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A Review of Breast Density Implications and Breast Cancer Screening
Journal Pre-proof A review of breast density implications and breast cancer screening Jingge Lian, Kangan Li PII:
S1526-8209(20)30060-4
DOI:
https://doi.org/10.1016/j.clbc.2020.03.004
Reference:
CLBC 1109
To appear in:
Clinical Breast Cancer
Received Date: 25 October 2019 Revised Date:
10 February 2020
Accepted Date: 12 March 2020
Please cite this article as: Lian J, Li K, A review of breast density implications and breast cancer screening, Clinical Breast Cancer (2020), doi: https://doi.org/10.1016/j.clbc.2020.03.004. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Inc.
A review of breast density implications and breast cancer screening
Jingge lian, Kangan Li*
Department of Radiology, Shanghai General Hospital, Shanghai Jiaotong University, Shanghai, 200080, P.R.
China
Corresponding author: Kangan Li, E-mail：[email protected]
___________________________________________________________________________________________
*Corresponding author: Kangan Li, MD, PhD, Department of Radiology, Shanghai General Hospital, No. 650
New Songjiang Road, Shanghai, China. E-mail: [email protected]; Fax: 0086-21-63240825; Tel:
0086-21-37798252
Compliance with Ethical Standards: Funding: This research is financially supported by the National Natural Science Foundation of China (KAL 81972872, 11826020), the Shanghai Pujiang Project (KAL 2014PJD028), the Shanghai Jiaotong University Cross-cooperation Project (KAL YG2015MS31), the Shanghai Science and Technology Commission Project (KAL 17441900700), and the Shanghai Shenkang Project (KAL 6CR3091B). Conflict of Interest: All of the authors declare that they have no conflicts of interest to report. Ethical approval: This is a review article that does not contain any studies conducted with human participants or animals.
Abstract
Breast
density is an independent risk factor for breast cancer and significantly
decreases the sensitivity of mammography. Assessing a woman’s risk of developing breast cancer is becoming increasingly important for establishing individual screening recommendations and preventive strategies. This paper reviews the factors influencing mammographic density (MD), the available methods of MD assessment, and its effect on breast cancer. Finally, we discuss the supplemental screening methods for women with dense breast tissue.
Keywords
Breast cancer, Breast density, Mammography, Supplemental screening
1. Introduction
Breast cancer has the highest incidence and mortality rates among cancers in women worldwide. Approximately 2.1 million new breast cancer cases and 0.6 million breast cancer deaths were reported in 2018[1]. Therefore, early diagnosis and treatment play an important role. Mammography, which is the first screening method for breast cancer, provides a quick and easy method to assess breast density. In X-ray imaging, the fatty tissue appears radiolucent/black, while fibroglandular tissue, including fibroblasts, epithelial cells and connective tissues[2], is radiopaque/white. Multiple studies[3, 4] have indicated that screening mammography could reduce breast cancer-associated mortality.
Mammographic density (MD), a method of measuring the amount of fibroglandular tissue relative to fat in the breast, has been proven to be associated with the sensitivity and specificity of screening and has also been considered to be an independent risk factor for breast cancer. High breast density is a major risk factor for breast cancer. One study[5] found that women with dense breasts (>75% density) had a 4-6 times higher risk of developing breast cancer than women with fatty breasts (<25% density).
However, the standard method of evaluating breast density, the screening interval and the starting age remain uncertain. This paper reviews the available methods of MD evaluation, some of the factors that modulate MD and its association with cancer risk. Additionally, we discuss the supplemental screening methods available to women with dense breasts.
2. Factors influencing MD
MD is highly variable and is influenced by many factors during a woman’s life. It usually decreases with increasing age and body mass index (BMI), increases with hormone replacement therapy (HRT) and decreases with tamoxifen therapy. Other variables affecting breast density include diet and reproductive factors.
Age and BMI
Numerous studies[6, 7] have investigated the association between age and MD. It is estimated that MD usually decreases with age, especially during the menopausal transition period. The International Consortium on Mammographic Density (ICMD)[8] recently used a quantitative method to research differences in MD among more than 11 000 women in 22 countries by age and menopausal status. Investigators found that MD decreased with age in both premenopausal and postmenopausal women regardless of ethnicity, and the decrease was more marked during menopausal transition.
A high BMI is associated with reduced MD. Some studies[9] have also found that increased birth weight and grown-up height are positively correlated with high MD. In one investigation[10] that used data from a UK case-control study and a Norwegian cohort study, researchers found that BMI was strongly positively associated with nondense volume (NDV) but strongly negatively associated with percent mammographic density (PMD). Thus, the failure to correct for BMI may
underestimate risk. Nevertheless, previous studies[11, 12] have considered the relationship between BMI and MD and reported inconsistent results.
Exogenous hormonal exposures
Extrinsic hormones and medication impact density in different ways. HRT, which is used to relieve menopausal symptoms, leads to increased MD. Clinical trials[13] have shown that MD increases with estrogen and progestin use. Nevertheless, estrogen alone does not significantly increase MD[14, 15]. Furthermore, HRT is related to a potential increase in breast cancer risk[16]. A recent nested case-control study[17] within the Women's Health Initiative Trial (WHI) found that the risk of breast cancer increased by 3% (odds ratio (OR) = 1.03; 95% confidence interval (CI): 1.10, 1.06) with each 1% increase in MD after assigned HRT.
