Breast density has been shown to be a strong, independent risk factor for breast cancer. Unfortunately, mammography is less accurate on dense breast tissue compared to fattier breast tissue. Multiple studies suggest a solution to this by demonstrating the ability of supplemental screening ultrasound to detect additional malignant lesions in women with dense breast tissue but negative mammography. In particular, supplemental screening ultrasound may be beneficial to women with dense breast tissue and intermediate or average risk for breast cancer, women in specific ethnic populations with greater prevalence of dense breast tissue, and women living in resource-poor healthcare environments. Although magnetic resonance imaging is currently recommended for women with high risk for breast cancer, not all women can access or tolerate a magnetic resonance imaging examination. Notably, ultrasound does not require intravenous gadolinium and may be an alternative for women with socioeconomic or medical restrictions, which limit their access to magnetic resonance imaging. Limitations of supplemental screening ultrasound include a substantial rate of false-positives, increased cost, and limited resource availability, particularly in regard to the time required for image interpretation. Additional clinical experience with this application of ultrasound, improved patient selection criteria, and new technology, such as the promising results seen with automated whole breast ultrasound, may address these limitations. In light of recent legislation in some states that has called for discussing supplemental imaging with patients who have dense breast tissue, the optimal role for supplemental screening ultrasound merits further exploration.
Introduction
Worldwide, breast cancer is the second most common cancer and a leading cause of cancer death among women . Meta-analysis of randomized controlled trials has demonstrated decreased mortality after the implementation of mammography in women age 39–69. However, conflicting estimates of the impact of screening mammography on breast cancer-related and all-cause mortality have incited criticism that screening mammography has contributed to breast cancer overdiagnosis . As a result, further elucidation of the best imaging practices for breast cancer screening is being intensely researched. Well-established risk factors for breast cancer have been incorporated into models for clinical decision making. The Gail model is the most widely utilized validated tool, which incorporates current age, age at menarche and first parturition, race and ethnicity, family history of breast cancer in first-degree relatives, number of prior breast biopsies, and biopsy findings of atypical hyperplasia . Increased risk determined by the Gail model guides the prophylactic use of risk-reducing medications and the earlier use of clinical breast examinations and screening mammography. Increasingly, the use of risk factors to guide appropriate screening regimens is being explored. For example, screening with contrast-enhanced magnetic resonance imaging (MRI) is currently recommended by the American Cancer Society as a supplement to mammography in patients with greater than 20% lifetime risk of breast cancer (as assessed by either the Gail model or the BRCAPRO model), with known BRCA1/2 mutations, first-degree relatives of known BRCA1/2 mutation carriers, other cancer-associated genetic mutation carriers, and with chest radiation exposure between the ages of 10 and 30 .
Breast density has been shown to be a strong, independent risk factor for breast cancer. Breast density can be assessed through mammography and is described most frequently with the Breast Imaging Reporting and Data System (BI-RADS) classification. The BI-RADS lexicon includes four categories, which refer to the percentage of breast tissue that is fibroglandular: (1) almost entirely fatty, (2) scattered fibroglandular, (3) heterogeneously dense, and (4) extremely dense . Apart from age and specific genetic mutations, breast density is the strongest risk factor. In fact, women with extremely dense breast tissue are between four and five times more likely to develop breast cancer than women with predominantly fatty breast tissue, an association that remains strong across age groups .
The impact of breast density on breast cancer mortality is twofold: it remains an inherent risk for developing breast cancer after adjusting for other associated risk factors and also complicates cancer detection through screening mammography . Full field digital mammography has inherent limitations in imaging dense breast tissue because greater superimposition of tissue can make lesions more difficult to visualize. Technological advances in mammography have improved the ability of this modality to detect cancer in women with dense breast tissue. Specifically, digital breast tomosynthesis (DBT) has been utilized to overcome the limitations of standard full field digital mammography in dense breast tissue. Studies of DBT have demonstrated improved cancer detection and significantly reduced recall rates with the addition of DBT to mammography for women with dense breasts . Still, both ultrasound and MRI have a higher sensitivity than DBT for dense breast tissue (DBT: 87.4%; ultrasound: 91.6%; MRI: 98.3%). Also, the ASTOUND trial ( Table 1 ), the largest prospective study to date for women with dense breast tissue and negative mammography, recently demonstrated increased cancer detection with supplemental ultrasound compared to DBT (ultrasound: 7.1 per 1000 women; DBT: 4.0 per 1000; P = 0.006), with a similar false-positive recall rate (ultrasound: 2.0%; DBT: 1.7%) . The combined use of DBT with ultrasound is being explored as a means to improve the recall rates observed with ultrasound screening for women with dense breasts , as DBT has been demonstrated to improve specificity. However, a recent retrospective study found that the improvement in recall rate observed with DBT is negated by the addition of ultrasound regardless of breast density classification . In a complementary role, DBT is currently being investigated as a method for assessing breast density and may allow for more accurate density assessments than standard full field digital mammography, allowing centers that utilize DBT to more effectively identify women with increased breast density for supplemental screening protocols .
