scholarly journals An automated approach for the optimised estimation of breast density with Dixon methods

2020 ◽  
Vol 93 (1106) ◽  
pp. 20190639 ◽  
Author(s):  
Rosie Goodburn ◽  
Evanthia Kousi ◽  
Alison Macdonald ◽  
Veronica Morgan ◽  
Erica Scurr ◽  
...  
Keyword(s):  
Breast Density ◽  
Spatial Gradient ◽  
Breast Volume ◽  
Absolute Increase ◽  
Water Fraction ◽  
Total Percentage ◽  
Automated Method ◽  
Image Volume ◽  
Text Images ◽  
Uniform Scaling

Objective: To present and evaluate an automated method to correct scaling between Dixon water/fat images used in breast density (BD) assessments. Methods: Dixon images were acquired in 14 subjects with different T1 weightings (flip angles, FA, 4°/16°). Our method corrects intensity differences between water ([Formula: see text]) and fat ([Formula: see text]) images via the application of a uniform scaling factor (SF), determined subject-by-subject. Based on the postulation that optimal SFs yield relatively featureless summed fat/scaled-water ([Formula: see text]) images, each SF was chosen as that which generated the lowest 95th-percentile in the absolute spatial-gradient image-volume of [Formula: see text] . Water-fraction maps were calculated for data acquired with low/high FAs, and BD (%) was the total percentage water within each breast volume. Results: Corrected/uncorrected BD ranged from, respectively, 10.9–71.8%/8.9–66.7% for low-FA data to 8.1–74.3%/5.6–54.3% for high-FA data. Corrected metrics had an average absolute increase in BD of 6.4% for low-FA data and 18.4% for high-FA data. BD values estimated from low- and high-FA data were closer following SF-correction. Conclusion: Our results demonstrate need for scaling in such BD assessments, where our method brought high-FA and low-FA data into closer agreement. Advances in knowledge: We demonstrated a feasible method to address a main source of inaccuracy in Dixon-based BD measurements.

scholarly journals Quantitative Measurement of Breast Density Using Personalized 3D-Printed Breast Model for Magnetic Resonance Imaging

Diagnostics ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 793
Author(s):  
Rooa Sindi ◽  
Yin How Wong ◽  
Chai Hong Yeong ◽  
Zhonghua Sun
Keyword(s):  
Breast Density ◽  
Repeated Measures ◽  
Quantitative Measurement ◽  
Breast Volume ◽  
Tissue Volume ◽  
Fibroglandular Tissue ◽  
Quantitative Measurements ◽  
3D Printed ◽  
The Difference ◽  
Weighted Sequences

Despite the development and implementation of several MRI techniques for breast density assessments, there is no consensus on the optimal protocol in this regard. This study aimed to determine the most appropriate MRI protocols for the quantitative assessment of breast density using a personalized 3D-printed breast model. The breast model was developed using silicone and peanut oils to simulate the MRI related-characteristics of fibroglandular and adipose breast tissues, and then scanned on a 3T MRI system using non-fat-suppressed and fat-suppressed sequences. Breast volume, fibroglandular tissue volume, and percentage of breast density from these imaging sequences were objectively assessed using Analyze 14.0 software. Finally, the repeated-measures analysis of variance (ANOVA) was performed to examine the differences between the quantitative measurements of breast volume, fibroglandular tissue volume, and percentage of breast density with respect to the corresponding sequences. The volume of fibroglandular tissue and the percentage of breast density were significantly higher in the fat-suppressed sequences than in the non-fat-suppressed sequences (p < 0.05); however, the difference in breast volume was not statistically significant (p = 0.529). Further, a fat-suppressed T2-weighted with turbo inversion recovery magnitude (TIRM) imaging sequence was superior to the non-fat- and fat-suppressed T1- and T2-weighted sequences for the quantitative measurement of breast density due to its ability to represent the exact breast tissue compositions. This study shows that the fat-suppressed sequences tended to be more useful than the non-fat-suppressed sequences for the quantitative measurements of the volume of fibroglandular tissue and the percentage of breast density.

