@article{8608e8e5b20b4946b169862c372ecd6d,
title = "Automated histologic diagnosis of CNS tumors with machine learning",
abstract = "The discovery of a new mass involving the brain or spine typically prompts referral to a neurosurgeon to consider biopsy or surgical resection. Intraoperative decision-making depends significantly on the histologic diagnosis, which is often established when a small specimen is sent for immediate interpretation by a neuropathologist. Access to neuropathologists may be limited in resource-poor settings, which has prompted several groups to develop machine learning algorithms for automated interpretation. Most attempts have focused on fixed histopathology specimens, which do not apply in the intraoperative setting. The greatest potential for clinical impact probably lies in the automated diagnosis of intraoperative specimens. Successful future studies may use machine learning to automatically classify whole-slide intraoperative specimens among a wide array of potential diagnoses.",
keywords = "brain tumor, deep learning, frozen section, histopathology, intraoperative diagnosis, machine learning, neural networks, smear preparation, spine tumor, stimulated Raman histology",
author = "Khalsa, {Siri Sahib S.} and Hollon, {Todd C.} and Arjun Adapa and Esteban Urias and Sudharsan Srinivasan and Neil Jairath and Julianne Szczepanski and Peter Ouillette and Sandra Camelo-Piragua and Orringer, {Daniel A.}",
note = "Funding Information: This article was supported by grant funding awarded by the National Institute of Neurological Disorders and Stroke (NINDS) of the National Institutes of Health (NIH) under award number T32NS007222. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Funding Information: This article was supported by grant funding awarded by the National Institute of Neurological Disorders and Stroke (NINDS) of the National Institutes of Health (NIH) under award number T32NS007222. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. DA Orringe is an advisor and shareholder of Invenio Imaging, Inc., a company developing SRH microscopy systems. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. No writing assistance was utilized in the production of this manuscript. Publisher Copyright: {\textcopyright} 2020 Siri Sahib S Khalsa.",
year = "2020",
month = jun,
day = "1",
doi = "10.2217/cns-2020-0003",
language = "English (US)",
volume = "9",
pages = "48--54",
journal = "CNS oncology",
issn = "2045-0915",
publisher = "Future Medicine Ltd.",
number = "2",
}