Isotropic 3D nuclear morphometry of normal, fibrocystic and malignant breast epithelial cells reveals new structural alterations

Vivek Nandakumar, Laimonas Kelbauskas, Kathryn F. Hernandez, Kelly M. Lintecum, Patti Senechal, Kimberly J. Bussey, Paul Davies, Roger H. Johnson, Deirdre Meldrum

Research output: Contribution to journalArticle

37 Citations (Scopus)

Abstract

Background: Grading schemes for breast cancer diagnosis are predominantly based on pathologists' qualitative assessment of altered nuclear structure from 2D brightfield microscopy images. However, cells are three-dimensional (3D) objects with features that are inherently 3D and thus poorly characterized in 2D. Our goal is to quantitatively characterize nuclear structure in 3D, assess its variation with malignancy, and investigate whether such variation correlates with standard nuclear grading criteria. Methodology: We applied micro-optical computed tomographic imaging and automated 3D nuclear morphometry to quantify and compare morphological variations between human cell lines derived from normal, benign fibrocystic or malignant breast epithelium. To reproduce the appearance and contrast in clinical cytopathology images, we stained cells with hematoxylin and eosin and obtained 3D images of 150 individual stained cells of each cell type at sub-micron, isotropic resolution. Applying volumetric image analyses, we computed 42 3D morphological and textural descriptors of cellular and nuclear structure. Principal Findings: We observed four distinct nuclear shape categories, the predominant being a mushroom cap shape. Cell and nuclear volumes increased from normal to fibrocystic to metastatic type, but there was little difference in the volume ratio of nucleus to cytoplasm (N/C ratio) between the lines. Abnormal cell nuclei had more nucleoli, markedly higher density and clumpier chromatin organization compared to normal. Nuclei of non-tumorigenic, fibrocystic cells exhibited larger textural variations than metastatic cell nuclei. At p<0.0025 by ANOVA and Kruskal-Wallis tests, 90% of our computed descriptors statistically differentiated control from abnormal cell populations, but only 69% of these features statistically differentiated the fibrocystic from the metastatic cell populations. Conclusions: Our results provide a new perspective on nuclear structure variations associated with malignancy and point to the value of automated quantitative 3D nuclear morphometry as an objective tool to enable development of sensitive and specific nuclear grade classification in breast cancer diagnosis.

Original languageEnglish (US)
Article numbere29230
JournalPLoS One
Volume7
Issue number1
DOIs
StatePublished - Jan 5 2012

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morphometry
breasts
Breast
epithelial cells
Epithelial Cells
Cells
cells
Cell Nucleus
cell nucleus
breast neoplasms
Hematoxylin
Cell Nucleus Size
Eosine Yellowish-(YS)
Analysis of variance (ANOVA)
Breast Neoplasms
Chromatin
Microscopic examination
Agaricales
Cellular Structures
human cell lines

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

Cite this

Isotropic 3D nuclear morphometry of normal, fibrocystic and malignant breast epithelial cells reveals new structural alterations. / Nandakumar, Vivek; Kelbauskas, Laimonas; Hernandez, Kathryn F.; Lintecum, Kelly M.; Senechal, Patti; Bussey, Kimberly J.; Davies, Paul; Johnson, Roger H.; Meldrum, Deirdre.

In: PLoS One, Vol. 7, No. 1, e29230, 05.01.2012.

Research output: Contribution to journalArticle

Nandakumar, Vivek ; Kelbauskas, Laimonas ; Hernandez, Kathryn F. ; Lintecum, Kelly M. ; Senechal, Patti ; Bussey, Kimberly J. ; Davies, Paul ; Johnson, Roger H. ; Meldrum, Deirdre. / Isotropic 3D nuclear morphometry of normal, fibrocystic and malignant breast epithelial cells reveals new structural alterations. In: PLoS One. 2012 ; Vol. 7, No. 1.
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abstract = "Background: Grading schemes for breast cancer diagnosis are predominantly based on pathologists' qualitative assessment of altered nuclear structure from 2D brightfield microscopy images. However, cells are three-dimensional (3D) objects with features that are inherently 3D and thus poorly characterized in 2D. Our goal is to quantitatively characterize nuclear structure in 3D, assess its variation with malignancy, and investigate whether such variation correlates with standard nuclear grading criteria. Methodology: We applied micro-optical computed tomographic imaging and automated 3D nuclear morphometry to quantify and compare morphological variations between human cell lines derived from normal, benign fibrocystic or malignant breast epithelium. To reproduce the appearance and contrast in clinical cytopathology images, we stained cells with hematoxylin and eosin and obtained 3D images of 150 individual stained cells of each cell type at sub-micron, isotropic resolution. Applying volumetric image analyses, we computed 42 3D morphological and textural descriptors of cellular and nuclear structure. Principal Findings: We observed four distinct nuclear shape categories, the predominant being a mushroom cap shape. Cell and nuclear volumes increased from normal to fibrocystic to metastatic type, but there was little difference in the volume ratio of nucleus to cytoplasm (N/C ratio) between the lines. Abnormal cell nuclei had more nucleoli, markedly higher density and clumpier chromatin organization compared to normal. Nuclei of non-tumorigenic, fibrocystic cells exhibited larger textural variations than metastatic cell nuclei. At p<0.0025 by ANOVA and Kruskal-Wallis tests, 90{\%} of our computed descriptors statistically differentiated control from abnormal cell populations, but only 69{\%} of these features statistically differentiated the fibrocystic from the metastatic cell populations. Conclusions: Our results provide a new perspective on nuclear structure variations associated with malignancy and point to the value of automated quantitative 3D nuclear morphometry as an objective tool to enable development of sensitive and specific nuclear grade classification in breast cancer diagnosis.",
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