Breast fat volume measurement using wide-bore 3 T MRI: comparison of traditional mammographic density evaluation with MRI density measurements using automatic segmentation

E Petridou; M Kibiro; C Gladwell; P Malcolm; A Toms; A Juette; M Borga; O Dahlqvist Leinhard; T Romu; B Kasmai; E Denton

doi:10.1016/j.crad.2017.02.014

Breast fat volume measurement using wide-bore 3 T MRI: comparison of traditional mammographic density evaluation with MRI density measurements using automatic segmentation

Clin Radiol. 2017 Jul;72(7):565-572. doi: 10.1016/j.crad.2017.02.014. Epub 2017 Mar 28.

Authors

E Petridou¹, M Kibiro¹, C Gladwell¹, P Malcolm², A Toms¹, A Juette¹, M Borga³, O Dahlqvist Leinhard⁴, T Romu⁵, B Kasmai¹, E Denton¹

Affiliations

¹ Department of Radiology, Norfolk and Norwich University Hospital, UK.
² Department of Radiology, Norfolk and Norwich University Hospital, UK. Electronic address: paul.malcolm@nnuh.nhs.uk.
³ Centre for Medical Image Science and Visualisation, Linköping University, Sweden; Department of Biomedical Engineering, Linköping University, Sweden; Advanced MR Analytics AB, Teknikringen 7, Linköping, Sweden.
⁴ Centre for Medical Image Science and Visualisation, Linköping University, Sweden; Department of Medical and Health Sciences, Linköping University, Sweden; Advanced MR Analytics AB, Teknikringen 7, Linköping, Sweden.
⁵ Centre for Medical Image Science and Visualisation, Linköping University, Sweden; Department of Biomedical Engineering, Linköping University, Sweden.

PMID: 28363661
DOI: 10.1016/j.crad.2017.02.014

Abstract

Aim: To compare magnetic resonance imaging (MRI)-derived breast density measurements using automatic segmentation algorithms with radiologist estimations using the Breast Imaging Reporting and Data Systems (BI-RADS) density classification.

Materials and methods: Forty women undergoing mammography and dynamic breast MRI as part of their clinical management were recruited. Fat-water separated MRI images derived from a two-point Dixon technique, phase-sensitive reconstruction, and atlas-based segmentation were obtained before and after intravenous contrast medium administration. Breast density was assessed using software from Advanced MR Analytics (AMRA), Linköping, Sweden, with results compared to the widely used four-quartile quantitative BI-RADS scale.

Results: The proportion of glandular tissue in the breast on MRI was derived from the AMRA sequence. The mean unenhanced breast density was 0.31±0.22 (mean±SD; left) and 0.29±0.21 (right). Mean breast density on post-contrast images was 0.32±0.19 (left) and 0.32±0.2 (right). There was "almost perfect" correlation between pre- and post-contrast breast density quantification: Spearman's correlation rho=0.98 (95% confidence intervals [CI]: 0.97-0.99; left) and rho=0.99 (95% CI: 0.98-0.99; right). The 95% limits of agreement were -0.11-0.08 (left) and -0.08-0.03 (right). Interobserver reliability for BI-RADS was "substantial": weighted Kappa k=0.8 (95% CI: 0.74-0.87). The Spearman correlation coefficient between BI-RADS and MRI breast density was rho=0.73 (95% CI: 0.60-0.82; left) and rho=0.75 (95% CI: 0.63-0.83; right) which was also "substantial".

Conclusion: The AMRA sequence provides a fully automated, reproducible, objective assessment of fibroglandular breast tissue proportion that correlates well with mammographic assessment of breast density with the added advantage of avoidance of ionising radiation.

Publication types

Comparative Study

MeSH terms

Adipose Tissue / anatomy & histology*
Adipose Tissue / diagnostic imaging*
Breast / anatomy & histology*
Breast / diagnostic imaging*
Breast Density*
Female
Humans
Magnetic Resonance Imaging* / methods
Mammography*
Organ Size
Prospective Studies