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2019, Volume 35, Number 3, Page(s) 173-184
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DOI: 10.5146/tjpath.2018.01460 |
Intraoperative Consultations of Central Nervous System Tumors: A Review for Practicing Pathologists and Testing of an Algorithmic Approach |
Emel ÇAKIR1,2, Gülbin ORAN2, Gül Emek YÜKSEK2, Cristine DING2,3, Tarık TİHAN4 |
1Department of Pathology, Karadeniz Technical University Faculty of Medicine, TRABZON, TURKEY 2Department of, Visiting Scholar, University of California, SAN FRANCISCO, USA 3Department of, Tan Tock Seng Hospital, SINGAPORE 4Department of, Neuropathology Division, Department of Pathology University of California, SAN FRANCISCO, USA |
Keywords:
Algorithm, Central nervous system tumors, Frozen section, Intraoperative consultation, Surgical neuropathology |
Intraoperative consultations or frozen sections for central nervous system (CNS) tumors present a significant challenge for surgical pathologists
because of their relative rarity and diversity. Yet, such lesions are encountered by every surgical pathologist, and a basic understanding of clinical,
radiological and genetic information is critical to successfully evaluate CNS frozen sections. It is often beneficial to have a systematic approach
or an algorithm, and to be aware of the common pitfalls and mimickers when dealing with these lesions. We propose such an algorithm in an
effort to construct a sensible approach to CNS frozen sections that considers recent developments in the WHO CNS tumor classification. The
algorithm was developed for surgical pathologists who are occasionally faced with making diagnosis of CNS tumors on frozen sections. To test
the algorithm and its practicability, we selected a group of tumors among a total of 3288 consecutive intraoperative consultations performed at
UCSF between 2013 and 2017. The selected cases represented lesions that may be encountered in everyday surgical pathology and constituted
a fair reflection of the main group. The algorithm was used by three of the authors who did not have formal neuropathology training and had
been in surgical pathology practice for at least 3 years. There was a very high level of concordance among the authors’ diagnosis (interobserver
concordance: 0.83-0.97-kappa value) using the algorithm with high intraobserver reliability (concordance 93%, p<0.001). We suggest that an
algorithmic approach is an effective means for the surgical pathologists, and may help reach diagnosis during frozen sections.
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