ArticleSpringerPlus2016
Automated volumetric breast density estimation out of digital breast tomosynthesis data: feasibility study of a new software version.
Article in SpringerPlus, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Validity of computed mean compressed fibroglandular tissue thickness and breast composition for stratification of masking risk in Japanese women.Breast cancer (Tokyo, Japan) · 2023Article
- Fully Automated Volumetric Breast Density Estimation from Digital Breast Tomosynthesis.Radiology · 2021Article
- Automated volumetric breast density measures: differential change between breasts in women with and without breast cancer.Breast cancer research : BCR · 2019Article
- Volumetric breast density measurement for personalized screening: accuracy, reproducibility, consistency, and agreement with visual assessment.Journal of medical imaging (Bellingham, Wash.) · 2019Article
- Breast density implications and supplemental screening.European radiology · 2019Review
- Prediction of glandularity and breast radiation dose from mammography results in Japanese women.Medical & biological engineering & computing · 2019Article
- Comparison between software volumetric breast density estimates in breast tomosynthesis and digital mammography images in a large public screening cohort.European radiology · 2019Article
- Automated and Clinical Breast Imaging Reporting and Data System Density Measures Predict Risk for Screen-Detected and Interval Cancers: A Case-Control Study.Annals of internal medicine · 2018Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA new software version of VolparaDensity (Volpara Algorithm version 1.5.1) is capable of calculating volumetric breast density (VBD) using either full-field digital mammography (FFDM) or digital breast tomosynthesis (DBT) images. In this preliminary study, we evaluated the feasibility and consistency of this new automated software.
findingsRaw data from both DBT and FFDM were acquired from women breast cancer screening at our institution between April and August 2015 using. The DBT and FFDM images obtained under a single compression were collected and VBD was measured using fully automated software. A paired t test was used to analyze differences in the VBD calculated from paired FFDM and DBT images. The correlation coefficient (R value) was calculated and p < 0.05 was considered significant. Dualmodality images were acquired in 160 women; VBD data were available for all but one. There was a significant difference in the VBD of individual breasts calculated from DBT and FFDM and when data were compared per case (<0.001 and p = 0.006, respectively). There were very good to excellent correlations between data from FFDM and from DBT (R = 0.78, p < 0.0001; per breast, R = 0.89, p < 0.0001, per case, R = 0.91, p < 0.0001).
conclusionsVBD from DBT was well correlated to that from FFDM, though significant differences were observed between the two.
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