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Frozen section analysis of margins for head and neck tumor resections: reduction of sampling errors with a third histologic level.

Olson SM,Hussaini M,Lewis JS

Abstract

Frozen section analysis is an essential tool for assessing margins intra-operatively to assure complete resection. Many institutions evaluate surgical defect edge tissue provided by the surgeon after the main lesion has been removed. With the increasing use of transoral laser microsurgery, this method is becoming even more prevalent. We sought to evaluate error rates at our large academic institution and to see if sampling errors could be reduced by the simple method change of taking an additional third section on these specimens. All head and neck tumor resection cases from January 2005 through August 2008 with margins evaluated by frozen section were identified by database search. These cases were analyzed by cutting two levels during frozen section and a third permanent section later. All resection cases from August 2008 through July 2009 were identified as well. These were analyzed by cutting three levels during frozen section (the third a 'much deeper' level) and a fourth permanent section later. Error rates for both of these periods were determined. Errors were separated into sampling and interpretation types. There were 4976 total frozen section specimens from 848 patients. The overall error rate was 2.4% for all frozen sections where just two levels were evaluated and was 2.5% when three levels were evaluated (P=0.67). The sampling error rate was 1.6% for two-level sectioning and 1.2% for three-level sectioning (P=0.42). However, when considering only the frozen section cases where tumor was ultimately identified (either at the time of frozen section or on permanent sections) the sampling error rate for two-level sectioning was 15.3 versus 7.4% for three-level sectioning. This difference was statistically significant (P=0.006). Cutting a single additional 'deeper' level at the time of frozen section identifies more tumor-bearing specimens and may reduce the number of sampling errors.

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