Meeting Abstract Stromal Filters in Automated Immunostain Scoring Kunal Patel, 1 Anthony Bui, 1,2 Greg Riedlinger, 1 and Yukako Yagi 1 1 Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA 2 Boston Children’s Hospital, 300 Longwood Avenue, Boston, MA 02115, USA Correspondence should be addressed to Kunal Patel; kpatel1182@gmail.com Received 2 September 2014; Accepted 2 September 2014 Copyright © 2014 Kunal Patel et al. Tis 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. Introduction KI-67 is a marker for cell proliferation which binds a nuclear antigen and is thus a prime antibody in immunohistochem- istry. Scoring methodologies derived from KI-67 immunos- taining have shown promise as predictors of lethality in a range of cancers [1]. Te most common scoring method is the labelling index, a ratio of KI-67 positive cells to the entire population [1]. Manual calculation of a labelling index can be a time- consuming process for pathologists, who count cells in a bright feld. Automated scoring algorithms and programs have been written to generate labelling indices, but they are nonspecifc with respect to cell type and most ofen skew results by including stromal cells in the index. Tis problem is particularly relevant in breast cancer, where tumors are ofen located in a feld of fbroadipose tissue. We have adapted algorithms for immunostain scoring and tested 2 stromal cell fltration algorithms which remove these cells based on their elongated nuclear morphology. Methods An automated scoring algorithm was adapted from Immuno- Ratio [2] and written in Python programming language. Within this scoring program, two methods of stromal fl- tration were tested. Te frst fltered cells below a threshold defned by the isoperimetric quotient of labeled cells, while the second used a shape property known as the Hu moment invariant. Te algorithm was applied to a dataset of breast cancer images, and results were correlated with nonstromal fltration and a pathologist score. Results Preliminary results show that algorithms with stromal fltra- tion correlate better with pathology results than nonfltered counterparts. Additionally, the Hu moment invariant is a bet- ter variable for stromal cell fltration due to its nonreliance on perimeter. Deviation of automatic scores from pathologist- scored results is directly correlated with the amount of stromal tissue in the image feld. Conclusions Stromal cell fltration is a promising technique in the devel- opment of automatic scoring algorithms. Additional methods should be explored to address the fltration of nonelongated stromal nuclei. With improvements in usability, it can be integrated into whole slide imaging systems in the near future. References [1] B. K. B. Hirata, J. M. M. Oda, R. L. Guembarovski, C. B. Ariza, C. E. C. D. Oliveira, and M. A. E. Watanabe, “Molecular markers for breast cancer: prediction on tumor behavior,” Disease Markers, vol. 2014, Article ID 513158, 12 pages, 2014. [2] V. J. Tuominen, S. Ruotoistenm¨ aki, A. Viitanen, M. Jumppanen, and J. Isola, “ImmunoRatio: a publicly available web application for quantitative image analysis of estrogen receptor (ER), progesterone receptor (PR), and Ki-67,” Breast Cancer Research, vol. 12, no. 4, article R56, 2010. Hindawi Publishing Corporation Analytical Cellular Pathology Volume 2014, Article ID 497426, 1 page http://dx.doi.org/10.1155/2014/497426