The Potential Impact of Risk-Based Screening Mammography in Women 40–49 Years Old

Price, Elissa R.; Keedy, Alexander W.; Gidwaney, Rita; Sickles, Edward A.; Joe, Bonnie N.

doi:10.2214/ajr.15.14668

Cited by 24 publications

(9 citation statements)

References 46 publications

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“…Also, they have proposed risk-based screening, such as selective screening of women 40 to 49 years old who are at increased risk of breast cancer, as a means of improving the benefit-to-risk ratio using a meta-analysis. However, a recent retrospective study suggested that 88% and 86% of women with screening-detected breast cancer did not have a very strong family history of breast cancer or extremely dense breast tissue, respectively (Price et al, 2015). Among patients with screening-detected malignancies, 76% did not have a very strong family history or extremely dense breasts (Ray et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Breast Density of Mammography is Correlated with Reproductive Risk Factors Regardless of Menopausal Status: A Cross-Sectional Study of the Korean National Screening Program

Lee

2020

Asian Pac J Cancer Prev

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Objective: To clarify the limitations of mammography screening for women with dense breasts, we examined breast density and its effects on screening results. Patients and Methods: We performed a cross-sectional, observational study on women who underwent mammography. Data from the National Cancer Screening Program(NCSP) from 2009 to 2013 were used. The study population consisted of participants with high breast density. We used a logistic regression analysis to evaluate the relationships between breast density and reproductive factors and screening results according to menopause status. Results: High breast density was reported for 57.5% of all participants (3,417,319 participants). Screening results indicated breast density of <25%, 25-50%, 51-75%, and ≥76% for 16.4%, 26.3%, 37.8%, and 19.5%, respectively, of participants. According to the screening results, high breast density was correlated with high deferment and recall rates. Reproductive factors, especially parity, breastfeeding, and use of oral contraceptives, had consistent effects on screening results of premenopausal and postmenopausal women. Regardless of menopausal status, age, early onset of menarche (15 years or younger), fewer live births (≤1 birth), and previous benign breast disease were correlated with increased breast density. In postmenopausal women, early-onset menopause and longer-term hormone replacement therapy (≥2 years) also independently increased breast density. Conclusion: Breast density influenced screening results, which could increase the rate of recall. Breast density was also influenced by reproductive factors, with patterns similar to those of breast cancer risk, regardless of menopausal status. We need to identify high-risk women with high density who would probably benefit from supplemental breast cancer screening.

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Section: Discussionmentioning

confidence: 99%

Breast Density of Mammography is Correlated with Reproductive Risk Factors Regardless of Menopausal Status: A Cross-Sectional Study of the Korean National Screening Program

Lee

2020

Asian Pac J Cancer Prev

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“…• T1-weighted 3D images, acquired using a VIBE sequence with fat saturation and a nominal resolution of (0.759 9 0.759 9 0.900) mm 3 , as routinely used in clinical dynamic contrast-enhanced MRI protocols for the breast;…”

Section: A2 Mr Imagingmentioning

confidence: 99%

“…Women with more than 75% density have a fourfold or higher risk of breast cancer compared to those with less than 5% . More intensive screening for women with high mammographic density has been proposed but remains controversial …”

Section: Introductionmentioning

confidence: 99%

“…1 More intensive screening for women with high mammographic density has been proposed 2 but remains controversial. 3 However, in clinical practice, mammographic density, as assessed on x-ray mammograms, is generally reported using only qualitative, radiologist-assessed categories, and agreement between radiologists tends to be only moderate. 4 Quantitative analysis is hampered by the fact that breast density is an inherently 3-D material property and therefore not well suited to measurement using 2-D x-ray projections.…”

Section: Introductionmentioning

confidence: 99%

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Breast MRI segmentation for density estimation: Do different methods give the same results and how much do differences matter?

