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Year 2021, Volume: 38 Issue: 3s, 81 - 85, 09.05.2021

Merva Soluk Tekkeşin Syed Ali Khurram

Abstract

References

  • Araújo, A.L.D., Amaral-Silva, G.K., Fonseca, F.P., Palmier, N.R., Lopes, M.A., Speight,.P.M., de Almeida, O.P., Vargas, P.A., Santos-Silva, A.R., 2018. Validation of digital microscopy in the histopathological diagnoses of oral diseases. Virchows Arch. 473, 321-327.
  • Araújo, A.L.D., Arboleda, L.P.A., Palmier, N.R., Fonsêca, J.M., de Pauli Paglioni, M., Gomes-Silva, W., Ribeiro, A.C.P., Brandão, T.B, Simonato, L.E., Speight, P.M., Fonseca, F.P., Lopes, M.A., de Almeida, O.P., Vargas, P.A., Madrid Troconis, C.C., Santos-Silva, A.R., 2019. The performance of digital microscopy for primary diagnosis in human pathology: a systematic review. Virchows Arch. 474, 269-287.
  • Arvaniti, E., Fricker, K.S., Moret, M., Rupp, N., Hermanns, T., Fankhauser, C., Wey, N., Wild, P.J., Rüschoff, J.H., Claassen, M., 2018. Automated Gleason grading of prostate cancer tissue microarrays via deep learning. Sci Rep. 8(1):12054.
  • Bankhead, P., Loughrey, M.B., Fernández, J.A., Dombrowski, Y., McArt, D.G., Dunne, P.D., McQuaid, S., Gray, R.T., Murray, L.J., Coleman, H.G., James, J.A., Salto-Tellez, M., Hamilton, PW., 2017 Sci Rep. 7(1):16878.
  • Coudray, N., Ocampo, P.S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., Moreira, A.L., Razavian, N., Tsirigos, A., 2018. Classification and mutation prediction from non‐small cell lung cancer histopathology images using deep learning. Nat Med. 24, 1559–1567.
  • Cross, S., Furness, P., Igali, L., Snead, D., Treanor, D., 2018. Best practice recommendations for implementing digital pathology. The Royal College of Pathologists. (https://www.rcpath.org/uploads/assets/f465d1b3-797b-4297-b7fedc00b4d77e51/Best-practice-recommendations-for-implementing-digital-pathology.pdf)
  • Ehteshami, Bejnordi, B., Mullooly, M., Pfeiffer, R.M., Fan, S., Vacek, P.M., Weaver, D.L., Herschorn, S., Brinton, L.A., van Ginneken, B., Karssemeijer, N., Beck, A.H., Gierach, G.L., van der Laak, J.A.W.M., Sherman, M.E., 2018. Using deep convolutional neural networks to identify and classify tumor‐associated stroma in diagnostic breast biopsies. Mod Pathol. 31, 1502–1512.
  • Griffin, J., Treanor, D., 2017. Digital pathology in clinical use:where are we now and what is holding us back? Histopathology. 70, 134-145.
  • Guan, Q., Wang, Y., Ping, B., Li, D., Du, J., Qin, Y., Lu, H., Wan, X., Xiang, J., 2019. Deep convolutional neural network VGG‐16 model for differential diagnosing of papillary thyroid carcinomas in cytological images: a pilot study. J Cancer. 10, 4876–4882.
  • Helin, H.O., Tuominen, V.J., Ylinen, O., Helin, H.J, Isola, J., 2016. Free digital image analysis software helps to resolve equivocal scores in HER2 immunohistochemistry. Virchows Arch. 468, 191–198.
  • Hipp, J., Bauer, T.W., Bui, M.M., Cornish, T.C., Glassy, E.F., Lloyd, M., McGee, R.S., Murphy, D., O’Neill, D.G., Parwani, A.V., Rampy, B.A., El-Sayed Salama, M., Waters, R., Westfall, K., 2017. Digital Pathology Resource Guide. College of American Pathologists. Version 7.0. Issue No: 2. ( https://documents.cap.org/documents/2017-digital-pathology-resource-guide-toc-v7.0.2.0.pdf )
  • Indu, M., Rathy, R., Binu, M.P., 2016. "Slide less pathology": Fairy tale or reality?. J Oral Maxillofac Pathol. 20, 284-288.
  • Jiang, Y., Yang, M., Wang, S., Li, X., Sun, Y., 2020. Emerging role of deep learning-based artificial intelligence in tumor pathology. Cancer Commun (Lond). 40, 154-166.
  • Kourou, K., Exarchos, T.P., Exarchos, K.P., Karamouzis, M.V., Fotiadis, D.I., 2015. Machine learning applications in cancer prognosis and prediction. Computational and structural biotechnology journal. 13, 8-17.
  • Marée, R., Rollus, L., Stévens, B., Hoyoux, R., Louppe, G., Vandaele, R., Begon, J.M., Kainz, P., Pierre, Geurts., Wehenkel, L., 2016. Collaborative analysis of multi-gigapixel imaging data using Cytomine. Bioinformatics. 32, 1395–1401.