Table 1
ASTOUND Trial Comparison of Supplemental Screening in Addition to Mammography
Supplemental Modality Cancer Detection Per 1000 Examinations False-positives (%) DBT 4 1.70 Ultrasound 7.1 2.00
DBT, digital-based tomosynthesis.
Supplemental ultrasound detects more cancers than supplemental DBT with a similar false-positive rate .
In light of the challenges of imaging dense breast tissue with mammography, consideration of breast density in screening recommendations likely has potential to improve the sensitivity and specificity for detecting malignant lesions. Observational studies suggest that patients who will benefit from screening breast MRI in addition to screening mammography can be identified by considering the patient’s breast density category relative to her Gail model percentage . At present, no large randomized clinical trial has completed investigating the effects of supplemental MRI screening in women with dense breasts, but the ongoing DENSE trial is expected to address this question in the coming years . The American Cancer Society (ACS) currently recommends the use of supplemental screening MRI in women with high risk for breast cancer, including women with dense breast tissue. However, the ACS does not consider increased breast density alone as a sufficient indicator for supplemental screening MRI . The optimal supplemental imaging modality for women with dense breast tissue and average to intermediate risk for breast cancer remains a topic of much study and debate.
Advantages of Supplemental Screening Ultrasound in Patients with Dense Breast Tissue
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Specific Populations of Women with Potential to Benefit From Supplemental Screening Ultrasound
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Table 2
Performance of Ultrasound in Chinese Women
Modality Cancer Detection Per 1000 Screening Examinations Sensitivity (%)P Value (Sensitivity) Specificity (%)P Value (Specificity) Cost to Detect One Cancer Ultrasound 1.51 100 0.04 100 0.51 $7876 Mammography 0.72 57.10 N/A 99.90 N/A $45,253 Both 2.02 N/A N/A N/A N/A $21,599
N/A, not available.
Compared to mammography, ultrasound was superior in detecting cancer and was significantly more sensitivity ( P = 0.04) across 14 breast imaging centers throughout China, as reported by Shen et al. Ultrasound was also substantially more cost-effective. Increased prevalence of dense breast tissue in this population may account for the superior performance of ultrasound .
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Table 3
Supplemental Ultrasound Screening in Asian Populations
Study Population Geography Number of Patients Enrolled Sensitivity (Ultrasound) (%) Sensitivity (Mammography) (%)P Value for Sensitivity Additional Cancer Detection per 1000 Examinations Shen et al. China 13,339 100 57.10 0.04 1.3 Leong et al. Singapore 141 100 N/A N/A 14 Chae et al. Korea 20,864 100 54.50 0.002 2.5
N/A, not available.
In multiple studies in Asian women, ultrasound detects additional cancer when utilized in addition to screening mammography and has significantly increased sensitivity compared to mammography .
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Supplemental Screening Ultrasound for Women with Average or Intermediate Risk of Breast Cancer
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Table 4
Connecticut Experiment Compared to the Prospective ACRIN 6666 Study
Authors Study Sensitivity (%) Specificity (%) PPV (%) Additional Cancer Detection per 1000 Examinations Weigert et al. Connecticut experiment 96.60 94.90 6.70 3.25 Berg et al. ACRIN 6666 76 84 16 3.70
PPV, positive predictive value.
Supplemental screening ultrasound detected additional breast cancers in women with dense breast tissue, with similar rates reported in both the randomized, prospective ACRIN 6666 study and the multicenter, retrospective Connecticut Experiment following the implementation of breast density reporting laws in that state .
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Limitations of Supplemental Screening Ultrasound
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Conclusions
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