Conversely, tamoxifen and aromatase inhibitors (AI) lead to a decrease in MD by inhibiting the proliferation of mammary cells. Tamoxifen and AI act through the estrogenic pathway and thus affect only estrogen-receptor (ER)-positive breast cancer. In a randomized prevention trial[18], compared with the control group, women in the tamoxifen group had a significantly lower MD and a 63% lower risk of breast cancer. In a [19]study that included 213 premenopausal women with breast cancer after tamoxifen discontinuation, a mean density increase of 1.8% was found compared to that before tamoxifen discontinuation. Similar results[20, 21] on AI have also been found. Engmann et al.[22] reported significantly greater reductions in volumetric percent density among women with breast cancer treated with tamoxifen
(premenopausal) or AI (postmenopausal); the greatest reductions were observed when the baseline density was ≥10%. Therefore, we can conclude from these results that women who experience a quantitative drop in breast density on tamoxifen or AI benefit from a reduction in the risk of subsequent cancer. Furthermore, MD may have clinical implications for assessing the response to chemoprevention with selective estrogen receptor modulators and AI.
Heritability and single-nucleotide polymorphisms (SNPs)
MD has been shown to be highly heritable, and twin studies[23, 24] have shown that genes account for approximately 60% of the variation in MD. Boyd and colleagues[23] compared PMD among monozygotic and dizygotic twins, and 63% of the variation in breast density was explained by heritability in all twins studied. Another study[24] also investigated greater absolute MD and PMD in monozygotic twins than in dizygotic twins. Similarly, Brand et al.[25] found high heritability of MD using volumetric measures. These studies suggest the significance of genetic components in MD.
Numerous genetic association studies have examined the correlation between SNPs and MD. To date, SNPs in genes including AREG, ESR1, ZNF365, LSP1/TNNT3, IGF1,
TMEM184B,
SGSM3/MKL1,
8p11.23,
PRDM6,
1q12.21A,
TAB2,
CCDC170/ESR1 and 12q24 have been determined to have a significant association with MD[26, 27]. Additionally, a recent study[28] discovered three novel MD loci and demonstrated that common genetic variants account for approximately 25% of the
variation in MD and that the ratio of SNP-based to heritability varies between the absolute dense volume and NDV.
Other factors
Other modifiable factors influencing breast density include nutrition, ethnicity, air pollution, menarche and reproductive factors (e.g., age at first birth, parity; refs.). In a study[29], a western dietary pattern was related to higher MD among overweight and obese females. Another cross‐sectional study[30] indicated a clear positive correlation between MD and a higher calorie intake after controlling for covariates. A previous study[31] suggested that race/ethnicity and migration history can impact the risk for breast cancer by increasing MD. It has also been found that air pollution increases the risk of breast cancer by increasing MD. A study[32] evaluated the correlation between air pollutants and breast density and considered that women living in areas with higher levels of cobalt and lead would prefer to have higher breast density. However, this is not a consistent finding; some studies have detected a positive association[33], while others have detected no association[34].
3. MD assessment measures
Mammographic images are two-dimensional (2D) planar representations of a three-dimensional (3D) structure. There are numerous methods available for assessing MD, which can be classified as subjective visual assessment, semi-automated methods and automated methods (Table 1). Different methods of assessing MD are now being used as a surrogate endpoint for breast cancer incidence in several early
phase chemoprevention trials, and changes in MD have been considered as biomarkers for assessing risk. To date, there are no recommendations or standards for MD standardization. Therefore, developing a standardized measurement of MD for clinical implementations is desirable but represents a substantial challenge.
Wolfe[35] first proposed a categorical classification according to parenchymal patterns in 1976: N1 indicated a breast with parenchyma consisting mainly of fat and the lowest breast cancer risk. DY represents a breast with diffuse parenchyma or extensive nodular density and the highest risk of cancer. P1 and P2 represented connective tissue hyperplasia surrounding ducts of lesser and greater extent, respectively, associated with intermediate increases in risk. However, the method proposed by Wolfe was not reproducible[36-40].
Currently, the Breast Imaging Reporting and Data System (BI-RADS) developed by the American College of Radiology (ACR) is the most widely used clinical classification by radiologists. The recently updated 5th edition of the BI-RADS atlas[41] is used to standardize the reporting of visual assessment of mammograms, and it defines four categories as follows:
A) almost entirely fatty;
B) scattered areas of fibroglandular density;
C) heterogeneously dense, which may obscure small masses; and
D) extremely dense, which lowers the sensitivity of mammography.
Less dense breasts have a lower risk of breast cancer (A, B), whereas denser breasts have a higher risk (C, D) (Fig. 1). In the current edition of the classification system, this method emphasizes the potential masking effect of dense breast tissue rather than the size of the dense area; breasts that are dense enough to hide cancer in only a portion of the dense area of glandular tissue should be categorized as heterogeneously dense, prompting a discussion of supplemental screening. In findings from multiple studies[42-45], investigators have also shown the presence of significant inter- and intraobserver differences for subjective visual estimation of breast density by mammography, particularly with regard to the distinction between the two middle categories (B and C), which implies that the experience of radiologists is important but that even experience cannot overcome the subjectivity of visual assessment. Thus, qualitative assessment is an imperfect method of breast density measurement[46].
Considering
the
limitations
of
subjective
visual
assessment,
various
computer-assisted evaluation approaches are now available, ranging from semi-automated to fully automated methods. Among these, CumulusTM, a computerized model developed by Byng et al.[47], has been considered the gold standard for quantitative measurement for many years, and numerous data have shown a higher reproducibility of this method than that of BI-RADS-based visual assessment[48-51]. However, these methods require human input and training to obtain the density thresholds, which may limit their widespread clinical application. Many researchers have studied automated area-based options of MD assessment, which availably remove the human interactive component of CumulusTM and Madena.
These research tools include AutoDensity, Densitas, ImageJ, iReveal, STRATUS, Libra, and MedDensity.
