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Pathologists face a substantial increase in workload and complexity of histopathologic cancer diagnosis due to the advent of personalized medicine. Therefore, diagnostic protocols have to focus equally on efficiency and accuracy. In this paper we introduce 'deep learning' as a technique to improve the objectivity and efficiency of histopathologic slide analysis. Through two examples, prostate cancer identification in biopsy specimens and breast cancer metastasis detection in sentinel lymph nodes, we show the potential of this new methodology to reduce the workload for pathologists, while at the same time increasing objectivity of diagnoses. We found that all slides containing prostate cancer and micro- and macro-metastases of breast cancer could be identified automatically while 30-40% of the slides containing benign and normal tissue could be excluded without the use of any additional immunohistochemical markers or human intervention. We conclude that 'deep learning' holds great promise to improve the efficacy of prostate cancer diagnosis and breast cancer staging.

Titel:	Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis.
Autor/in / Beteiligte Person:	Litjens, G ; Sánchez, CI ; Timofeeva, N ; Hermsen, M ; Nagtegaal, I ; Kovacs, I ; Hulsbergen-van de Kaa, C ; Bult, P ; van Ginneken B ; van der Laak J
Link:	Full Text Finder (Volltext)
Zeitschrift:	Scientific reports, Jg. 6 (2016-05-23), S. 26286
Veröffentlichung:	London : Nature Publishing Group, copyright 2011-, 2016
Medientyp:	academicJournal
ISSN:	2045-2322 (electronic)
DOI:	10.1038/srep26286
Schlagwort:	Breast Neoplasms pathology Female Histological Techniques Humans Image Interpretation, Computer-Assisted methods Lymphatic Metastasis diagnosis Lymphatic Metastasis pathology Male Neoplasm Staging methods Neural Networks, Computer Prostatic Neoplasms pathology Sentinel Lymph Node Biopsy Breast Neoplasms diagnosis Diagnosis, Computer-Assisted methods Machine Learning Prostatic Neoplasms diagnosis
Sonstiges:	Nachgewiesen in: MEDLINE Sprachen: English Publication Type: Journal Article; Research Support, Non-U.S. Gov't Language: English [Sci Rep] 2016 May 23; Vol. 6, pp. 26286. <i>Date of Electronic Publication: </i>2016 May 23. MeSH Terms: Machine Learning* ; Breast Neoplasms / diagnosis ; Diagnosis, Computer-Assisted / methods ; Prostatic Neoplasms / *diagnosis ; Breast Neoplasms / pathology ; Female ; Histological Techniques ; Humans ; Image Interpretation, Computer-Assisted / methods ; Lymphatic Metastasis / diagnosis ; Lymphatic Metastasis / pathology ; Male ; Neoplasm Staging / methods ; Neural Networks, Computer ; Prostatic Neoplasms / pathology ; Sentinel Lymph Node Biopsy References: Am J Surg Pathol. 2005 Sep;29(9):1228-42. (PMID: 16096414) ; Cancer. 2006 Jan 1;106(1):4-16. (PMID: 16329134) ; J Am Coll Surg. 2009 Mar;208(3):333-40. (PMID: 19317993) ; N Engl J Med. 2009 Aug 13;361(7):653-63. (PMID: 19675329) ; J Natl Cancer Inst. 2010 Mar 17;102(6):410-25. (PMID: 20190185) ; Mod Pathol. 2010 May;23 Suppl 2:S26-32. (PMID: 20436499) ; IEEE Rev Biomed Eng. 2009;2:147-71. (PMID: 20671804) ; N Engl J Med. 2011 Feb 3;364(5):412-21. (PMID: 21247310) ; CSH Protoc. 2008 May 01;2008:pdb.prot4986. (PMID: 21356829) ; Eur Urol. 2012 Jun;61(6):1115-7; discussion 1117-8. (PMID: 22326676) ; Med Image Comput Comput Assist Interv. 2013;16(Pt 2):403-10. (PMID: 24579166) ; Med Image Comput Comput Assist Interv. 2013;16(Pt 2):411-8. (PMID: 24579167) ; Nature. 2015 May 28;521(7553):436-44. (PMID: 26017442) ; J Med Imaging (Bellingham). 2014 Oct;1(3):034003. (PMID: 26158062) ; AMIA Annu Symp Proc. 2015 Nov 05;2015:1899-908. (PMID: 26958289) ; Med Image Comput Comput Assist Interv. 2015 Oct;9351:383-390. (PMID: 27796013) ; Neurocomputing. 2016 May 26;191:214-223. (PMID: 28154470) ; Anticancer Res. 1998 Jan-Feb;18(1B):571-6. (PMID: 9568179) Entry Date(s): Date Created: 20160524 Date Completed: 20180402 Latest Revision: 20220410 Update Code: 20231215 PubMed Central ID: PMC4876324

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Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis.