Comparison of Standard and Vacuum Specimen Mammography in the Detection of Margin Status in Conservative Surgery for Breast Cancer: a Cross-Sectional Diagnostic Study

Abstract

Background: One of the most important factors that increases breast cancer (BC) recurrence after Breast-conserving surgery (BCS) is the positivity of the margins, which is found in permanent histological exams. Intra-operative specimen mammography (SM) can reduce the rate of margin positivity and re-operation. Our aim was to examine whether vacuum SM (VSM) is more accurate than standard SM (SSM) in detecting the positivity of the margins. Methods: For this cross-sectional study, in the operating room, excised specimens of 55 women with breast cancer who underwent lumpectomy were oriented by metallic staples and sutures for radiologic and histological assessment, respectively. In the radiology ward, SSM was first taken; then, the specimen was vacuum packed and VSM was performed. Afterwards, the specimen was sent for histopathological analysis as a gold standard for the assessment of surgical margins. Specimens’ margins were classified according to the size of clear margins in millimeters as zero or >0; and ≤1 or >1.   Results: The mean age of all participants was 51.22 ± 10.58 years. Totally, 220 margins were assessed. According to classified margins (zero and 1 mm), for the detection of affected margins, the accuracy values of the VSM method were 90.52%, and 87.20% while these figures were 91.51% and 88.68% for SSM. There was substantial agreement between the two methods of detecting the affected margins (VSM and SSM), with Cohen's κ =0.66, 95% CI: 0.34-0.97, P-value <0.001). Finally, there was not a statistically significant difference in the proportion of detecting margin between SSM and VSM (McNemar test P-value =0.63). Conclusion: Specimen mammography with an adequate orientation of the tissue is an accurate and practical method for immediate intraoperative examination of the margin status in BCS, and VSM is not superior to SSM in the detection of affected margins.