However, a serious disadvantage of the area-based assessment is that it is based on 2D images, with a lack of consideration of breast thickness, and depends heavily on the operator. At the same time, breast density assessment methods based on 3D mammography have emerged. To date, the best-known automatic systems, such as Quantra (Hologic, Inc., Bedford, MA, USA) and Volpara (Matakina International, Wellington, New Zealand), allow mammography-based, volumetric, quantitative measurements of the absolute breast tissue[52-54]. Several studies[55, 56] have investigated the consistency between density assessments by radiologists and these automated methods and demonstrated a positive association between BI-RADS categories and automated assessment. Although these methods are fully automated, breast density calculations according to mammographic images may still vary depending on differences in tissue compression and breast location[57].
4. Breast density with risk of breast cancer
Breast density affects the risk for breast cancer in two primary ways. On the one hand, breast density has a masking effect leading to decreased sensitivity of mammography, and on the other hand, breast density is an independent risk factor for breast cancer.
Masking effect
Dense breast tissues can mask cancers, resulting in lower sensitivity of mammography[58-64]. Dense tissues and cancer tissues have similar X-ray attenuation properties, which appear white on mammograms. This implies that women who have higher breast density are more likely to have a high false positive rate in mammography[65]. This phenomenon is known as the masking effect.
Studies[66-68] have demonstrated that mammogram screenings cannot detect all breast cancers because the sensitivity of mammograms is determined by the breast density. Although breast density is correlated with adverse outcomes, it does not affect the risk of developing breast cancer. In a retrospective review[69] including 335 cases of breast cancers, 263 (78%) were masked by dense breast tissue on mammography, which confirmed the masking effect of dense tissue. Data obtained by a Dutch screening agency using digital mammography (DM) from 2003 to 2011[70] indicated that mammography had a sensitivity of 85.7% for women with fatty breasts. By comparison, women with extremely dense breasts had a sensitivity of 61%. The researchers also found that false positive recalls increased gradually from screening to assessment with increasing MD, from 11.2% in mostly fatty breasts to 23.8% in extremely dense breasts.
The masking effect does, moreover, lead to an increase in the incidence of interval cancers. Interval cancers are those detected due to clinical features during the interval between recommended screens[71]. Interval cancer rates increase with increasing breast density[49, 72, 73], with a more than 17-fold higher risk observed among
women with dense breasts than among women with nondense breasts or those with a personal history of breast cancer[74]. These findings demonstrate that high-density breast tissue may affect the early detection of screening mammography and therefore lead to inconclusiveness in the mammogram results of women with high-density breast tissue.
Breast density as an independent risk factor for breast cancer
Beyond the masking effect, MD is also an independent risk factor for breast cancer. Wolfe[35] first published a description of MD and breast cancer risk. Following Wolfe’s work, the relationship between MD and breast cancer has been widely investigated[75-78].
Several studies[53, 79-82] have demonstrated that breast density is a strong and independent risk factor for breast cancer. In one study[83] that included 6020 breast screening assessed cases and 1040 screened women with a family history of breast cancer, there was a positive correlation between breast density and the incidence of cancer, and the risk dense tissue increased by 3% (95% confidence interval (CI), 1-5%) per 10 cm3 by Volpara. In a meta-analysis[84] evaluating women from Asian countries, the risk calculated for PMD in premenopausal and postmenopausal women was evaluated to be 2.21 times that at baseline (95% CI, 1.52 to 3.21), suggesting that a higher risk for breast cancer is related to higher PD values among women in Asian countries. These studies indicate that there is a positive correlation between increased breast density and increased breast cancer risk.
To identify high-risk women before breast cancer, many breast cancer risk prediction models have been developed, such as the Gail model, the Breast Cancer Surveillance Consortium (BCSC) model, the Claus model, and the Tyrer-Cuzick (TC) model. In a retrospective cohort[85] of 35 921 women undergoing mammography screenings in the United States, the Gail and BCSC model had slightly higher AUCs than BRCAPRO and TC. The Breast Cancer Prospective Family Study Cohort (ProF-SC)[86] compared the 10-year performance of four models; in this cohort, BOADICEA and IBIS were well calibrated, whereas BRCAPRO and BCRAT underestimated risk in the overall validation cohort. Thus, models that incorporate additional genetic and nongenetic risk factors and estimate the risk of tumor subtypes may further improve the ability to predict breast cancer risk.
5. Supplemental screening
Not all women benefit equally from mammography. With the increasing awareness that dense breasts have an impact on the risk of breast cancer and the sensitivity of mammography screenings, a growing number of women need supplemental screening. Digital breast tomosynthesis (DBT), ultrasonography, and magnetic resonance imaging (MRI) can all be used as important supplemental tools for screening women with higher breast density. However, it is not clear whether this supplemental screening will provide beneficial effects or lead to overdiagnosis[45].
DBT
In 2011, the US Food and Drug Administration (FDA) approved DBT for all clinical
indications
accepted
for
mammography.
Tomosynthesis,
or
3D
mammography, is a digital mammographic technique in which the X-ray tube moves in an arc on the compressed breast; low-dose images are thereby obtained from multiple angles[87]. DBT can reduce the masking effect of dense breasts and reveal small breast cancer[88]. Numerous studies[89-93] have repeatedly shown that undergoing DBT screening after mammography increases cancer detection rates and reduces recall rates.
A recent publication[94] using prospective cohort data from 3 research centers showed that the cancer detection rate using DBT was higher than that using DM for all age groups (odds ratio (OR), 1.41; 95% CI, 1.05-1.89; P=0.02). The prospective Oslo Tomosynthesis Screening Trial (OTST)[95] showed that adding DBT to DM resulted in significantly improved sensitivity, from 54.1% (152 of 281) to 70.5% (198 of 281), and specificity, from 94.2% (22,632 of 24,020) to 95.0% (22,811 of 24,020). Another study[96] also showed that DBT improved cancer detection in breast cancer staging regarding the presence of additional ipsilateral lesions.
However, radiation exposure from DBT is twice that of conventional mammography. Another drawback is that the interpretation of DBT images relies heavily on the expertise of the radiologist, which leads to high variability. In addition, one study[97] showed that the use of tomosynthesis increases the rate of breast biopsy.
These factors all reflect the uncertainty of the balance between the pros and cons of tomosynthesis screening. Therefore, adoption of this technique has been limited.
Ultrasonography
Supplemental ultrasound is well accepted by patients because it does not emit ionizing radiation, regardless of breast density. Previous studies[45, 98, 99] have repeatedly shown greater breast cancer detection and sensitivity among women with dense breasts when mammography and ultrasound are combined rather than when mammography is used alone. However, the addition of ultrasound to mammogram screenings also increased false positive and benign biopsy rates[100].
In a multicenter retrospective analysis of 501 women who were followed up for 7 years, Kim et al.[101] indicated that there was a 98% 5-year recurrence-free survival rate among women with cancer detected by ultrasound. Adjunct screening with ultrasound in women with dense breasts increased breast cancer detection from 1.9 to 4.2 per 1000 screenings. The Japan Strategic Anticancer Randomized Trial (J-START)[102] investigated the advantages of adding ultrasound to mammography for screening for breast cancer. That study demonstrated a significant increase in breast cancer detection in the experimental group, with a significant reduction in interval cancers compared with the control group subjected to mammography only.
However, hand-held traditional ultrasound (HHUS) has several drawbacks, such as its high operator dependency, time-consuming nature and small field of view. Therefore, the FDA approved automated whole breast ultrasound (ABUS) as a new
screening method in 2012[103, 104], which attempts to use semiautomated techniques to evaluate breast density [105-107], but the application of these techniques in clinical practice has remained in the research phase. Some studies[108-111] comparing ABUS and HHUS have demonstrated similar lesion visualization and evaluations. One study[112] indicated that ABUS was superior to HHUS on the coronal plane by displaying architectural distortions.
MRI
Supplemental MRI has been regarded as the most sensitive imaging tool for screening breast cancer because it is not influenced by breast density and provides more information on breast masses, including lesion vascularity. The National Comprehensive Cancer Network (NCCN) guidelines[113] recommended MRI as a supplemental screening tool for high-risk women beginning at 25–29 years of age as well as those who have prior atypical biopsy or lobular carcinoma in situ.
In a multicenter, randomized, controlled study, Bakker et al[114] evaluated supplemental MRI screening among women in the Netherlands between the ages of 56 and 75 years with extremely dense breast tissue. Their study showed a significantly lower interval cancer rate of the MRI-invitation group than the mammography-only group (2.5 vs. 5.0 per 1000 screenings). A recent study[115] found that among women with an average risk for breast cancer, MRI could detect 15.5 additional cancers per 1000 women and had a specificity of 97.1% and a positive predictive value (PPV) of 35.7%. In addition, the interval cancer rate among women
who underwent MRI screening dropped to zero. In the ACRIN 6666 study, Berg et al[116] indicated that MRI plus mammography has a higher cancer yield and a lower false positive rate than ultrasound plus mammography, thus confirming that screening with MRI is better at detecting breast cancer than the combination of mammography and ultrasound.
Nevertheless, MRI is relatively expensive and time consuming, requires intravenous gadolinium injection and may increase the false positive rate. To address these challenges, recent studies[117, 118] have focused on techniques that have the potential to enhance the productivity of breast MRI, reducing the acquisition and reading times, without affecting the diagnosis. This abbreviated protocol can acquire images in less than 10 minutes and can be explained as quickly and easily as a screening mammogram.
6. Conclusions
Breast density has become a popular yet controversial topic in the field of breast imaging. MD is dynamic and modifiable, and there are many factors affecting MD throughout a woman’s lifetime, including age, BMI, and HRT. Mammography, as a routine examination method, can be used to assess MD and decrease the incidence and mortality rates for breast cancer. This review emphasizes that MD is an independent risk factor for breast cancer and that it is necessary to define a standard assessment to measure MD. Earlier assessment methods have shown limited consistency between observers and in relation to breast cancer risk. The application of
automated computer-based density measurements is beneficial because these methods have been proven to provide consistent and objective results that can be applied in large-scale clinical research. However, the sensitivity of breast cancer detection decreases with increasing breast density. To improve the sensitivity of breast cancer detection for women with dense breasts, physicians should consider the cost, potential hazards, and important outcomes and then work with patients to tailor the most appropriate multimodal screening strategies based on the patient’s risks, preferences, and values. MD assessment Visual
Semiautomated
Fully automated
Area
Area
Area
Method
FDA approval
Study
Wolfe
Not applicable
Wolfe (1976) [35]
TABAR
Not applicable
Gram et al. (1997)[119]
Qualitative
BI-RADS
Not applicable
Sickles et al. (2013) [41]
Semi qualitative
Boyd
Not applicable
Boyd et al. (1995) [120]
Visual analogue scale
Not applicable
Duffy et al. (2008) [121]
CumulusTM
No
Byng et al. (1994) [47]
Madena
No
Ursin et al. (1998) [122]
Qualitative
DenSeeMammo
FDA approved to provide information on 5th edition BI-RADS density categories
Quantitative
AutoDensity
No
Parenchymal pattern
Quantitative
Developed by the University
of Melbourne
Volume
Quantitative
Densitas
No
Abdolell et al. (2016) [123]
ImageJ
No
Li et al. (2012) [124]
iReveal
Yes
[125]
STRATUS
No
Eriksson et al. (2016) [126]
Libra
No
Keller et al. (2015) [127]
MedDensity
No
Tagliafico et al. (2013) [128]
BDSXA
No
Shepherd et al. (2005) [129]
Cumulus V
No
Alonzo-Proulx et al. (2010) [130]
Quantra
Yes
[131]
Spectral density
Yes
[132]
Volpara
Yes
[133]
Table 1. Mammographic density (MD) assessment methods.
