Breast Tissue Density Influences Tumor Malignancy Perception and Decisions in Mammography

Abstract

High density breast tissue has been found to reduce radiologists’ accuracy in detecting and classifying mammogram abnormalities. The current research examines the perceptual and decisional components that underlie diagnostic classification (independent of detection) in a sample of novices. Mammograms were varied along two dimensions: Breast tissue density (low/high) and the nature of an identified mass (benign/malignant). In two experiments, participants learned to classify images into 4 categories created by factorial combination of these dimensions. In low density tissue, accuracy was higher for benign than for malignant masses. Surprisingly, and in contrast to the mammography literature, accuracy for malignant masses was higher in high-density than in low-density tissue. Cognitive modeling based in general recognition theory (GRT) indicated that low-density/benign category accuracy was largely due to high perceptual discriminability for that category. The high accuracy level for malignant masses in high density tissue was accounted for by decision bound slopes that favored the “malignant” response for items in the high-density malignant category. Because GRT can provide insight into perceptual and decisional determinants of observed classification accuracy, as well as individual-difference level parameters about attention allocation, GRT may be a means to obtain a more detailed understanding of diagnostic classification of complex naturalistic stimuli.