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Novel image analysis approach quantifies morphological characteristics of 3D breast culture acini with varying metastatic potentials.

McKeen Polizzotti L, Oztan B, Bjornsson CS, Shubert KR, Yener B, Plopper GE - J. Biomed. Biotechnol. (2012)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Biology, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.

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.

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Three-dimensional view of a typical nonmalignant cell line culture. Acinar structures are surrounded by the ECM proteins.
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fig1: Three-dimensional view of a typical nonmalignant cell line culture. Acinar structures are surrounded by the ECM proteins.

Mentions: Figure 1 shows a 3D view of a typical nonmalignant breast culture that comprises several acini that are surrounded by extracellular matrix (ECM) proteins. Figure 2 shows an enlarged view of a cross section from a typical nonmalignant acinus. Acinar structures in healthy/nonmalignant cultures include polarized luminal epithelial cells. (A polarized cell has specialized proteins localized to specific cell membranes such as the basal, lateral, or apical side) and a hollow lumen. The lateral membranes of neighboring cells are in close proximity due to cellular junctions and adhesion proteins. The basal side of cells contacts the surrounding ECM proteins, while the apical sides of cells face the hollow lumen. Malignant cancers result in loss of cell polarity that induces changes in the morphology of acinar structures. In this paper, 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. We propose novel features to characterize these changes along the metastatic cascade and exploit them in a supervised machine learning setting for the automated grading of breast cancer. Proposed features capture not only similar factors as the Scarff-Bloom-Richardson grading system but also additional subvisual changes observed in breast cancer progression in a quantitative manner to reduce variability. As shown by the grading accuracies, the proposed features efficiently capture the differences caused by the metastatic progression of the cancer.


Novel image analysis approach quantifies morphological characteristics of 3D breast culture acini with varying metastatic potentials.

McKeen Polizzotti L, Oztan B, Bjornsson CS, Shubert KR, Yener B, Plopper GE - J. Biomed. Biotechnol. (2012)

Three-dimensional view of a typical nonmalignant cell line culture. Acinar structures are surrounded by the ECM proteins.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3362088&req=5

fig1: Three-dimensional view of a typical nonmalignant cell line culture. Acinar structures are surrounded by the ECM proteins.
Mentions: Figure 1 shows a 3D view of a typical nonmalignant breast culture that comprises several acini that are surrounded by extracellular matrix (ECM) proteins. Figure 2 shows an enlarged view of a cross section from a typical nonmalignant acinus. Acinar structures in healthy/nonmalignant cultures include polarized luminal epithelial cells. (A polarized cell has specialized proteins localized to specific cell membranes such as the basal, lateral, or apical side) and a hollow lumen. The lateral membranes of neighboring cells are in close proximity due to cellular junctions and adhesion proteins. The basal side of cells contacts the surrounding ECM proteins, while the apical sides of cells face the hollow lumen. Malignant cancers result in loss of cell polarity that induces changes in the morphology of acinar structures. In this paper, 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. We propose novel features to characterize these changes along the metastatic cascade and exploit them in a supervised machine learning setting for the automated grading of breast cancer. Proposed features capture not only similar factors as the Scarff-Bloom-Richardson grading system but also additional subvisual changes observed in breast cancer progression in a quantitative manner to reduce variability. As shown by the grading accuracies, the proposed features efficiently capture the differences caused by the metastatic progression of the cancer.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Biology, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.

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.

Show MeSH
Related in: MedlinePlus