Abstract
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.
Highlights
Breast cancer is the second most common cancer in women and is the second leading cause of cancer-related death in women [1]
We investigate morphological characteristics of mammary acinar structures in nonmalignant, noninvasive carcinoma, and invasive carcinoma cancer grades in six MCF10 series of cell lines grown in 3D cultures
After evaluating the metastatic potentials of these six cell lines, we considered the 10A and AT cell lines as nonmalignant, KCL and DCIS cell lines as noninvasive carcinoma, and CA1H and CA1A cell lines as invasive carcinoma that constituted the three grades of breast cancer we considered in this study
Summary
Breast cancer is the second most common cancer in women and is the second leading cause of cancer-related death in women [1]. In vitro epithelial breast cells cultured in laminin rich extracellular matrix form acinar-like structures that Both morphologically and structurally resemble in vivo acini of breast glands and lobules [6]. As shown by the grading accuracies, the proposed features efficiently capture the differences caused by the metastatic progression of the cancer Previous work on this problem includes examining the change in the morphological characteristics of nontumorigenic MCF10A epithelial acini over time and exploiting them to model the growth of culture over time. Features, Tang et al utilized features like acinus volume, density, sphericity, and epithelial thickness to investigate the relationship between acinus morphology and apoptosis, proliferation, and polarization [12] They built a computational model that can predict the growth of acini over a 12-day period. We perform a preliminary study on the grading of in vivo tissue section using our framework and demonstrate that the proposed features can be used on in vivo tissue slides albeit with additional constraints on the preparation of the tissue for our analysis
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