  • Mukhopadhyay, S., Feldman, M. D., Abels, E., Ashfaq, R., Beltaıfa, S., Caccıabeve, N. G., Cathro, H. P., Cheng, L., Cooper, K., Dıckey, G. E., Gıll, R. M., Heaton, R. P., Jr., Kerstens, R., Lındberg, G. M., Malhotra, R. K., Mandell, J. W., Manlucu, E. D., Mılls, A. M., Mılls, S. E., Moskaluk, C. A., Nelıs, M., Patıl, D. T., Przybycın, C. G., Reynolds, J. P., Rubın, B. P., Saboorıan, M. H., Salıcru, M., Samols, M. A., Sturgıs, C. D., Turner, K. O., Wıck, M. R., Yoon, J. Y., Zhao, P. Taylor, C. R., 2018. Whole Slide Imaging Versus Microscopy for Primary Diagnosis in Surgical Pathology: A Multicenter Blinded Randomized Noninferiority Study of 1992 Cases (Pivotal Study). Am J Surg Pathol, 42, 39-52.
  • Pantanowitz, L., Dickinson, K., Evans, A.J., Hassell, L.A., Henricks, W.H., Lennerz, J.K., Lowe, A., Parwani, A.V., Riben, M., Smith, C.D., Tuthill, J.M., Weinstein, R.S., Wilbur, D.C., Krupinski, E.A., Bernard, J., 2014. American Telemedicine Association clinical guidelines for telepathology. J Pathol Inform, 5:39.
  • Randell, R., Ruddle, R.A., Thomas, R.G., Mello-Thoms, C., Treanor, D., 2014. Diagnosis of Major Cancer Resection Specimens With Virtual Slides: Impact of a Novel Digital Pathology Workstation. Hum Pathol. 45, 2101-2106.
  • Shaban, M., Khurram, S.A., Fraz, M.M., Alsubaie, N., Masood, I., Mushtaq, S., Hassan, M., Loya, A., Rajpoot, N.M., 2019. A Novel Digital Score for Abundance of Tumour Infiltrating Lymphocytes Predicts Disease Free Survival in Oral Squamous Cell Carcinoma. .Sci Rep. 9(1):13341.
  • Snead, D.R, Tsang, Y.W, Meskiri, A., Kimani, P.K., Crossman, R., Rajpoot, N.M., Blessing, E., Chen, K., Gopalakrishnan, K., Matthews, P., Momtahan, N., Read-Jones, S., Sah, S., Simmons, E., Sinha, B., Suortamo, S., Yeo, Y., El Daly, H., Cree, I.A., 2016. Validation of digital pathology imaging for primary histopathological diagnosis.Histopathology. 68, 1063-1072.
  • Stritt, M., Stalder, A.K., Vezzali, E., 2020. Orbit Image Analysis: An Open-Source Whole Slide Image Analysis Tool. PLoS Comput Biol. 16(2):e1007313.
  • Vu, Q.D., Graham, S., Kurc, T., To, M.N.N., Shaban, M., Qaiser, T., Koohbanani, N.A., Khurram, S.A., Kalpathy-Cramer, J., Zhao, T., Gupta, R., Kwak, J.T., Rajpoot, N., Saltz, J., Farahani, K., 2019. Methods for Segmentation and Classification of Digital Microscopy Tissue Images. Front Bioeng Biotechnol. 7:53.
  • Weinstein, R.S, Graham, A.R, Lian, F., Braunhut, B.L, Barker, G.R., Krupinski, E.A., Bhattacharyya, A.K., 2012. Reconciliation of diverse telepathology system designs. Historic issues and impli-cations for emerging markets and new applications. Acta Pathol. Microbiol. Immunol. Scand. 120, 256–275.
  • Weinstein, R.S., 1986. Prospects for telepathology. Hum Pathol 17, 433–434.
  • Weis, C.A., Kather, J.N., Melchers, S., Al-Ahmdi, H., Pollheimer, M.J., Langner, C., Gaiser, T., 2018. Automatic evaluation of tumor budding in immunohistochemically stained colorectal carcinomas and correlation to clinical outcome. Diagn Pathol. 13(1):64.
  • Williams, B. J., Dacosta, P., Goacher, E., Treanor, D., 2017. A systematic analysis of discordant diagnoses in digital pathology compared with light microscopy. Arch Pathol Lab Med. 141, 1712-1718.
  • Williams, B.J., Lee, J., Oien, K.A., Treanor, D., 2018. Digital pathology access and usage in the UK: results from a national survey on behalf of the National Cancer Research Institute's CM-Path initiative. J Clin Pathol. 71, 463-466.
  • Zarbo, R.J., D'Angelo, R., 2007. The Henry Ford production system: effective reduction of process defects and waste in surgical pathology. Am. J. Clin. Pathol. 128, 1015–1022.
  • Zarella, M.D., Bowman, D., Aeffner, F., Farahani, N., Xthona, A., Absar, S.F., Parwani, A., Bui, M., Hartman, D.J., 2019. A Practical Guide to Whole Slide Imaging: A White Paper From the Digital Pathology Association. Archives of pathology & laboratory medicine. 43, 222-234.

