Mammographic breast density and the risk of breast cancer: A systematic review and meta-analysis

Bodewes, F.T.H.; Asselt, A.A. van; Dorrius, Monique D.; Greuter, Marcel J W; Bock, Geertruida H. de

doi:10.1016/j.breast.2022.09.007

Cited by 92 publications

(61 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The top row shows the d 1 (x,y) components from Eq. [1]: (a) derived from the C-View image for illustration-1; (b) respective central slice image; (c) derived from the C-View image for illustration-2; and (d) respective central slice image. In the same order, the bottom row shows the respective e 1 (x,y) components (from the third row of Figure 1) derived from noise multiplication in Eq.…”

Section: Resultsmentioning

confidence: 99%

“…Signal Dependent Noise Plots: the top row shows the plots resulting from Eq. [1] (without noise multiplication) for the illustration images: top row, (a) derived from the C-view of illustration-1; (b) derived from the respective central slice image; (c) derived from the C-view illustration-2; and (d) derived from the respective central slice. The bottom row shows the respective plots derived from Eq.…”

Section: Resultsmentioning

confidence: 99%

“…Eq. [1], although complete, is a truncated one level expansion. Using the wavelet orthogonality conditions, the variance of g(x,y) can be expressed as where the variances on the right side of Eq.…”

Section: Methodsmentioning

confidence: 99%

“…[3] gives where e 1 (x,y) and z 1 (x,y) correspond to the components in Eq. [1], respectively, with the same interpretations relative to z(x,y). The expression for the variance for z(x,y) follows that of Eq.…”

Section: Methodsmentioning

confidence: 99%

“…As a control example, we expanded a white noise field using the Eq. [1] and investigated the component variances as comparators. To make some illustrations clear for viewing purposes, we have excised the largest rectangle that can fit within the breast image that has a small margin on the breast perimeter side excluded, referred to as the largest rectangle below; the algorithm was described in detail elsewhere [17].…”

Section: Methodsmentioning

confidence: 99%

See 4 more Smart Citations

Breast Density Analysis Using Digital Breast Tomosynthesis

Heine

Fowler

Weinfurtner

et al. 2023

Preprint

View full text Add to dashboard Cite

Background: Breast density (BD) is a significant breast cancer risk factor, traditionally assessed from two-dimensional (2D) mammograms. Mammography has shifted to digital breast tomosynthesis (DBT). BD measures developed for traditional 2D mammograms will require translation to DBT images. Objectives: We evaluated an anutomated percentage of BD measure (PDa) using several methods as risk factors with DBT data: (1) normalized volumetric measure; (2) total dense volume (Dv); (3) 2D measure applied to DBT volume images (slices) and averaged (slice-mean); and (4) applied to synthetic 2D images. For the DBT volume analysis, measure were derived theoretically and evaluated. PDawas modeled as a function of compressed breast thickness. An alternative method for constructing synthetic 2D mammograms was evaluated using the PDavolume results as a basis. Other measures such as the mean and standard deviations of the pixel values were also investigated as risk factors. Methods: A case-control study (n = 426 pairs) design was investigated with matching on age, hormone replacement, imaging unit, and screening history. Conditional logistic regression modeling, controlling body mas index and ethnicity, was used to estimate odds ratios (ORs) for the various image measures with 95% confidence intervals provided parenthetically. ORs were estimated per standard deviation increment of the respective image measurement. Results: Volumetic PDawas significantly associated with breast cancer risk [OR = 1.43 (1.18, 1.72)] and produced an identical OR as the slice-mean measure. PDavalues from the slices (function of compressed breast thickness) were modeled as 2nddegree polynomial (concave-down). The maximum PDavalue occurred at approximately 0.41×(compressed breast thickness), and this position was similar across case-control groups; PDafrom this slice location was significant [OR = 1.47 (1.21, 1.78)]. PDaproduced significant findings when applied to the 2D synthetic DBT images: [OR = 1.44 (1.18, 1.75)]. The mean of the pixel values taken from either the volume or synthetic 2D images were similar and significant [ORs ~ 1.31 (1.09, 1.57)]. Dvdid not produce a significant finding. Discussion: This study produced several findings. The PDaalgorithm provided significant ORs when applied to the volume and synthetic 2D images. AS predicted by the derivations, the volumetric and slice-mean measures are equivalent and produced an identical OR. The maximum PDaslice position found, empirically agreed with the polynomial model prediction. Dvwas not significant until normalized by the breast volume. The volumetric analysis enabled the construction of an alternative standardized 2D synthetic image, where each pixel value represents the percentage of BD above its location. Conclusion: Several BD measurements derived from DBT images produced significant breast cancer associations, showing the validity of the automated techniques. Theoretical derivations supported by empirical analyses led to the construction of a new synthetic 2D standardized image derived from the volume measurements. Ancillary to the study objectives, these volumetric findings provide evidence for understanding the nature of the percentage of breast density measure applied to traditional 2D mammograms. The study will require replication in different populations. Notwithstanding the findings, the study design provides a template for investigating other possible risk measures such as texture from DBT.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Breast Density Analysis Using Digital Breast Tomosynthesis

Heine

Fowler

Weinfurtner

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Mammographic density and breast cancer risk among Black American women

Holder,

Bigham,

Nelson

et al. 2024

Intl Journal of Cancer

View full text Add to dashboard Cite

High mammographic density is a well‐established risk factor for breast cancer; however, data from Black women are limited. It is largely unknown how mammographic density is associated with breast cancer subtypes among Black women. We examined the association between percent mammographic density (PMD) and breast cancer risk among participants in the Black Women's Health Study. Digital screening mammograms were available for 363 cases and 5541 non‐cases. Cumulus software was used to assess PMD. We used inverse probability of sampling weights and Cox proportional hazards models, adjusted for age and body mass index, to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) overall and by age at mammography and estrogen receptor (ER) status of the breast tumors. Multivariable models included additional breast cancer risk factors. Tests of statistical significance were 2‐sided. In simple models, women in the highest quartile of PMD had 53% increased odds of breast cancer compared to those in the lowest quartile (HR 1.53; 95% CI: 1.11, 2.11). HRs were 1.37 (95% CI: 0.83, 2.24) among women <55 years of age and 1.68 (95% CI: 1.10, 2.56) among women aged ≥55 years. HRs were 1.49 (95% CI: 1.02, 2.16) for ER+ cancer and 1.45 (95% CI: 0.73, 2.87) for ER– cancer. Associations were largely unchanged in multivariable models. In this study of U.S. Black women, higher PMD was associated with ER+ and ER– breast cancer risk. Findings from this study reinforce the importance of breast density as a risk factor for breast cancer in Black women.

show abstract

Letter to the editor: Relationship between menopausal hormone therapy and breast cancer: A nationwide population‐based cohort study

Jin

2024

Intl J Gynecology & Obste

View full text Add to dashboard Cite

Mammographic breast density and the risk of breast cancer: A systematic review and meta-analysis

Cited by 92 publications

References 56 publications

Breast Density Analysis Using Digital Breast Tomosynthesis

Breast Density Analysis Using Digital Breast Tomosynthesis

Mammographic density and breast cancer risk among Black American women

Letter to the editor: Relationship between menopausal hormone therapy and breast cancer: A nationwide population‐based cohort study

Contact Info

Product

Resources

About