Doran

Hipwell²,

Denholm

et al. 2017

Medical Physics

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PurposeTo compare two methods of automatic breast segmentation with each other and with manual segmentation in a large subject cohort. To discuss the factors involved in selecting the most appropriate algorithm for automatic segmentation and, in particular, to investigate the appropriateness of overlap measures (e.g., Dice and Jaccard coefficients) as the primary determinant in algorithm selection.MethodsTwo methods of breast segmentation were applied to the task of calculating MRI breast density in 200 subjects drawn from the Avon Longitudinal Study of Parents and Children, a large cohort study with an MRI component. A semiautomated, bias‐corrected, fuzzy C‐means (BC‐FCM) method was combined with morphological operations to segment the overall breast volume from in‐phase Dixon images. The method makes use of novel, problem‐specific insights. The resulting segmentation mask was then applied to the corresponding Dixon water and fat images, which were combined to give Dixon MRI density values. Contemporaneously acquired T1‐ and T2‐weighted image datasets were analyzed using a novel and fully automated algorithm involving image filtering, landmark identification, and explicit location of the pectoral muscle boundary. Within the region found, fat‐water discrimination was performed using an Expectation Maximization–Markov Random Field technique, yielding a second independent estimate of MRI density.ResultsImages are presented for two individual women, demonstrating how the difficulty of the problem is highly subject‐specific. Dice and Jaccard coefficients comparing the semiautomated BC‐FCM method, operating on Dixon source data, with expert manual segmentation are presented. The corresponding results for the method based on T1‐ and T2‐weighted data are slightly lower in the individual cases shown, but scatter plots and interclass correlations for the cohort as a whole show that both methods do an excellent job in segmenting and classifying breast tissue.ConclusionsEpidemiological results demonstrate that both methods of automated segmentation are suitable for the chosen application and that it is important to consider a range of factors when choosing a segmentation algorithm, rather than focus narrowly on a single metric such as the Dice coefficient.

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“…Risk prediction models designed to personalize mammography screening include personal and familial risk factors (9–11), which have been validated in case-control data (12) but less frequently in screening populations (13). A substantial number of women diagnosed with breast cancer do not exhibit breast cancer risk factors (14).…”

mentioning

confidence: 99%

Age-based versus Risk-based Mammography Screening in Women 40–49 Years Old: A Cross-sectional Study

et al. 2019

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Background: Risk-based screening in women 40–49 years old has not been evaluated in routine screening mammography practice. Purpose: To use a cross-sectional study design to compare the trade-offs of risk-based and age-based screening for women 45 years of age or older to determine short-term outcomes. Materials and Methods: A retrospective cross-sectional study was performed by using a database of 20 539 prospectively interpreted consecutive digital screening mammograms in 10 280 average-risk women aged 40–49 years who were screened at an academic medical center between January 1, 2006, and December 31, 2013. Two hypothetical screening scenarios were compared: an age-based (≥45 years) scenario versus a risk-based (a 5-year risk of breast cancer greater than that of an average 50-year-old) scenario. Risk factors for risk-based screening included family history, race, age, prior breast biopsy, and breast density. Outcomes included breast cancers detected at mammography, false-positive mammograms, and benign biopsy findings. Short-term outcomes were compared by using the χ2 test. Results: The screening population included 71 148 screening mammograms in 24 928 women with a mean age of 55.5 years ± 8.9 (standard deviation) (age range, 40–74 years). In women 40–49 years old, usual care included 50 screening-detected cancers, 1787 false-positive mammograms, and 384 benign biopsy results. The age-based (≥45 years) screening strategy revealed more cancers than did the risk-based strategy (34 [68%] vs 13 [26%] of 50; P < .001), while prompting more false-positive mammograms (899 [50.3%] vs 216 [12.1%] of 1787; P < .001) and benign biopsy results (175 [45.6%] vs 49 [12.8%] of 384; P < .001). The risk-based strategy demonstrated low levels of eligibility (few screenings) in the 40–44-year age group. Differences in outcomes in the 45–49-year age group explained the overall hypothetical screening strategy differences. Conclusion: Risk-based screening for women 40–49 years old includes few women in the 40–44-year age range. Significant trade-offs in the 45–49-year age group explain the overall difference between hypothetical screening scenarios, both of which reduce the benefits as well as the harms of mammography for women 40–49 years old.

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The Potential Impact of Risk-Based Screening Mammography in Women 40–49 Years Old

Cited by 24 publications

References 46 publications

Breast Density of Mammography is Correlated with Reproductive Risk Factors Regardless of Menopausal Status: A Cross-Sectional Study of the Korean National Screening Program

Breast Density of Mammography is Correlated with Reproductive Risk Factors Regardless of Menopausal Status: A Cross-Sectional Study of the Korean National Screening Program

Breast MRI segmentation for density estimation: Do different methods give the same results and how much do differences matter?

Age-based versus Risk-based Mammography Screening in Women 40–49 Years Old: A Cross-sectional Study

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