A New Dawn: The impact of digital technologies in oral and maxillofacial pathology

Year 2021, Volume: 38 Issue: 3s, 81 - 85, 09.05.2021

Merva Soluk Tekkeşin Syed Ali Khurram

Abstract

The rapid evolution of digital technology in all walks of the life is an important indicator that a different future is waiting for us. Technology has become a mainstay of daily life and is being increasingly used in education, research as well clinical activities with new innovations aiding healthcare and increase our knowledge, productivity and efficiency. There is no doubt that the healthcare sector, including dental sciences will be influenced by these rapid changes with many standard procedures likely to change. This is supported by the fact that some medical and dental specialties such as radiology have already made the digital leap. Digital pathology is an emerging area which has started to transform education and workflow in pathology. In this review, we will discuss how these novel and ‘disruptive’ technologies are likely to change education, training and diagnostic work flow in oral and maxillofacial pathology.

Keywords

Digital pathology oral and maxillofacial pathology image analysis machine learning artificial intelligence education

References

  • Araújo, A.L.D., Amaral-Silva, G.K., Fonseca, F.P., Palmier, N.R., Lopes, M.A., Speight,.P.M., de Almeida, O.P., Vargas, P.A., Santos-Silva, A.R., 2018. Validation of digital microscopy in the histopathological diagnoses of oral diseases. Virchows Arch. 473, 321-327.
  • Araújo, A.L.D., Arboleda, L.P.A., Palmier, N.R., Fonsêca, J.M., de Pauli Paglioni, M., Gomes-Silva, W., Ribeiro, A.C.P., Brandão, T.B, Simonato, L.E., Speight, P.M., Fonseca, F.P., Lopes, M.A., de Almeida, O.P., Vargas, P.A., Madrid Troconis, C.C., Santos-Silva, A.R., 2019. The performance of digital microscopy for primary diagnosis in human pathology: a systematic review. Virchows Arch. 474, 269-287.
  • Arvaniti, E., Fricker, K.S., Moret, M., Rupp, N., Hermanns, T., Fankhauser, C., Wey, N., Wild, P.J., Rüschoff, J.H., Claassen, M., 2018. Automated Gleason grading of prostate cancer tissue microarrays via deep learning. Sci Rep. 8(1):12054.
  • Bankhead, P., Loughrey, M.B., Fernández, J.A., Dombrowski, Y., McArt, D.G., Dunne, P.D., McQuaid, S., Gray, R.T., Murray, L.J., Coleman, H.G., James, J.A., Salto-Tellez, M., Hamilton, PW., 2017 Sci Rep. 7(1):16878.
  • Coudray, N., Ocampo, P.S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., Moreira, A.L., Razavian, N., Tsirigos, A., 2018. Classification and mutation prediction from non‐small cell lung cancer histopathology images using deep learning. Nat Med. 24, 1559–1567.
  • Cross, S., Furness, P., Igali, L., Snead, D., Treanor, D., 2018. Best practice recommendations for implementing digital pathology. The Royal College of Pathologists. (https://www.rcpath.org/uploads/assets/f465d1b3-797b-4297-b7fedc00b4d77e51/Best-practice-recommendations-for-implementing-digital-pathology.pdf)
