Literature DB >> 25559234

Mammographic breast density: comparison of methods for quantitative evaluation.

Oliver W E Morrish1, Lorraine Tucker, Richard Black, Paula Willsher, Stephen W Duffy, Fiona J Gilbert.   

Abstract

PURPOSE: To evaluate the results from two software tools for measurement of mammographic breast density and compare them with observer-based scores in a large cohort of women.
MATERIALS AND METHODS: Following written informed consent, a data set of 36 281 mammograms from 8867 women were collected from six United Kingdom centers in an ethically approved trial. Breast density was assessed by one of 26 readers on a visual analog scale and with two automated density tools. Mean differences were calculated as the mean of all the individual percentage differences between each measurement for each case (woman). Agreement in total breast volume, fibroglandular volume, and percentage density was assessed with the Bland-Altman method. Association with observer's scores was calculated by using the Pearson correlation coefficient (r).
RESULTS: Correlation between the Quantra and Volpara outputs for total breast volume was r = 0.97 (P < .001), with a mean difference of 43.5 cm(3) for all cases representing 5.0% of the mean total breast volume. Correlation of the two measures was lower for fibroglandular volume (r = 0.86, P < .001). The mean difference was 30.3 cm(3) for all cases representing 21.2% of the mean fibroglandular tissue volume result. Quantra gave the larger value and the difference tended to increase with volume. For the two measures of percentage volume density, the mean difference was 1.61 percentage points (r = 0.78, P < .001). Comparison of observer's scores with the area-based density given by Quantra yielded a low correlation (r = 0.55, P < .001). Correlations of observer's scores with the volumetric density results gave r values of 0.60 (P < .001) and 0.63 (P < .001) for Quantra and Volpara, respectively.
CONCLUSION: Automated techniques for measuring breast density show good correlation, but these are poorly correlated with observer's scores. However automated techniques do give different results that should be considered when informing patient personalized imaging. (©) RSNA, 2015 Clinical trial registration no. ISRCTN 73467396.

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Mesh:

Year:  2015        PMID: 25559234     DOI: 10.1148/radiol.14141508

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


  15 in total

1.  Quantitative evaluation of background parenchymal enhancement (BPE) on breast MRI. A feasibility study with a semi-automatic and automatic software compared to observer-based scores.

Authors:  Alberto Tagliafico; Bianca Bignotti; Giulio Tagliafico; Simona Tosto; Alessio Signori; Massimo Calabrese
Journal:  Br J Radiol       Date:  2015-10-14       Impact factor: 3.039

2.  Fully Automated Quantitative Estimation of Volumetric Breast Density from Digital Breast Tomosynthesis Images: Preliminary Results and Comparison with Digital Mammography and MR Imaging.

Authors:  Said Pertuz; Elizabeth S McDonald; Susan P Weinstein; Emily F Conant; Despina Kontos
Journal:  Radiology       Date:  2015-10-21       Impact factor: 11.105

3.  Using ultrasound tomography to identify the distributions of density throughout the breast.

Authors:  Mark Sak; Neb Duric; Peter Littrup; Mark E Sherman; Gretchen L Gierach
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2016-04

4.  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

5.  Breast parenchymal patterns in processed versus raw digital mammograms: A large population study toward assessing differences in quantitative measures across image representations.

Authors:  Aimilia Gastounioti; Andrew Oustimov; Brad M Keller; Lauren Pantalone; Meng-Kang Hsieh; Emily F Conant; Despina Kontos
Journal:  Med Phys       Date:  2016-11       Impact factor: 4.071

6.  Proton density water fraction as a reproducible MR-based measurement of breast density.

Authors:  Leah C Henze Bancroft; Roberta M Strigel; Erin B Macdonald; Colin Longhurst; Jacob Johnson; Diego Hernando; Scott B Reeder
Journal:  Magn Reson Med       Date:  2021-11-14       Impact factor: 4.668

7.  Inter- and intra-observer agreement of BI-RADS-based subjective visual estimation of amount of fibroglandular breast tissue with magnetic resonance imaging: comparison to automated quantitative assessment.

Authors:  G J Wengert; T H Helbich; R Woitek; P Kapetas; P Clauser; P A Baltzer; W-D Vogl; M Weber; A Meyer-Baese; Katja Pinker
Journal:  Eur Radiol       Date:  2016-04-23       Impact factor: 5.315

8.  Measuring mammographic density: comparing a fully automated volumetric assessment versus European radiologists' qualitative classification.

Authors:  Hanna Sartor; Kristina Lång; Aldana Rosso; Signe Borgquist; Sophia Zackrisson; Pontus Timberg
Journal:  Eur Radiol       Date:  2016-03-24       Impact factor: 5.315

9.  Comparing Visually Assessed BI-RADS Breast Density and Automated Volumetric Breast Density Software: A Cross-Sectional Study in a Breast Cancer Screening Setting.

Authors:  Daniëlle van der Waal; Gerard J den Heeten; Ruud M Pijnappel; Klaas H Schuur; Johanna M H Timmers; André L M Verbeek; Mireille J M Broeders
Journal:  PLoS One       Date:  2015-09-03       Impact factor: 3.240

10.  Mammographic density and breast cancer risk in breast screening assessment cases and women with a family history of breast cancer.

Authors:  Stephen W Duffy; Oliver W E Morrish; Prue C Allgood; Richard Black; Maureen G C Gillan; Paula Willsher; Julie Cooke; Karen A Duncan; Michael J Michell; Hilary M Dobson; Roberta Maroni; Yit Y Lim; Hema N Purushothaman; Tamara Suaris; Susan M Astley; Kenneth C Young; Lorraine Tucker; Fiona J Gilbert
Journal:  Eur J Cancer       Date:  2017-11-27       Impact factor: 9.162

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