A new automated method to evaluate 2D mammographic breast density according to BI-RADS® Atlas Fifth Edition recommendations

2019 ◽  
Vol 29 (7) ◽  
pp. 3830-3838 ◽  
Author(s):  
Corinne Balleyguier ◽  
Julia Arfi-Rouche ◽  
Bruno Boyer ◽  
Emilien Gauthier ◽  
Valerie Helin ◽  
...  
Keyword(s):  
Breast Density ◽  
Mammographic Breast Density ◽  
Automated Method

Proton density water fraction as a reproducible MR‐based measurement of breast density

2021 ◽  
Author(s):  
Leah C. Henze Bancroft ◽  
Roberta M. Strigel ◽  
Erin B. Macdonald ◽  
Colin Longhurst ◽  
Jacob Johnson ◽  
...  
Keyword(s):  
Breast Density ◽  
Proton Density ◽  
Water Fraction

scholarly journals Association between indicators of systemic inflammation biomarkers during puberty with breast density and onset of menarche

2020 ◽  
Vol 22 (1) ◽  
Author(s):  
Karin B. Michels ◽  
Kristen Keller ◽  
Ana Pereira ◽  
Claire E. Kim ◽  
José L. Santos ◽  
...  
Keyword(s):  
Systemic Inflammation ◽  
Breast Density ◽  
Inflammatory Markers ◽  
Failure Time ◽  
Inflammatory Marker ◽  
Pubertal Development ◽  
Tanner Stage ◽  
Breast Volume ◽  
Body Fatness ◽  
Multivariable Models

Abstract Background Systemic inflammation may play a role in shaping breast composition, one of the strongest risk factors for breast cancer. Pubertal development presents a critical window of breast tissue susceptibility to exogenous and endogenous factors, including pro-inflammatory markers. However, little is known about the role of systemic inflammation on adolescent breast composition and pubertal development among girls. Methods We investigated associations between circulating levels of inflammatory markers (e.g., interleukin-6 (IL-6), tumor necrosis factor receptor 2 (TNFR2), and C-reactive protein (CRP)) at Tanner stages 2 and 4 and breast composition at Tanner stage 4 in a cohort of 397 adolescent girls in Santiago, Chile (Growth and Obesity Cohort Study, 2006–2018). Multivariable linear models were used to examine the association between breast composition and each inflammatory marker, stratifying by Tanner stage at inflammatory marker measurement. Accelerated failure time models were used to evaluate the association between inflammatory markers concentrations at each Tanner stage and time to menarche. Results In age-adjusted linear regression models, a doubling of TNFR2 at Tanner 2 was associated with a 26% (95% CI 7–48%) increase in total breast volume at Tanner 4 and a 22% (95% CI 10–32%) decrease of fibroglandular volume at Tanner 4. In multivariable models further adjusted for body fatness and other covariates, these associations were attenuated to the null. The time to menarche was 3% (95% CI 1–5%) shorter among those in the highest quartile of IL-6 at Tanner 2 relative to those in the lowest quartile in fully adjusted models. Compared to those in the lowest quartile of CRP at Tanner 4, those in the highest quartile experienced 2% (95% CI 0–3%) longer time to menarche in multivariable models. Conclusions Systemic inflammation during puberty was not associated with breast volume or breast density at the conclusion of breast development among pubertal girls after adjusting for body fatness; however, these circulating inflammation biomarkers, specifically CRP and IL-6, may affect the timing of menarche onset.

Comparison of subjective and fully automated methods for measuring mammographic density

2017 ◽  
Vol 59 (2) ◽  
pp. 154-160 ◽  
Author(s):  
Nataliia Moshina ◽  
Marta Roman ◽  
Sofie Sebuødegård ◽  
Gunvor G Waade ◽  
Giske Ursin ◽  
...  
Keyword(s):  
Mammographic Density ◽  
Breast Density ◽  
Screening Program ◽  
Weighted Kappa ◽  
Screening Mammography ◽  
Point Scale ◽  
Mean Values ◽  
Automated Method ◽  
Cancer Screening Program ◽  
Density Assessment

Background Breast radiologists of the Norwegian Breast Cancer Screening Program subjectively classified mammographic density using a three-point scale between 1996 and 2012 and changed into the fourth edition of the BI-RADS classification since 2013. In 2015, an automated volumetric breast density assessment software was installed at two screening units. Purpose To compare volumetric breast density measurements from the automated method with two subjective methods: the three-point scale and the BI-RADS density classification. Material and Methods Information on subjective and automated density assessment was obtained from screening examinations of 3635 women recalled for further assessment due to positive screening mammography between 2007 and 2015. The score of the three-point scale (I = fatty; II = medium dense; III = dense) was available for 2310 women. The BI-RADS density score was provided for 1325 women. Mean volumetric breast density was estimated for each category of the subjective classifications. The automated software assigned volumetric breast density to four categories. The agreement between BI-RADS and volumetric breast density categories was assessed using weighted kappa (kw). Results Mean volumetric breast density was 4.5%, 7.5%, and 13.4% for categories I, II, and III of the three-point scale, respectively, and 4.4%, 7.5%, 9.9%, and 13.9% for the BI-RADS density categories, respectively ( P for trend < 0.001 for both subjective classifications). The agreement between BI-RADS and volumetric breast density categories was kw = 0.5 (95% CI = 0.47–0.53; P < 0.001). Conclusion Mean values of volumetric breast density increased with increasing density category of the subjective classifications. The agreement between BI-RADS and volumetric breast density categories was moderate.