  • Ehteshami, Bejnordi, B., Mullooly, M., Pfeiffer, R.M., Fan, S., Vacek, P.M., Weaver, D.L., Herschorn, S., Brinton, L.A., van Ginneken, B., Karssemeijer, N., Beck, A.H., Gierach, G.L., van der Laak, J.A.W.M., Sherman, M.E., 2018. Using deep convolutional neural networks to identify and classify tumor‐associated stroma in diagnostic breast biopsies. Mod Pathol. 31, 1502–1512.
  • Griffin, J., Treanor, D., 2017. Digital pathology in clinical use:where are we now and what is holding us back? Histopathology. 70, 134-145.
  • Guan, Q., Wang, Y., Ping, B., Li, D., Du, J., Qin, Y., Lu, H., Wan, X., Xiang, J., 2019. Deep convolutional neural network VGG‐16 model for differential diagnosing of papillary thyroid carcinomas in cytological images: a pilot study. J Cancer. 10, 4876–4882.
  • Helin, H.O., Tuominen, V.J., Ylinen, O., Helin, H.J, Isola, J., 2016. Free digital image analysis software helps to resolve equivocal scores in HER2 immunohistochemistry. Virchows Arch. 468, 191–198.
  • Hipp, J., Bauer, T.W., Bui, M.M., Cornish, T.C., Glassy, E.F., Lloyd, M., McGee, R.S., Murphy, D., O’Neill, D.G., Parwani, A.V., Rampy, B.A., El-Sayed Salama, M., Waters, R., Westfall, K., 2017. Digital Pathology Resource Guide. College of American Pathologists. Version 7.0. Issue No: 2. ( https://documents.cap.org/documents/2017-digital-pathology-resource-guide-toc-v7.0.2.0.pdf )
  • Indu, M., Rathy, R., Binu, M.P., 2016. "Slide less pathology": Fairy tale or reality?. J Oral Maxillofac Pathol. 20, 284-288.
  • Jiang, Y., Yang, M., Wang, S., Li, X., Sun, Y., 2020. Emerging role of deep learning-based artificial intelligence in tumor pathology. Cancer Commun (Lond). 40, 154-166.
  • Kourou, K., Exarchos, T.P., Exarchos, K.P., Karamouzis, M.V., Fotiadis, D.I., 2015. Machine learning applications in cancer prognosis and prediction. Computational and structural biotechnology journal. 13, 8-17.
  • Marée, R., Rollus, L., Stévens, B., Hoyoux, R., Louppe, G., Vandaele, R., Begon, J.M., Kainz, P., Pierre, Geurts., Wehenkel, L., 2016. Collaborative analysis of multi-gigapixel imaging data using Cytomine. Bioinformatics. 32, 1395–1401.
  • Mukhopadhyay, S., Feldman, M. D., Abels, E., Ashfaq, R., Beltaıfa, S., Caccıabeve, N. G., Cathro, H. P., Cheng, L., Cooper, K., Dıckey, G. E., Gıll, R. M., Heaton, R. P., Jr., Kerstens, R., Lındberg, G. M., Malhotra, R. K., Mandell, J. W., Manlucu, E. D., Mılls, A. M., Mılls, S. E., Moskaluk, C. A., Nelıs, M., Patıl, D. T., Przybycın, C. G., Reynolds, J. P., Rubın, B. P., Saboorıan, M. H., Salıcru, M., Samols, M. A., Sturgıs, C. D., Turner, K. O., Wıck, M. R., Yoon, J. Y., Zhao, P. Taylor, C. R., 2018. Whole Slide Imaging Versus Microscopy for Primary Diagnosis in Surgical Pathology: A Multicenter Blinded Randomized Noninferiority Study of 1992 Cases (Pivotal Study). Am J Surg Pathol, 42, 39-52.
  • Pantanowitz, L., Dickinson, K., Evans, A.J., Hassell, L.A., Henricks, W.H., Lennerz, J.K., Lowe, A., Parwani, A.V., Riben, M., Smith, C.D., Tuthill, J.M., Weinstein, R.S., Wilbur, D.C., Krupinski, E.A., Bernard, J., 2014. American Telemedicine Association clinical guidelines for telepathology. J Pathol Inform, 5:39.
