Imprint cytology versus frozen section analysis for intraoperative assessment of sentinel lymph node in breast cancer

INTRODUCTION: Sentinel lymph node (SLN) biopsy is the gold standard for surgical staging of the axilla in breast cancer (BC). Frozen section (FS) remains the most popular means of intraoperative SLN diagnosis. Imprint cytology (IC) has also been suggested as a less expensive and equally accurate alternative to FS. The aim of our study was to perform a direct comparison between IC and FS on the same SLNs of BC cases operated in a single center by the same surgical team. MATERIALS AND METHODS: Into this prospective study we enrolled 60 consecutive patients with histologically proven T1–T3 BC and clinically negative axilla. Sentinel nodes were detected using a standard protocol. The SLN(s) was always assessed by IC as well as FS analysis and immunohistochemistry. Nevertheless, all intraoperative decisions were based on FS analysis. RESULTS: During the study period 60 patients with invasive BC were registered, with 80 SLNs harvested. Mean number of SLN(s) identified for each patient was 1.33. The sensitivity and specificity were 90% and 100%, respectively, for IC, and 80% and 100% for FS. Relevant positive/negative predictive values were 100%/98% for IC and 100%/96.15%, respectively, for FS. Overall accuracy was 98% for IC and 97% for FS. Therefore, statistically significant difference between the two methods in the detection of positive nodes was not elucidated (p=1.000). CONCLUSIONS: IC appeared to be marginally more sensitive than FS in detecting SLN metastatic activity. Overall accuracy was 98.75%. With regard to the primary lesion characteristics, we conclude that initial lesion size and lymphovascular invasion play a pivotal role in metastatic involvement of the SLN with the dimensions of metastasis bearing no correlation with tumor size. Therefore, IC appears to be a sensitive and accurate method for the intraoperative assessment of SLN in BC patients, but further studies are required to confirm this interesting data.