Fully automatic quantification of fibroglandular tissue and background parenchymal enhancement with accurate implementation for axial and sagittal breast MRI protocols

Background Motion artifacts can reduce image quality of breast magnetic resonance imaging (MRI). There is a lack of data regarding their effect on diagnostic estimates. Purpose To evaluate factors that potentially influence readers’ diagnostic estimates in breast MRI: motion artifacts; amount of fibroglandular tissue; background parenchymal enhancement; lesion size; and lesion type. Material and Methods This Institutional Review Board-approved, retrospective, cross-sectional, single-center study included 320 patients (mean age = 55.1 years) with 334 histologically verified breast lesions (139 benign, 195 malignant) who underwent breast MRI. Two expert breast radiologists evaluated the images considering: motion artifacts (1 = minimal to 4 = marked); fibroglandular tissue (BI-RADS FGT); background parenchymal enhancement (BI-RADS BPE); lesion size; lesion type; and BI-RADS score. Univariate (Chi-square) and multivariate (Generalized Estimation Equations [GEE]) statistics were used to identify factors influencing sensitivity, specificity, and accuracy. Results Lesions were: 230 mass (68.9%) and 59 non-mass (17.7%), no foci. Forty-five lesions (13.5%) did not enhance in MRI but were suspicious or unclear in conventional imaging. Sensitivity, specificity, and accuracy were 93.8%, 83.4%, and 89.8% for Reader 1 and 95.4%, 87.8%, and 91.9% for Reader 2. Lower sensitivity was observed in case of increased motion artifacts ( P = 0.007), non-mass lesions ( P < 0.001), and small lesions ≤ 10 mm ( P < 0.021). No further factors (e.g. BPE, FGT) significantly influenced diagnostic estimates. At multivariate analysis, lesion type and size were retained as independent factors influencing the diagnostic performance ( P < 0.033). Conclusion Motion artifacts can impair lesion characterization with breast MRI, but lesion type and small size have the strongest influence on diagnostic estimates.

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Background Parenchymal Enhancement and Fibroglandular Tissue Proportion on Breast MRI: Correlation with Hormone Receptor Expression and Molecular Subtypes of Breast Cancer

Journal of Breast Health ◽

10.5152/tjbh.2016.3247 ◽

2017 ◽

Vol 13 (1) ◽

pp. 27-33 ◽

Cited By ~ 5

Author(s):

Mesut Ozturk ◽

Ahmet Veysel Polat ◽

Yurdanur Sullu ◽

Leman Tomak ◽

Ayfer Kamali Polat

Keyword(s):

Breast Cancer ◽

Hormone Receptor ◽

Molecular Subtypes ◽

Breast Mri ◽

Receptor Expression ◽

Fibroglandular Tissue ◽

Background Parenchymal Enhancement ◽

Hormone Receptor Expression ◽

Tissue Proportion ◽

Parenchymal Enhancement

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The Impact of Bilateral Salpingo-Oophorectomy on Breast MRI Background Parenchymal Enhancement and Fibroglandular Tissue

European Radiology ◽

10.1007/s00330-013-2993-9 ◽

2013 ◽

Vol 24 (1) ◽

pp. 162-168 ◽

Cited By ~ 23

Author(s):

E. R. Price ◽

J. D. Brooks ◽

E. J. Watson ◽

S. B. Brennan ◽

E. A. Comen ◽

...

Keyword(s):

Breast Mri ◽

Fibroglandular Tissue ◽

Background Parenchymal Enhancement ◽

The Impact ◽

Parenchymal Enhancement

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P2-08-01: Impact of Aromatase Inhibitors on Background Parenchymal Enhancement and Amount of Fibroglandular Tissue on Breast MRI.

10.1158/0008-5472.sabcs11-p2-08-01 ◽

2011 ◽

Author(s):

SB Goldfarb ◽

V King ◽

J Sung ◽

M Pike ◽

B Nulsen ◽

...

Keyword(s):

Aromatase Inhibitors ◽

Breast Mri ◽

Fibroglandular Tissue ◽

Background Parenchymal Enhancement ◽

Parenchymal Enhancement

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Fully Automated Convolutional Neural Network Method for Quantification of Breast MRI Fibroglandular Tissue and Background Parenchymal Enhancement

Journal of Digital Imaging ◽

10.1007/s10278-018-0114-7 ◽

2018 ◽

Vol 32 (1) ◽

pp. 141-147 ◽

Cited By ~ 5

Author(s):

Richard Ha ◽

Peter Chang ◽

Eralda Mema ◽

Simukayi Mutasa ◽

Jenika Karcich ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Breast Mri ◽

Neural Network Method ◽

Fibroglandular Tissue ◽

Background Parenchymal Enhancement ◽

Network Method ◽

Parenchymal Enhancement

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Comparison of Radiologist-Assigned Categories and Quantitative Measures of Background Parenchymal Enhancement on Breast MRI

10.1101/2020.03.09.20031153 ◽

2020 ◽

Author(s):

Bethany L Niell ◽

Mahmoud Abdalah ◽

Olya Stringfield ◽

Malesa Pereira ◽

Dana Ataya ◽

...

Keyword(s):

Active Contours ◽

Breast Mri ◽

Automated Segmentation ◽

Post Contrast ◽

Fibroglandular Tissue ◽

Background Parenchymal Enhancement ◽

Otsu’S Method ◽

Risk Patients ◽

Student’S T ◽

Parenchymal Enhancement

AbstractBackground parenchymal enhancement (BPE) on multi-parametric breast magnetic resonance imaging (mpMRI) reflects the volume and intensity of gadolinium uptake. We developed a semi-automated segmentation algorithm to extract and quantify measures of BPE and investigated the agreement of computed measures of BPE with radiologist-assigned categories. We retrospectively identified 123 high risk patients with breast mpMRI performed for screening indications. Pre- and post-gadolinium T1-weighted series with and without fat suppression were co-registered. Using Otsu’s method and an active contours method, the breast tissue was segmented from the chest wall and non-fat voxels were clustered to identify the amount of fibroglandular tissue. Median and inter-quartile ranges for the absolute volume of BPE (BPE cm3) and the percentage of BPE (BPE%) using a threshold PE of 30% were computed within the segmented FGT on the first post-contrast phase. Student’s t-test was used to evaluate BPE volume and BPE% by radiologist-assigned categories. Both BPE volume and BPE% differed significantly between minimal/mild and moderate/marked radiologist assigned categories (p=0.030 and 0.004, respectively). Using our newly developed semi-automated segmentation pipeline, we quantified fibroglandular tissue and BPE. The overlapping ranges of quantitative BPE corresponding to the radiologist-assigned BPE categories showcases the inter-reader variability with visual estimation of BPE.

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