Textural analysis in the diagnosis of benign and malignant breast cells.

Objective: To study the discriminatory capacity of textural variables to classify the nuclei of breast tumor cells as benign or malignant, using a statistical approach.

Study Design: Image analysis techniques were used to automatically segment nuclei of cells obtained by fine needle aspiration and Papanicolaou stained. The sample comprised 95 cases of malignant lesions and 47 cases of benign lesions (approximately 25 nuclei per case), and 27 textural variables were measured. Two methods were used to analyze the data: classification and regression trees (CART) and discriminant analysis.

Results: The variance in gray levels was the most decisive variable in the CART analysis, correctly classifying 57% and 97% of benign and malignant cases, respectively. Discriminant analysis yielded the best results, correctly classifying 79% and 85% of benign and malignant cases, respectively.

Conclusion: The classifier obtained by a statistical approach to the textural analysis of Papanicolaou-stained nuclei did not prove useful for diagnostic discrimination. Staining techniques that are not chromatin specific are highly variable, and other features have proven more effective with this type of staining.