Figure 1. Example images of the four breast density categories defined by the 5th edition of the BI-RADS mammography atlas.
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S1526-8209(20)30060-4
DOI:
https://doi.org/10.1016/j.clbc.2020.03.004
Reference:
CLBC 1109
To appear in:
Clinical Breast Cancer
Received Date: 25 October 2019 Revised Date:
10 February 2020
Accepted Date: 12 March 2020
Please cite this article as: Lian J, Li K, A review of breast density implications and breast cancer screening, Clinical Breast Cancer (2020), doi: https://doi.org/10.1016/j.clbc.2020.03.004. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Inc.
A review of breast density implications and breast cancer screening
Jingge lian, Kangan Li*
Department of Radiology, Shanghai General Hospital, Shanghai Jiaotong University, Shanghai, 200080, P.R.
China
Corresponding author: Kangan Li, E-mail：[email protected]
___________________________________________________________________________________________
*Corresponding author: Kangan Li, MD, PhD, Department of Radiology, Shanghai General Hospital, No. 650
New Songjiang Road, Shanghai, China. E-mail: [email protected]; Fax: 0086-21-63240825; Tel:
0086-21-37798252
Compliance with Ethical Standards: Funding: This research is financially supported by the National Natural Science Foundation of China (KAL 81972872, 11826020), the Shanghai Pujiang Project (KAL 2014PJD028), the Shanghai Jiaotong University Cross-cooperation Project (KAL YG2015MS31), the Shanghai Science and Technology Commission Project (KAL 17441900700), and the Shanghai Shenkang Project (KAL 6CR3091B). Conflict of Interest: All of the authors declare that they have no conflicts of interest to report. Ethical approval: This is a review article that does not contain any studies conducted with human participants or animals.
Abstract
Breast
density is an independent risk factor for breast cancer and significantly
decreases the sensitivity of mammography. Assessing a woman’s risk of developing breast cancer is becoming increasingly important for establishing individual screening recommendations and preventive strategies. This paper reviews the factors influencing mammographic density (MD), the available methods of MD assessment, and its effect on breast cancer. Finally, we discuss the supplemental screening methods for women with dense breast tissue.
Keywords
Breast cancer, Breast density, Mammography, Supplemental screening
1. Introduction
Breast cancer has the highest incidence and mortality rates among cancers in women worldwide. Approximately 2.1 million new breast cancer cases and 0.6 million breast cancer deaths were reported in 2018[1]. Therefore, early diagnosis and treatment play an important role. Mammography, which is the first screening method for breast cancer, provides a quick and easy method to assess breast density. In X-ray imaging, the fatty tissue appears radiolucent/black, while fibroglandular tissue, including fibroblasts, epithelial cells and connective tissues[2], is radiopaque/white. Multiple studies[3, 4] have indicated that screening mammography could reduce breast cancer-associated mortality.
Mammographic density (MD), a method of measuring the amount of fibroglandular tissue relative to fat in the breast, has been proven to be associated with the sensitivity and specificity of screening and has also been considered to be an independent risk factor for breast cancer. High breast density is a major risk factor for breast cancer. One study[5] found that women with dense breasts (>75% density) had a 4-6 times higher risk of developing breast cancer than women with fatty breasts (<25% density).
However, the standard method of evaluating breast density, the screening interval and the starting age remain uncertain. This paper reviews the available methods of MD evaluation, some of the factors that modulate MD and its association with cancer risk. Additionally, we discuss the supplemental screening methods available to women with dense breasts.
2. Factors influencing MD
MD is highly variable and is influenced by many factors during a woman’s life. It usually decreases with increasing age and body mass index (BMI), increases with hormone replacement therapy (HRT) and decreases with tamoxifen therapy. Other variables affecting breast density include diet and reproductive factors.
Age and BMI
Numerous studies[6, 7] have investigated the association between age and MD. It is estimated that MD usually decreases with age, especially during the menopausal transition period. The International Consortium on Mammographic Density (ICMD)[8] recently used a quantitative method to research differences in MD among more than 11 000 women in 22 countries by age and menopausal status. Investigators found that MD decreased with age in both premenopausal and postmenopausal women regardless of ethnicity, and the decrease was more marked during menopausal transition.
A high BMI is associated with reduced MD. Some studies[9] have also found that increased birth weight and grown-up height are positively correlated with high MD. In one investigation[10] that used data from a UK case-control study and a Norwegian cohort study, researchers found that BMI was strongly positively associated with nondense volume (NDV) but strongly negatively associated with percent mammographic density (PMD). Thus, the failure to correct for BMI may
underestimate risk. Nevertheless, previous studies[11, 12] have considered the relationship between BMI and MD and reported inconsistent results.
Exogenous hormonal exposures
Extrinsic hormones and medication impact density in different ways. HRT, which is used to relieve menopausal symptoms, leads to increased MD. Clinical trials[13] have shown that MD increases with estrogen and progestin use. Nevertheless, estrogen alone does not significantly increase MD[14, 15]. Furthermore, HRT is related to a potential increase in breast cancer risk[16]. A recent nested case-control study[17] within the Women's Health Initiative Trial (WHI) found that the risk of breast cancer increased by 3% (odds ratio (OR) = 1.03; 95% confidence interval (CI): 1.10, 1.06) with each 1% increase in MD after assigned HRT.
