Image analysis of low magnification images of fine needle aspirates of the breast produces useful discrimination between benign and malignant cases.

Abstract

Fine needle aspirates of the breast (FNAB) (n = 362; 204 malignant, 158 benign), prepared by cytocentrifuge methods and stained by the Papanicolaou technique, were analysed using a semi-automated image analysis system at a low magnification which precluded resolution of nuclear detail. The measured parameters were integrated optical density, fractal textural dimension, number of cellular objects (single cells and contiguous groups of cells), distance between cellular objects (mean, s.d., skewness and kurtosis), area of cellular objects (mean, s.d., skewness, kurtosis) and the nearest neighbour statistic. The cases were divided into a 200-case training set and a 162-case test set. Analysis was performed by logistic regression and the multi-layer Perceptron type of artificial neural network. Logistic regression and the neural network produced similar performances with a sensitivity of 82-83%, specificity 85% and a positive predictive value for a malignant result of 85%. A non-parametric analysis of all the predictor variables showed that all except the mean area of cellular objects and the s.d. of this measurement were significant discriminants (P < 0.05), but most were highly interrelated and this was reflected in the selection of only three predictor variables by forward and backward conditional logistic regression. This study shows that much diagnostic information is present in low power views of FNAB, and that image analysis could form the basis of a semi-automated decision-support aid.