scholarly journals AutoDensity: an automated method to measure mammographic breast density that predicts breast cancer risk and screening outcomes

2013 ◽  
Vol 15 (5) ◽  
Author(s):  
Carolyn Nickson ◽  
Yulia Arzhaeva ◽  
Zoe Aitken ◽  
Tarek Elgindy ◽  
Mitchell Buckley ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Breast Density ◽  
Mammographic Breast Density ◽  
Automated Method

Novel use of Single X-Ray Absorptiometry for Measuring Breast Density

2005 ◽  
Vol 4 (2) ◽  
pp. 173-182 ◽  
Author(s):  
John A. Shepherd ◽  
Lionel Herve ◽  
Jessie Landau ◽  
Bo Fan ◽  
Karla Kerlikowske ◽  
...  
Keyword(s):  
Breast Density ◽  
Reference Standards ◽  
Dense Area ◽  
Cancer Risk Factors ◽  
X Ray ◽  
Automated Method ◽  
Breast Cancer Risk Factors ◽  
Accuracy And Repeatability

Breast density is one of the strongest known breast cancer risk factors. Breast density has primarily been measured by either categorical scores of the proportion of opacity on a screening mammogram or as a ratio of the delineated dense area to the total breast area on a mammogram. These methods are limited by their subjectivity, their lack of absolute reference standards, and lack of automation for clinical use. We present a novel automated method for measuring breast composition we call Breast Compositional Density measured using single X-ray absorptiometry techniques, or BDSXA. BDSXA measures breast compositional density by comparing the opacity on the mammogram to two reference standards imaged with each breast. We present the mathematical derivation of BDSXA, methods for correcting for radiographic nonuniformity in the mammogram, and in vitro measures of accuracy and repeatability. Using phantoms, BDSXA has been shown to have a long-term repeatability of better than 2% for a breast composition range of 0 to 100% fibroglandular density.

scholarly journals Mammographic Breast Density Assessed with Fully Automated Method and its Risk for Breast Cancer

2019 ◽  
Vol 9 ◽  
pp. 43 ◽  
Author(s):  
Pendem Saikiran ◽  
Ruqiya Ramzan ◽  
Nandish S. ◽  
Phani Deepika Kamineni ◽  
Priyanka ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Cancer Risk ◽  
Breast Density ◽  
Roc Curve ◽  
Clinical Risk Factors ◽  
Mammographic Breast Density ◽  
Risk Models ◽  
Clinical Risk ◽  
Automated Method

Objectives: We evaluated the association between breast cancer and breast density (BD) measured using fully automated software. We also evaluated the performance of cancer risk models such as only clinical risk factors, density related measures, and both clinical risk factors and density-related measures for determining cancer risk. Materials and Methods: This is a retrospective case–control study. The data were collected from August 2015 to December 2018. Two hundred fifty women with breast cancer and 400 control subjects were included in this study. We evaluated the BD qualitatively using breast imaging-reporting and data system density and quantitatively using 3D slicer. We also collected clinical factors such as age, familial history of breast cancer, menopausal status, number of births, body mass index, and hormonal replacement therapy use. We calculated the odds ratio (OR) for BD to determine the risk of breast cancer. We performed receiver operating characteristic (ROC) curve to assess the performance of cancer risk models. Results: The OR for the percentage BD for second, third, and fourth quartiles was 1.632 (95% confidence intervals [CI]: 1.102–2.416), 2.756 (95% CI: 1.704–4.458), and 3.163 (95% CI: 1.356–5.61). The area under ROC curve for clinical risk factors only, mammographic density measures, combined mammographic, and clinical risk factors was 0.578 (95% CI: 0.45, 0.64), 0.684 (95% CI: 0.58, 0.75), and 0.724 (95% CI: 0.64, 0.80), respectively. Conclusion: Mammographic BD was found to be positively associated with breast cancer. The density related measures combined clinical risk factors, and density model had good discriminatory power in identifying the cancer risk.

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