Conversely, tamoxifen and aromatase inhibitors (AI) lead to a decrease in MD by inhibiting the proliferation of mammary cells. Tamoxifen and AI act through the estrogenic pathway and thus affect only estrogen-receptor (ER)-positive breast cancer. In a randomized prevention trial[18], compared with the control group, women in the tamoxifen group had a significantly lower MD and a 63% lower risk of breast cancer. In a [19]study that included 213 premenopausal women with breast cancer after tamoxifen discontinuation, a mean density increase of 1.8% was found compared to that before tamoxifen discontinuation. Similar results[20, 21] on AI have also been found. Engmann et al.[22] reported significantly greater reductions in volumetric percent density among women with breast cancer treated with tamoxifen
(premenopausal) or AI (postmenopausal); the greatest reductions were observed when the baseline density was ≥10%. Therefore, we can conclude from these results that women who experience a quantitative drop in breast density on tamoxifen or AI benefit from a reduction in the risk of subsequent cancer. Furthermore, MD may have clinical implications for assessing the response to chemoprevention with selective estrogen receptor modulators and AI.
Heritability and single-nucleotide polymorphisms (SNPs)
MD has been shown to be highly heritable, and twin studies[23, 24] have shown that genes account for approximately 60% of the variation in MD. Boyd and colleagues[23] compared PMD among monozygotic and dizygotic twins, and 63% of the variation in breast density was explained by heritability in all twins studied. Another study[24] also investigated greater absolute MD and PMD in monozygotic twins than in dizygotic twins. Similarly, Brand et al.[25] found high heritability of MD using volumetric measures. These studies suggest the significance of genetic components in MD.
Numerous genetic association studies have examined the correlation between SNPs and MD. To date, SNPs in genes including AREG, ESR1, ZNF365, LSP1/TNNT3, IGF1,
TMEM184B,
SGSM3/MKL1,
8p11.23,
PRDM6,
1q12.21A,
TAB2,
CCDC170/ESR1 and 12q24 have been determined to have a significant association with MD[26, 27]. Additionally, a recent study[28] discovered three novel MD loci and demonstrated that common genetic variants account for approximately 25% of the
variation in MD and that the ratio of SNP-based to heritability varies between the absolute dense volume and NDV.
Other factors
Other modifiable factors influencing breast density include nutrition, ethnicity, air pollution, menarche and reproductive factors (e.g., age at first birth, parity; refs.). In a study[29], a western dietary pattern was related to higher MD among overweight and obese females. Another cross‐sectional study[30] indicated a clear positive correlation between MD and a higher calorie intake after controlling for covariates. A previous study[31] suggested that race/ethnicity and migration history can impact the risk for breast cancer by increasing MD. It has also been found that air pollution increases the risk of breast cancer by increasing MD. A study[32] evaluated the correlation between air pollutants and breast density and considered that women living in areas with higher levels of cobalt and lead would prefer to have higher breast density. However, this is not a consistent finding; some studies have detected a positive association[33], while others have detected no association[34].
3. MD assessment measures
Mammographic images are two-dimensional (2D) planar representations of a three-dimensional (3D) structure. There are numerous methods available for assessing MD, which can be classified as subjective visual assessment, semi-automated methods and automated methods (Table 1). Different methods of assessing MD are now being used as a surrogate endpoint for breast cancer incidence in several early
phase chemoprevention trials, and changes in MD have been considered as biomarkers for assessing risk. To date, there are no recommendations or standards for MD standardization. Therefore, developing a standardized measurement of MD for clinical implementations is desirable but represents a substantial challenge.
Wolfe[35] first proposed a categorical classification according to parenchymal patterns in 1976: N1 indicated a breast with parenchyma consisting mainly of fat and the lowest breast cancer risk. DY represents a breast with diffuse parenchyma or extensive nodular density and the highest risk of cancer. P1 and P2 represented connective tissue hyperplasia surrounding ducts of lesser and greater extent, respectively, associated with intermediate increases in risk. However, the method proposed by Wolfe was not reproducible[36-40].
Currently, the Breast Imaging Reporting and Data System (BI-RADS) developed by the American College of Radiology (ACR) is the most widely used clinical classification by radiologists. The recently updated 5th edition of the BI-RADS atlas[41] is used to standardize the reporting of visual assessment of mammograms, and it defines four categories as follows:
A) almost entirely fatty;
B) scattered areas of fibroglandular density;
C) heterogeneously dense, which may obscure small masses; and
D) extremely dense, which lowers the sensitivity of mammography.
Less dense breasts have a lower risk of breast cancer (A, B), whereas denser breasts have a higher risk (C, D) (Fig. 1). In the current edition of the classification system, this method emphasizes the potential masking effect of dense breast tissue rather than the size of the dense area; breasts that are dense enough to hide cancer in only a portion of the dense area of glandular tissue should be categorized as heterogeneously dense, prompting a discussion of supplemental screening. In findings from multiple studies[42-45], investigators have also shown the presence of significant inter- and intraobserver differences for subjective visual estimation of breast density by mammography, particularly with regard to the distinction between the two middle categories (B and C), which implies that the experience of radiologists is important but that even experience cannot overcome the subjectivity of visual assessment. Thus, qualitative assessment is an imperfect method of breast density measurement[46].
Considering
the
limitations
of
subjective
visual
assessment,
various
computer-assisted evaluation approaches are now available, ranging from semi-automated to fully automated methods. Among these, CumulusTM, a computerized model developed by Byng et al.[47], has been considered the gold standard for quantitative measurement for many years, and numerous data have shown a higher reproducibility of this method than that of BI-RADS-based visual assessment[48-51]. However, these methods require human input and training to obtain the density thresholds, which may limit their widespread clinical application. Many researchers have studied automated area-based options of MD assessment, which availably remove the human interactive component of CumulusTM and Madena.
These research tools include AutoDensity, Densitas, ImageJ, iReveal, STRATUS, Libra, and MedDensity.
However, a serious disadvantage of the area-based assessment is that it is based on 2D images, with a lack of consideration of breast thickness, and depends heavily on the operator. At the same time, breast density assessment methods based on 3D mammography have emerged. To date, the best-known automatic systems, such as Quantra (Hologic, Inc., Bedford, MA, USA) and Volpara (Matakina International, Wellington, New Zealand), allow mammography-based, volumetric, quantitative measurements of the absolute breast tissue[52-54]. Several studies[55, 56] have investigated the consistency between density assessments by radiologists and these automated methods and demonstrated a positive association between BI-RADS categories and automated assessment. Although these methods are fully automated, breast density calculations according to mammographic images may still vary depending on differences in tissue compression and breast location[57].
