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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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