  • Randell, R., Ruddle, R.A., Thomas, R.G., Mello-Thoms, C., Treanor, D., 2014. Diagnosis of Major Cancer Resection Specimens With Virtual Slides: Impact of a Novel Digital Pathology Workstation. Hum Pathol. 45, 2101-2106.
  • Shaban, M., Khurram, S.A., Fraz, M.M., Alsubaie, N., Masood, I., Mushtaq, S., Hassan, M., Loya, A., Rajpoot, N.M., 2019. A Novel Digital Score for Abundance of Tumour Infiltrating Lymphocytes Predicts Disease Free Survival in Oral Squamous Cell Carcinoma. .Sci Rep. 9(1):13341.
  • Snead, D.R, Tsang, Y.W, Meskiri, A., Kimani, P.K., Crossman, R., Rajpoot, N.M., Blessing, E., Chen, K., Gopalakrishnan, K., Matthews, P., Momtahan, N., Read-Jones, S., Sah, S., Simmons, E., Sinha, B., Suortamo, S., Yeo, Y., El Daly, H., Cree, I.A., 2016. Validation of digital pathology imaging for primary histopathological diagnosis.Histopathology. 68, 1063-1072.
  • Stritt, M., Stalder, A.K., Vezzali, E., 2020. Orbit Image Analysis: An Open-Source Whole Slide Image Analysis Tool. PLoS Comput Biol. 16(2):e1007313.
  • Vu, Q.D., Graham, S., Kurc, T., To, M.N.N., Shaban, M., Qaiser, T., Koohbanani, N.A., Khurram, S.A., Kalpathy-Cramer, J., Zhao, T., Gupta, R., Kwak, J.T., Rajpoot, N., Saltz, J., Farahani, K., 2019. Methods for Segmentation and Classification of Digital Microscopy Tissue Images. Front Bioeng Biotechnol. 7:53.
  • Weinstein, R.S, Graham, A.R, Lian, F., Braunhut, B.L, Barker, G.R., Krupinski, E.A., Bhattacharyya, A.K., 2012. Reconciliation of diverse telepathology system designs. Historic issues and impli-cations for emerging markets and new applications. Acta Pathol. Microbiol. Immunol. Scand. 120, 256–275.
  • Weinstein, R.S., 1986. Prospects for telepathology. Hum Pathol 17, 433–434.
  • Weis, C.A., Kather, J.N., Melchers, S., Al-Ahmdi, H., Pollheimer, M.J., Langner, C., Gaiser, T., 2018. Automatic evaluation of tumor budding in immunohistochemically stained colorectal carcinomas and correlation to clinical outcome. Diagn Pathol. 13(1):64.
  • Williams, B. J., Dacosta, P., Goacher, E., Treanor, D., 2017. A systematic analysis of discordant diagnoses in digital pathology compared with light microscopy. Arch Pathol Lab Med. 141, 1712-1718.
  • Williams, B.J., Lee, J., Oien, K.A., Treanor, D., 2018. Digital pathology access and usage in the UK: results from a national survey on behalf of the National Cancer Research Institute's CM-Path initiative. J Clin Pathol. 71, 463-466.
  • Zarbo, R.J., D'Angelo, R., 2007. The Henry Ford production system: effective reduction of process defects and waste in surgical pathology. Am. J. Clin. Pathol. 128, 1015–1022.
  • Zarella, M.D., Bowman, D., Aeffner, F., Farahani, N., Xthona, A., Absar, S.F., Parwani, A., Bui, M., Hartman, D.J., 2019. A Practical Guide to Whole Slide Imaging: A White Paper From the Digital Pathology Association. Archives of pathology & laboratory medicine. 43, 222-234.
There are 29 citations in total.