4. Breast density with risk of breast cancer
Breast density affects the risk for breast cancer in two primary ways. On the one hand, breast density has a masking effect leading to decreased sensitivity of mammography, and on the other hand, breast density is an independent risk factor for breast cancer.
Masking effect
Dense breast tissues can mask cancers, resulting in lower sensitivity of mammography[58-64]. Dense tissues and cancer tissues have similar X-ray attenuation properties, which appear white on mammograms. This implies that women who have higher breast density are more likely to have a high false positive rate in mammography[65]. This phenomenon is known as the masking effect.
Studies[66-68] have demonstrated that mammogram screenings cannot detect all breast cancers because the sensitivity of mammograms is determined by the breast density. Although breast density is correlated with adverse outcomes, it does not affect the risk of developing breast cancer. In a retrospective review[69] including 335 cases of breast cancers, 263 (78%) were masked by dense breast tissue on mammography, which confirmed the masking effect of dense tissue. Data obtained by a Dutch screening agency using digital mammography (DM) from 2003 to 2011[70] indicated that mammography had a sensitivity of 85.7% for women with fatty breasts. By comparison, women with extremely dense breasts had a sensitivity of 61%. The researchers also found that false positive recalls increased gradually from screening to assessment with increasing MD, from 11.2% in mostly fatty breasts to 23.8% in extremely dense breasts.
The masking effect does, moreover, lead to an increase in the incidence of interval cancers. Interval cancers are those detected due to clinical features during the interval between recommended screens[71]. Interval cancer rates increase with increasing breast density[49, 72, 73], with a more than 17-fold higher risk observed among
women with dense breasts than among women with nondense breasts or those with a personal history of breast cancer[74]. These findings demonstrate that high-density breast tissue may affect the early detection of screening mammography and therefore lead to inconclusiveness in the mammogram results of women with high-density breast tissue.
Breast density as an independent risk factor for breast cancer
Beyond the masking effect, MD is also an independent risk factor for breast cancer. Wolfe[35] first published a description of MD and breast cancer risk. Following Wolfe’s work, the relationship between MD and breast cancer has been widely investigated[75-78].
Several studies[53, 79-82] have demonstrated that breast density is a strong and independent risk factor for breast cancer. In one study[83] that included 6020 breast screening assessed cases and 1040 screened women with a family history of breast cancer, there was a positive correlation between breast density and the incidence of cancer, and the risk dense tissue increased by 3% (95% confidence interval (CI), 1-5%) per 10 cm3 by Volpara. In a meta-analysis[84] evaluating women from Asian countries, the risk calculated for PMD in premenopausal and postmenopausal women was evaluated to be 2.21 times that at baseline (95% CI, 1.52 to 3.21), suggesting that a higher risk for breast cancer is related to higher PD values among women in Asian countries. These studies indicate that there is a positive correlation between increased breast density and increased breast cancer risk.
To identify high-risk women before breast cancer, many breast cancer risk prediction models have been developed, such as the Gail model, the Breast Cancer Surveillance Consortium (BCSC) model, the Claus model, and the Tyrer-Cuzick (TC) model. In a retrospective cohort[85] of 35 921 women undergoing mammography screenings in the United States, the Gail and BCSC model had slightly higher AUCs than BRCAPRO and TC. The Breast Cancer Prospective Family Study Cohort (ProF-SC)[86] compared the 10-year performance of four models; in this cohort, BOADICEA and IBIS were well calibrated, whereas BRCAPRO and BCRAT underestimated risk in the overall validation cohort. Thus, models that incorporate additional genetic and nongenetic risk factors and estimate the risk of tumor subtypes may further improve the ability to predict breast cancer risk.
5. Supplemental screening
Not all women benefit equally from mammography. With the increasing awareness that dense breasts have an impact on the risk of breast cancer and the sensitivity of mammography screenings, a growing number of women need supplemental screening. Digital breast tomosynthesis (DBT), ultrasonography, and magnetic resonance imaging (MRI) can all be used as important supplemental tools for screening women with higher breast density. However, it is not clear whether this supplemental screening will provide beneficial effects or lead to overdiagnosis[45].
DBT
In 2011, the US Food and Drug Administration (FDA) approved DBT for all clinical
indications
accepted
for
mammography.
Tomosynthesis,
or
3D
mammography, is a digital mammographic technique in which the X-ray tube moves in an arc on the compressed breast; low-dose images are thereby obtained from multiple angles[87]. DBT can reduce the masking effect of dense breasts and reveal small breast cancer[88]. Numerous studies[89-93] have repeatedly shown that undergoing DBT screening after mammography increases cancer detection rates and reduces recall rates.
A recent publication[94] using prospective cohort data from 3 research centers showed that the cancer detection rate using DBT was higher than that using DM for all age groups (odds ratio (OR), 1.41; 95% CI, 1.05-1.89; P=0.02). The prospective Oslo Tomosynthesis Screening Trial (OTST)[95] showed that adding DBT to DM resulted in significantly improved sensitivity, from 54.1% (152 of 281) to 70.5% (198 of 281), and specificity, from 94.2% (22,632 of 24,020) to 95.0% (22,811 of 24,020). Another study[96] also showed that DBT improved cancer detection in breast cancer staging regarding the presence of additional ipsilateral lesions.
However, radiation exposure from DBT is twice that of conventional mammography. Another drawback is that the interpretation of DBT images relies heavily on the expertise of the radiologist, which leads to high variability. In addition, one study[97] showed that the use of tomosynthesis increases the rate of breast biopsy.
