Table of Contents
ISRN Surgery
Volume 2012, Article ID 546721, 8 pages
http://dx.doi.org/10.5402/2012/546721
Clinical Study

Computerized Decision Support System for Intraoperative Analysis of Margin Status in Breast Conservation Therapy

1Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA
2Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA
3Department of Surgery, University of California San Diego, La Jolla, CA 92093, USA
4Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA 92093, USA
5Department of Pathology, University of California San Diego, La Jolla, CA 92093, USA

Received 6 September 2012; Accepted 8 October 2012

Academic Editors: M. Aurich and A. Polydorou

Copyright © 2012 Manuel E. Ruidíaz et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background. Breast conservation therapy (BCT) is the standard treatment for breast cancer; however, 32–63% of procedures have a positive margin leading to secondary procedures. The standard of care to evaluate surgical margins is based on permanent section. Imprint cytology (IC) has been used to evaluate surgical samples but is limited by excessive cauterization thus requiring experienced cytopathologist for interpretation. An automated image screening process has been developed to detect cancerous cells from IC on cauterized margins. Methods. IC was prospectively performed on margins during lumpectomy operations for breast cancer in addition to permanent section on 127 patients. An 8-slide training subset and 8-slide testing subset were culled. H&E IC automated analysis, based on linear discriminant analysis, was compared to manual pathologist interpretation. Results. The most important descriptors, from highest to lowest performance, are nucleus color (23%), cytoplasm color (15%), shape (12%), grey intensity (9%), and local area (5%). There was 100% agreement between automated and manual interpretation of IC slides. Conclusion. Although limited by IC sampling variability, an automated system for accurate IC cancer cell identification system is demonstrated, with high correlation to manual analysis, even in the face of cauterization effects which supplement permanent section analysis.