Semiautomatic estimation of breast density with DM-Scan software

Abstract Objective To evaluate the reproducibility of the calculation of breast density with DM-Scan software, which is based on the semiautomatic segmentation of fibroglandular tissue, and to compare it with the reproducibility of estimation by visual inspection. Materials and methods The study inc...

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Published inRadiología (English ed.) Vol. 56; no. 5; pp. 429 - 434
Main Authors Martínez Gómez, I, Casals el Busto, M, Antón Guirao, J, Ruiz Perales, F, Llobet Azpitarte, R
Format Journal Article
LanguageEnglish
Published Elsevier España 01.09.2014
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Summary:Abstract Objective To evaluate the reproducibility of the calculation of breast density with DM-Scan software, which is based on the semiautomatic segmentation of fibroglandular tissue, and to compare it with the reproducibility of estimation by visual inspection. Materials and methods The study included 655 direct digital mammograms acquired using craniocaudal projections. Three experienced radiologists analyzed the density of the mammograms using DM-Scan, and the inter- and intra-observer agreements between pairs of radiologists for the Boyd and BI-RADS® scales were calculated using the intraclass correlation coefficient. The Kappa index was used to compare the inter- and intra-observer agreements with those obtained previously for visual inspection in the same set of images. Results For visual inspection, the mean inter-observer agreement was 0.876 (95% CI: 0.873–0.879) on the Boyd scale and 0.823 (95% CI: 0.818–0.829) on the BI-RADS® scale. The mean intra-observer agreement was 0.813 (95% CI: 0.796–0.829) on the Boyd scale and 0.770 (95% CI: 0.742–0.797) on the BI-RADS® scale. For DM-Scan, the mean inter- and intra-observer agreement was 0.92, considerably higher than the agreement for visual inspection. Conclusion The semiautomatic calculation of breast density using DM-Scan software is more reliable and reproducible than visual estimation and reduces the subjectivity and variability in determining breast density.
ISSN:2173-5107
2173-5107
DOI:10.1016/j.rxeng.2012.11.003