Abstract
BackgroundGrading 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.MethodologyWe 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 FindingsWe 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.ConclusionsOur 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.
Highlights
Breast cancer is a highly heterogeneous disease characterized by several clinical and molecular variations [1,2,3]
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
Histopathological assessment of nuclear structure by brightfield microscopy following the staining of tissue sections with hematoxylin and eosin (H&E staining) remains the definitive clinical diagnostic approach to determine malignancy
Summary
Breast cancer is a highly heterogeneous disease characterized by several clinical and molecular variations [1,2,3]. Pathologists qualitatively assess features such as nuclear size, shape, nucleus-to-cytoplasm ratio, and chromatin texture. Factors such as focal plane selection, sample orientation, and the bisectioning of cells during sample preparation may bias the outcome of the diagnosis due to obscuration or incomplete feature detail. Grading schemes for breast cancer diagnosis are predominantly based on pathologists’ qualitative assessment of altered nuclear structure from 2D brightfield microscopy images. 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
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