Contextual analysis complements single-cell analysis in the diagnosis of breast cancer in fine needle aspirates

Image analysis techniques were used to characterize individual nuclei of cells and entire clusters of cells in hematoxylin-and-eosin-stained smears of fine needle aspirates of the breast to determine the ability of these techniques to distinguish benign from malignant cases. Analysis of the individual nuclear features showed significant differences in nuclear area, shape (bending energy), texture and integrated darkness between benign and malignant samples. Analysis of the clusters demonstrated that the benign clusters were fewer in number, more cellular (average gray level) and larger than malignant clusters. A statistical classifier was constructed to test the discriminatory accuracy for benign and malignant cases. Good discrimination was found for both the individual nuclei and the clusters when analyzed separately, although a few cases were misclassified by each type of analysis. When combined, the two classifiers achieved a completely accurate classification. This suggests the complementary nature of high-resolution single-cell analysis and the more global cluster analysis techniques.