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Objective task-based assessment using physical phantoms,1 and virtual clinical trials (VCTs)2 are two useful approaches for evaluating new breast imaging technologies. Previous studies using these evaluation methods have suggested that lowering the radiation dose of mammograms would not lead to a significant change in the performance of the mass detection task.3 It has been speculated that decreases in the detectability of extended masses are mostly due to the level of anatomical structure commonly found in the breast, and less on the quantum noise of the system (and subsequently on the radiation dose level).4 However, these previous studies referred to above typically evaluate lesion detection performance using lobular mass models. The task of a breast radiologist in reading a mammogram is more complex than detection tasks usually modeled with physical phantom studies or VCTs.The radiologist typically searches throughout the breast for a suspicious lesion, and then determines a probability of malignancy for that lesion. This is also the task used for clinical reader studies designed to assess new technologies. Essentially this involves first a detection task which is then followed by a classification task. The classification of a malignant mass is sometimes dependent on the presence of high-frequency spiculations on the surface of the mass lesion. Visualization of mass spiculations are important because this suggests a higher chance to be malignant compared to a lobular mass. In this study, we use simulation studies to explore the use of a mass classification task to assess new breast technologies, instead of the often-used detection task. It is observed that unlike previous physical phantom and VCT experiments that assess detection of lobular masses, classification of spiculated masses does seem to be dependent on radiation dose level.
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