24 March 2016 Quantification of mammographic masking risk with volumetric breast density maps: how to select women for supplemental screening
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Abstract
The sensitivity of mammograms is low for women with dense breasts, since cancers may be masked by dense tissue. In this study, we investigated methods to identify women with density patterns associated with a high masking risk. Risk measures are derived from volumetric breast density maps. We used the last negative screening mammograms of 93 women who subsequently presented with an interval cancer (IC), and, as controls, 930 randomly selected normal screening exams from women without cancer. Volumetric breast density maps were computed from the mammograms, which provide the dense tissue thickness at each location. These were used to compute absolute and percentage glandular tissue volume. We modeled the masking risk for each pixel location using the absolute and percentage dense tissue thickness and we investigated the effect of taking the cancer location probability distribution (CLPD) into account. For each method, we selected cases with the highest masking measure (by thresholding) and computed the fraction of ICs as a function of the fraction of controls selected. The latter can be interpreted as the negative supplemental screening rate (NSSR). Between the models, when incorporating CLPD, no significant differences were found. In general, the methods performed better when CLPD was included. At higher NSSRs some of the investigated masking measures had a significantly higher performance than volumetric breast density. These measures may therefore serve as an alternative to identify women with a high risk for a masked cancer.
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Katharina Holland, Katharina Holland, Carla H. van Gils, Carla H. van Gils, Johanna OP Wanders, Johanna OP Wanders, Ritse M. Mann, Ritse M. Mann, Nico Karssemeijer, Nico Karssemeijer, "Quantification of mammographic masking risk with volumetric breast density maps: how to select women for supplemental screening", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97850I (24 March 2016); doi: 10.1117/12.2216810; https://doi.org/10.1117/12.2216810
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