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. 2017 Apr;162(3):541-548.
doi: 10.1007/s10549-017-4137-4. Epub 2017 Feb 4.

Quantification of masking risk in screening mammography with volumetric breast density maps

Katharina Holland  1 , Carla H van Gils  2 , Ritse M Mann  3 , Nico Karssemeijer  3
Affiliations

Quantification of masking risk in screening mammography with volumetric breast density maps

Katharina Holland et al. Breast Cancer Res Treat. 2017 Apr.

Abstract

Purpose: Fibroglandular tissue may mask breast cancers, thereby reducing the sensitivity of mammography. Here, we investigate methods for identification of women at high risk of a masked tumor, who could benefit from additional imaging.

Methods: The last negative screening mammograms of 111 women with interval cancer (IC) within 12 months after the examination and 1110 selected normal screening exams from women without cancer were used. From the mammograms, volumetric breast density maps were computed, which provide the dense tissue thickness for each pixel location. With these maps, three measurements were derived: (1) percent dense volume (PDV), (2) percent area where dense tissue thickness exceeds 1 cm (PDA), and (3) dense tissue masking model (DTMM). Breast density was scored by a breast radiologist using BI-RADS. Women with heterogeneously and extremely dense breasts were considered at high masking risk. For each masking measure, mammograms were divided into a high- and low-risk category such that the same proportion of the controls is at high masking risk as with BI-RADS.

Results: Of the women with IC, 66.1, 71.9, 69.2, and 63.0% were categorized to be at high masking risk with PDV, PDA, DTMM, and BI-RADS, respectively, against 38.5% of the controls. The proportion of IC at high masking risk is statistically significantly different between BI-RADS and PDA (p-value 0.022). Differences between BI-RADS and PDV, or BI-RADS and DTMM, are not statistically significant.

Conclusion: Measures based on density maps, and in particular PDA, are promising tools to identify women at high risk for a masked cancer.

Keywords: Breast cancer screening; Masking; Risk stratification; Supplemental screening; Volumetric breast density.

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Conflict of interest statement

Conflict of interest

K. Holland, C. van Gils, R. Mann, and N. Karssemeijer report all the same grant from the European Union’s Seventh Framework Programme (FP7), during the conduct of the study. C. van Gils also reports a personal grant from the Dutch Cancer Society, during the conduct of the study and a grant from Bayer Healthcare, and non-financial support from Volpara Health Technologies outside the submitted work. R. Mann reports grants, personal fees, and non-financial support from Siemens Healthcare and grants and personal fees from Bayer Healthcare outside the submitted work. R. Mann also reports a research contract with Seno Medical, and he reports to be a scientific advisor for ScreenPoint Medical (Nijmegen, NL) outside the submitted work. N. Karssemeijer reports to be one of the cofounders of Volpara Health Technologies Solutions, who develops and markets the breast density measurement software Volpara used in this study. In addition, N. Karssemeijer has shares of two other companies in the field of breast imaging. The two companies are Qview Medical (Los, Altos, CA) and ScreenPoint Medical (Nijmegen, NL).

Ethical standards

The authors declare that this study complies with the current laws in the Netherlands.

Figures

Fig. 1
Fig. 1
By thresholding the masking measures, cancers and controls were separated into high- and low-risk groups. The percentages of cancers and controls in the high-risk group are plotted against each other as function of the threshold
Fig. 2
Fig. 2
By thresholding the PDV measure, the cancers and controls were separated into a high- and a low-risk group. The figure shows the proportion of cancers and controls in the high-risk group as function of the PDV threshold
See this image and copyright information in PMC

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