A Simulation Study on Spectral Lesion Characterization

Abstract

Solitary, well-defined lesions are a common mammographic finding contributing more than 20 % of overall screening recalls. Discrimination of cystic from solid breast lesions therefore has the potential to reduce unnecessary recalls in mammography screening. A pre-clinical study, measuring the energy-dependent X-ray attenuation of tissue specimen and cystic fluid, revealed a measurable difference of the photon detection rate in the two energy bins of an energy-resolving photon-counting mammography system for these two tissue types. Based on these differences, a spectral lesion characterization algorithm has been developed, which estimates the lesion composition from spectral measurements in a lesion and a lesion-free reference region.

In this work, we present a simulation study to estimate the dependence of this lesion characterization algorithm on various types of uncertainties including the biological variation of cyst fluid and tumor tissue, variations in the mammographic background texture, and errors in the spectral measurements. The simulation study uses the receiver operating characteristics (ROC) for the task of identifying solid lesions (‘positive result’) to predict an expected area under the curve (AUC) and the specificity at the 99 % sensitivity level for a simulated screening population. The results of this simulation study are compared to those of a recently published pilot study.