Hindawi Publishing Corporation Journal of Biomedicine and Biotechnology Volume 2012, Article ID 102036, 16 pages doi:10.1155/2012/102036 Research Article Novel Image Analysis Approach Quantifies Morphological Characteristics of 3D Breast Culture Acini with Varying Metastatic Potentials Lindsey McKeen Polizzotti, 1 Basak Oztan, 2 Chris S. Bjornsson, 1 Katherine R. Shubert, 1 B¨ ulent Yener, 2 and George E. Plopper 1 1 Department of Biology, Rensselaer Polytechnic Institute, Troy, NY 12180, USA 2 Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY 12180, USA Correspondence should be addressed to Basak Oztan, oztanb@cs.rpi.edu Received 1 November 2011; Revised 21 February 2012; Accepted 22 February 2012 Academic Editor: James Sherley Copyright © 2012 Lindsey McKeen Polizzotti 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. Prognosis of breast cancer is primarily predicted by the histological grading of the tumor, where pathologists manually evaluate microscopic characteristics of the tissue. This labor intensive process suffers from intra- and inter-observer variations; thus, computer-aided systems that accomplish this assessment automatically are in high demand. We address this by developing an image analysis framework for the automated grading of breast cancer in in vitro three-dimensional breast epithelial acini through the characterization of acinar structure morphology. A set of statistically significant features for the characterization of acini morphology are exploited for the automated grading of six (MCF10 series) cell line cultures mimicking three grades of breast cancer along the metastatic cascade. In addition to capturing both expected and visually differentiable changes, we quantify subtle differences that pose a challenge to assess through microscopic inspection. Our method achieves 89.0% accuracy in grading the acinar structures as nonmalignant, noninvasive carcinoma, and invasive carcinoma grades. We further demonstrate that the proposed methodology can be successfully applied for the grading of in vivo tissue samples albeit with additional constraints. These results indicate that the proposed features can be used to describe the relationship between the acini morphology and cellular function along the metastatic cascade. 1. Introduction Breast cancer is the second most common cancer in women and is also the second leading cause of cancer-related death in women [1]. In its most common form, the tumor arises from the epithelial cells in the breast tissue. Histological grading systems are commonly used to predict the prog- nosis of tumors. The most frequently used tumor grading system for breast cancer is the modified Scarrf-Bloom-Rich- ardson method [2] where pathologists analyze the rate of cell division, percentage of tumor forming ducts, and the uniformity of cell nuclei to determine the cancer grade in H&E stained biopsies. While precancerous (or lower- grade) tumors tend to grow slowly and are less likely to spread, invasive (or higher-grade) tumors typically gain the ability to proliferate and spread rapidly. Subjectivity and variability of the results affect the accuracy of prognosis and subsequent patient treatment. A recent study indicates that the rate of misdiagnosis of breast cancer varies widely between clinicians and is nearly 40% in some cases [3]. Thus, there is an unmet need for robust methods that reduce the variability and subjectivity in the grading of breast tumors and lesions. Development of quantitative tools for image analysis and classification is rapidly expanding fields that constitute a great potential for improving diagnostic accuracy [4, 5]. In this paper, a method for automated grading of breast cancer in three-dimensional (3D) epithelial cell cultures is presented. In vitro epithelial breast cells cultured in laminin rich extracellular matrix form acinar-like structures that