8), 1301-1309. doi: 10.1038/s41591-019-0508-1.
Kather J. N., Pearson A. T., Halama N., . Luedde T. (2019). Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat. Med., 25 (7), 1054-1056. doi: 10.1038/ s41591-019-0462-y.
Fuyong Xing, Yuanpu Xie, Hai Su, Fujun Liu, Lin Yang. (2018). Deep learning in microscopy image analysis: a survey. IEEE Trans. Neural Netw. Learn Syst., 29 (10), 4550-4568. doi: 10.1109/TNNLS.2017.2766168.
Esteva A., Kuprel B., Novoa R. A., Ko J., Swetter S. M., Blau H. M., Thrun S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542 (7639), 115-118. doi: 10.1038/nature21056.
Gulshan V., Peng L., Coram M., ... Webster D. R. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA, 316 (22), 2402-2410. doi: 10.1001/jama.2016.17216
Bejnordi B., Veta M., van Diest P., ... Venancio, R. (2017). Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA, 318 (22), 2199-2210. doi:10.1001/jama.2017.14585.
House J. C., Henderson-Jackson E. B., Johnson J. O., .Bui M. M. (2013). Diagnostic digital cytopathology: Are we ready yet? J. Pathol. Inform., 4, 28. doi: 10.4103/2153-3539.120727.
Stalhammar G., Fuentes Martinez N., Lippert M., . Hartman J. (2016). Digital image analysis outperforms manual biomarker assessment in breast cancer. Mod. Pathol., 29 (4), 318-329. doi: 10.1038/ modpathol.2016.34.
Cimadamore A., Lopez-Beltran A., Scarpelli M., Cheng L., Montironi, R. (2020). Digital pathology and COVID-19 and future crises: pathologists can safely diagnose cases from home using a consumer monitor and a mini PC. J. Clin. Pathol., 73 (11), 695-696. doi: 10.1136/jclinpath-2020-206943.
Pantanowitz L., Wiley C. A., Demetris A., ... Parwani, A. V. (2012). Experience with multimodality telepathology at the University of Pittsburgh Medical Center. J. Pathol Inform., 3, 45. doi: 10.4103/2153-3539.104907.
Vigliar E., Cepurnaite R., Alcaraz-Mateos E., ... Troncone G. (2020). Global impact of the COVID-19 pandemic on cytopathology practice: Results from an international survey of laboratories in 23 countries. Cancer Cytopathol. doi: 10.1002!cncy.22373.
Griffin J., Treanor D. (2017). Digital pathology in clinical use: where are we now and what is holding us back? Histopathology, 70 (1), 134-145. doi: 10.1111/ his.12993.
Aeffner F., Zarella M. D., Buchbinder N., ... 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). doi: 10.4103/jpi.jpi_82_18.
Iaccarino A., Pisapia P., Vigliar E., Vielh P., Troncone G. (2020). Juggling the COVID-19 pandemic: a cytopathologist point of view. Cytopathology. First published: 03 November 2020 doi: 10.111Hcyt.12936.
Meijering E. (2020). A bird's-eye view of deep learning in bioimage analysis. Comput. Struct. Biotechnol. J., 18, 2312-2325. doi:10.1016/j.csbj.2020.08.003.
Niazi M. K. K., Parwani A. V., Gurcan M. N. (2019). Digital pathology and artificial intelligence. Lancet Oncol., 20 (5), e253-e261. doi: 10.1016/S1470-2045(19)30154-8.
Browning L., Colling R., Rakha E., ... Verrill C. (2020). Digital pathology and artificial intelligence will be key to supporting clinical and academic cellular pathology through COVID-19 and future crises: the PathLAKE consortium perspective. J. Clin. Pathol., Published Online First: 03 July 2020. doi: 10.1136І jclinpath-2020-206854.
Girolami I., Parwani A., Barresi V., ... Eccher, A. (2019). The landscape of digital pathology in transplantation: from the beginning to the virtual e-slide. J. Pathol. Inform., 10, 21. doi: 10.4103/jpi.jpi_27_19.
Tizhoosh H. R, Pantanowitz L. (2018). Artificial intelligence and digital pathology: challenges and opportunities. J. Pathol. Inform., 9, 38. doi:10.4103/ jpi.jpi_53_18.
Pantanowitz L., Sharma A., Carter A. B., Kurc T., Sussman A., Saltz J. (2018). Twenty years of digital pathology: an overview of the road travelled, what is on the horizon, and the emergence of vendor-neutral archives. J. Pathol. Inform., 9, 40. doi: 10.4103/jpi. jpi_69_18.
Hanna M. G., Reuter V. E., Ardon O., . Hameed M. (2020). Validation of a digital pathology system including remote review during the COVID-19 pandemic. Mod. Pathol., 33 (11), 2115-2127. doi:10.1038!s41379-020-0601-5.
Andersen L. N., Brugmann A., Lelkaitis G., Nielsen S., Lippert M. F., Vyberg M. (2018). Virtual double staining. A digital approach to immunohistochemical quantification of estrogen receptor protein in breast carcinoma specimens. Appl. Immunohistochem. Mol. Morphol., 26 (9), 620-626. doi: 10.1097І PAI.0000000000000502.
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