Literature DB >> 34519572

Fully Automated Volumetric Breast Density Estimation from Digital Breast Tomosynthesis.

Aimilia Gastounioti1, Lauren Pantalone1, Christopher G Scott1, Eric A Cohen1, Fang F Wu1, Stacey J Winham1, Matthew R Jensen1, Andrew D A Maidment1, Celine M Vachon1, Emily F Conant1, Despina Kontos1.   

Abstract

Background While digital breast tomosynthesis (DBT) is rapidly replacing digital mammography (DM) in breast cancer screening, the potential of DBT density measures for breast cancer risk assessment remains largely unexplored. Purpose To compare associations of breast density estimates from DBT and DM with breast cancer. Materials and Methods This retrospective case-control study used contralateral DM/DBT studies from women with unilateral breast cancer and age- and ethnicity-matched controls (September 19, 2011-January 6, 2015). Volumetric percent density (VPD%) was estimated from DBT using previously validated software. For comparison, the publicly available Laboratory for Individualized Breast Radiodensity Assessment software package, or LIBRA, was used to estimate area-based percent density (APD%) from raw and processed DM images. The commercial Quantra and Volpara software packages were applied to raw DM images to estimate VPD% with use of physics-based models. Density measures were compared by using Spearman correlation coefficients (r), and conditional logistic regression was performed to examine density associations (odds ratios [OR]) with breast cancer, adjusting for age and body mass index. Results A total of 132 women diagnosed with breast cancer (mean age ± standard deviation [SD], 60 years ± 11) and 528 controls (mean age, 60 years ± 11) were included. Moderate correlations between DBT and DM density measures (r = 0.32-0.75; all P < .001) were observed. Volumetric density estimates calculated from DBT (OR, 2.3 [95% CI: 1.6, 3.4] per SD for VPD%DBT) were more strongly associated with breast cancer than DM-derived density for both APD% (OR, 1.3 [95% CI: 0.9, 1.9] [P < .001] and 1.7 [95% CI: 1.2, 2.3] [P = .004] per SD for LIBRA raw and processed data, respectively) and VPD% (OR, 1.6 [95% CI: 1.1, 2.4] [P = .01] and 1.7 [95% CI: 1.2, 2.6] [P = .04] per SD for Volpara and Quantra, respectively). Conclusion The associations between quantitative breast density estimates and breast cancer risk are stronger for digital breast tomosynthesis compared with digital mammography. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Yaffe in this issue.

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Year:  2021        PMID: 34519572      PMCID: PMC8608738          DOI: 10.1148/radiol.2021210190

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  23 in total

Review 1.  A review of breast tomosynthesis. Part I. The image acquisition process.

Authors:  Ioannis Sechopoulos
Journal:  Med Phys       Date:  2013-01       Impact factor: 4.071

2.  Comparison of Clinical and Automated Breast Density Measurements: Implications for Risk Prediction and Supplemental Screening.

Authors:  Kathleen R Brandt; Christopher G Scott; Lin Ma; Amir P Mahmoudzadeh; Matthew R Jensen; Dana H Whaley; Fang Fang Wu; Serghei Malkov; Carrie B Hruska; Aaron D Norman; John Heine; John Shepherd; V Shane Pankratz; Karla Kerlikowske; Celine M Vachon
Journal:  Radiology       Date:  2015-12-22       Impact factor: 11.105

3.  Breast Cancer Risk Associations with Digital Mammographic Density by Pixel Brightness Threshold and Mammographic System.

Authors:  Tuong L Nguyen; Yoon-Ho Choi; Ye K Aung; Christopher F Evans; Nhut H Trinh; Shuai Li; Gillian S Dite; Myeong-Seong Kim; Patrick C Brennan; Mark A Jenkins; Joohon Sung; Yun-Mi Song; John L Hopper
Journal:  Radiology       Date:  2017-10-16       Impact factor: 11.105

4.  Agreement between Breast Percentage Density Estimations from Standard-Dose versus Synthetic Digital Mammograms: Results from a Large Screening Cohort Using Automated Measures.

Authors:  Emily F Conant; Brad M Keller; Lauren Pantalone; Aimilia Gastounioti; Elizabeth S McDonald; Despina Kontos
Journal:  Radiology       Date:  2017-01-25       Impact factor: 11.105

5.  Breast cancer screening with tomosynthesis (3D mammography) with acquired or synthetic 2D mammography compared with 2D mammography alone (STORM-2): a population-based prospective study.

Authors:  Daniela Bernardi; Petra Macaskill; Marco Pellegrini; Marvi Valentini; Carmine Fantò; Livio Ostillio; Paolina Tuttobene; Andrea Luparia; Nehmat Houssami
Journal:  Lancet Oncol       Date:  2016-06-23       Impact factor: 41.316

6.  Evaluation of LIBRA Software for Fully Automated Mammographic Density Assessment in Breast Cancer Risk Prediction.

Authors:  Aimilia Gastounioti; Christine Damases Kasi; Christopher G Scott; Kathleen R Brandt; Matthew R Jensen; Carrie B Hruska; Fang F Wu; Aaron D Norman; Emily F Conant; Stacey J Winham; Karla Kerlikowske; Despina Kontos; Celine M Vachon
Journal:  Radiology       Date:  2020-05-12       Impact factor: 11.105

7.  Effect of Mammographic Screening Modality on Breast Density Assessment: Digital Mammography versus Digital Breast Tomosynthesis.

Authors:  Aimilia Gastounioti; Anne Marie McCarthy; Lauren Pantalone; Marie Synnestvedt; Despina Kontos; Emily F Conant
Journal:  Radiology       Date:  2019-03-19       Impact factor: 29.146

Review 8.  Beyond breast density: a review on the advancing role of parenchymal texture analysis in breast cancer risk assessment.

Authors:  Aimilia Gastounioti; Emily F Conant; Despina Kontos
Journal:  Breast Cancer Res       Date:  2016-09-20       Impact factor: 6.466

9.  Automated volumetric breast density estimation out of digital breast tomosynthesis data: feasibility study of a new software version.

Authors:  Youichi Machida; Ai Saita; Hirofumi Namba; Eisuke Fukuma
Journal:  Springerplus       Date:  2016-06-18

10.  Impact of type of full-field digital image on mammographic density assessment and breast cancer risk estimation: a case-control study.

Authors:  Marta Cecilia Busana; Amanda Eng; Rachel Denholm; Mitch Dowsett; Sarah Vinnicombe; Steve Allen; Isabel Dos-Santos-Silva
Journal:  Breast Cancer Res       Date:  2016-09-26       Impact factor: 6.466

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  2 in total

Review 1.  Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review.

Authors:  Aimilia Gastounioti; Shyam Desai; Vinayak S Ahluwalia; Emily F Conant; Despina Kontos
Journal:  Breast Cancer Res       Date:  2022-02-20       Impact factor: 8.408

2.  Special Issue "New Advances in Breast Imaging".

Authors:  Daniele Ugo Tari
Journal:  Tomography       Date:  2022-06-28
  2 in total

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