These factors all reflect the uncertainty of the balance between the pros and cons of tomosynthesis screening. Therefore, adoption of this technique has been limited.
Ultrasonography
Supplemental ultrasound is well accepted by patients because it does not emit ionizing radiation, regardless of breast density. Previous studies[45, 98, 99] have repeatedly shown greater breast cancer detection and sensitivity among women with dense breasts when mammography and ultrasound are combined rather than when mammography is used alone. However, the addition of ultrasound to mammogram screenings also increased false positive and benign biopsy rates[100].
In a multicenter retrospective analysis of 501 women who were followed up for 7 years, Kim et al.[101] indicated that there was a 98% 5-year recurrence-free survival rate among women with cancer detected by ultrasound. Adjunct screening with ultrasound in women with dense breasts increased breast cancer detection from 1.9 to 4.2 per 1000 screenings. The Japan Strategic Anticancer Randomized Trial (J-START)[102] investigated the advantages of adding ultrasound to mammography for screening for breast cancer. That study demonstrated a significant increase in breast cancer detection in the experimental group, with a significant reduction in interval cancers compared with the control group subjected to mammography only.
However, hand-held traditional ultrasound (HHUS) has several drawbacks, such as its high operator dependency, time-consuming nature and small field of view. Therefore, the FDA approved automated whole breast ultrasound (ABUS) as a new
screening method in 2012[103, 104], which attempts to use semiautomated techniques to evaluate breast density [105-107], but the application of these techniques in clinical practice has remained in the research phase. Some studies[108-111] comparing ABUS and HHUS have demonstrated similar lesion visualization and evaluations. One study[112] indicated that ABUS was superior to HHUS on the coronal plane by displaying architectural distortions.
MRI
Supplemental MRI has been regarded as the most sensitive imaging tool for screening breast cancer because it is not influenced by breast density and provides more information on breast masses, including lesion vascularity. The National Comprehensive Cancer Network (NCCN) guidelines[113] recommended MRI as a supplemental screening tool for high-risk women beginning at 25–29 years of age as well as those who have prior atypical biopsy or lobular carcinoma in situ.
In a multicenter, randomized, controlled study, Bakker et al[114] evaluated supplemental MRI screening among women in the Netherlands between the ages of 56 and 75 years with extremely dense breast tissue. Their study showed a significantly lower interval cancer rate of the MRI-invitation group than the mammography-only group (2.5 vs. 5.0 per 1000 screenings). A recent study[115] found that among women with an average risk for breast cancer, MRI could detect 15.5 additional cancers per 1000 women and had a specificity of 97.1% and a positive predictive value (PPV) of 35.7%. In addition, the interval cancer rate among women
who underwent MRI screening dropped to zero. In the ACRIN 6666 study, Berg et al[116] indicated that MRI plus mammography has a higher cancer yield and a lower false positive rate than ultrasound plus mammography, thus confirming that screening with MRI is better at detecting breast cancer than the combination of mammography and ultrasound.
Nevertheless, MRI is relatively expensive and time consuming, requires intravenous gadolinium injection and may increase the false positive rate. To address these challenges, recent studies[117, 118] have focused on techniques that have the potential to enhance the productivity of breast MRI, reducing the acquisition and reading times, without affecting the diagnosis. This abbreviated protocol can acquire images in less than 10 minutes and can be explained as quickly and easily as a screening mammogram.
6. Conclusions
Breast density has become a popular yet controversial topic in the field of breast imaging. MD is dynamic and modifiable, and there are many factors affecting MD throughout a woman’s lifetime, including age, BMI, and HRT. Mammography, as a routine examination method, can be used to assess MD and decrease the incidence and mortality rates for breast cancer. This review emphasizes that MD is an independent risk factor for breast cancer and that it is necessary to define a standard assessment to measure MD. Earlier assessment methods have shown limited consistency between observers and in relation to breast cancer risk. The application of
automated computer-based density measurements is beneficial because these methods have been proven to provide consistent and objective results that can be applied in large-scale clinical research. However, the sensitivity of breast cancer detection decreases with increasing breast density. To improve the sensitivity of breast cancer detection for women with dense breasts, physicians should consider the cost, potential hazards, and important outcomes and then work with patients to tailor the most appropriate multimodal screening strategies based on the patient’s risks, preferences, and values. MD assessment Visual
Semiautomated
Fully automated
Area
Area
Area
Method
FDA approval
Study
Wolfe
Not applicable
Wolfe (1976) [35]
TABAR
Not applicable
Gram et al. (1997)[119]
Qualitative
BI-RADS
Not applicable
Sickles et al. (2013) [41]
Semi qualitative
Boyd
Not applicable
Boyd et al. (1995) [120]
Visual analogue scale
Not applicable
Duffy et al. (2008) [121]
CumulusTM
No
Byng et al. (1994) [47]
Madena
No
Ursin et al. (1998) [122]
Qualitative
DenSeeMammo
FDA approved to provide information on 5th edition BI-RADS density categories
Quantitative
AutoDensity
No
Parenchymal pattern
Quantitative
Developed by the University
of Melbourne
Volume
Quantitative
Densitas
No
Abdolell et al. (2016) [123]
ImageJ
No
Li et al. (2012) [124]
iReveal
Yes
[125]
STRATUS
No
Eriksson et al. (2016) [126]
Libra
No
Keller et al. (2015) [127]
MedDensity
No
Tagliafico et al. (2013) [128]
BDSXA
No
Shepherd et al. (2005) [129]
Cumulus V
No
Alonzo-Proulx et al. (2010) [130]
Quantra
Yes
[131]
Spectral density
Yes
[132]
Volpara
Yes
[133]
Table 1. Mammographic density (MD) assessment methods.
Figure 1. Example images of the four breast density categories defined by the 5th edition of the BI-RADS mammography atlas.
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