Details

Primary Language English
Subjects Health Care Administration
Journal Section Clinical Research
Authors

Merva Soluk Tekkeşin

Syed Ali Khurram This is me 0000-0002-0378-9380

Publication Date May 9, 2021
Submission Date May 26, 2020
Acceptance Date December 23, 2020
Published in Issue Year 2021 Volume: 38 Issue: 3s

Cite

APA Soluk Tekkeşin, M., & Khurram, S. A. (2021). A New Dawn: The impact of digital technologies in oral and maxillofacial pathology. Journal of Experimental and Clinical Medicine, 38(3s), 81-85.
AMA Soluk Tekkeşin M, Khurram SA. A New Dawn: The impact of digital technologies in oral and maxillofacial pathology. J. Exp. Clin. Med. May 2021;38(3s):81-85.
Chicago Soluk Tekkeşin, Merva, and Syed Ali Khurram. “A New Dawn: The Impact of Digital Technologies in Oral and Maxillofacial Pathology”. Journal of Experimental and Clinical Medicine 38, no. 3s (May 2021): 81-85.
EndNote Soluk Tekkeşin M, Khurram SA (May 1, 2021) A New Dawn: The impact of digital technologies in oral and maxillofacial pathology. Journal of Experimental and Clinical Medicine 38 3s 81–85.
IEEE M. Soluk Tekkeşin and S. A. Khurram, “A New Dawn: The impact of digital technologies in oral and maxillofacial pathology”, J. Exp. Clin. Med., vol. 38, no. 3s, pp. 81–85, 2021.
ISNAD Soluk Tekkeşin, Merva - Khurram, Syed Ali. “A New Dawn: The Impact of Digital Technologies in Oral and Maxillofacial Pathology”. Journal of Experimental and Clinical Medicine 38/3s (May 2021), 81-85.
JAMA Soluk Tekkeşin M, Khurram SA. A New Dawn: The impact of digital technologies in oral and maxillofacial pathology. J. Exp. Clin. Med. 2021;38:81–85.
MLA Soluk Tekkeşin, Merva and Syed Ali Khurram. “A New Dawn: The Impact of Digital Technologies in Oral and Maxillofacial Pathology”. Journal of Experimental and Clinical Medicine, vol. 38, no. 3s, 2021, pp. 81-85.
Vancouver Soluk Tekkeşin M, Khurram SA. A New Dawn: The impact of digital technologies in oral and maxillofacial pathology. J. Exp. Clin. Med. 2021;38(3s):81-5.
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