Imaging of radiographically dense breasts represents a diagnostic challenge for interpreting radiologists. In addition to the increased density resulting in obscured malignancies, patients with dense breasts have increased incidence of breast cancer. The author reviews the use of CAD, ultrasound, mammography, MRI, and nuclear imaging and their effectiveness in imaging of dense breasts.
is an Assistant Professor in the Department of Radiology, H. Lee
Moffitt Cancer Center and Research Institute at the University of
South Florida, Tampa, FL.
Imaging of radiographically dense breasts represents a
diagnostic challenge for interpreting radiologists. Breast cancer,
especially noncalcificed breast cancer, is more likely to be missed
in dense breasts than in radiologically fatty breasts.
In addition to the decreased visibility of the lesions secondary to
the increased density of the breast tissue, there is probably an
independent increased risk of malignancy in dense breasts.
Because of the increased risk of cancer and the difficulty in
recognizing lesions, various methods to improve the chance of
visualizing malignancies have been studied. These include, digital
imaging, computer-aided detection (CAD), ultrasound, magnetic
resonance imaging (MRI), sestamibi (Bristol Myers Squibb,
Billerica, MA) and positron emission tomography (PET) imaging.
Definition of mammographically dense breasts
Dense breast tissue has been defined in at least three different
* Four progressively more dense patterns, first defined by John
Wolfe, MD, and now referred to as Wolfe patterns (N1, P1, P2,
* Four progressively more dense patterns defined by the American
College of Radiology (ACR) Breast Imaging Reporting and Data system
(BIRADS; ACR, Reston, VA): 1) almost entirely fat; 2) scattered
fibroglandular densities that "could obscure a lesion"; 3)
heterogeneously dense that "may lower the sensitivity of
mammography"; 4) extremely dense that "lowers the sensitivity of
* Percentage of parenchymal tissue density (compared with fat
density) on the craniocaudal mammogram, as measured manually by
planimetry, or by computer software programs.
For clinical purposes, most descriptions of breast density today
use the BIRADS terminology.
Kolb et al
has further clarified the BIRADS definitions based on the amount of
fat "interspersed within the area of the densest fibroglandular
tissue" and the size of the area encompassed by the tissue. His
* Grade 1: Having no areas of tissue that could obscure
* Grade 2: Having at least one area of tissue that could obscure
* Grade 3: Having tissue that can obscure cancer in 50% to 75%
of the breast.
* Grade 4: Having tissue that can obscure cancer in >75% of
In general, dense breasts are considered BIRADS categories 3 and
4. The decreased visibility of masses and calcifications is related
to decreased contrast. This results in obscuration of subtle
abnormalities and increased risk of not visualizing
Reproducibility of dense breast categorization
Several studies have shown wide variability in radiologists'
ability to categorize breast density using Wolfe's classification
with reliability and reproducibility. Few studies have evaluated
radiologists' variability in using the BIRADS system.
However, because of the wide variability in classification of
breast density, Wolfe first suggested use of planimetry and showed
the validity and reproducibility of the process.
In an attempt to standardize the classification of breast density,
Byng et al
has developed a computerized method to categorize the density of
breast tissue. A single craniocaudal view is digitized; the
radiologist selects the edge of the breast and the regions of
density, and the computer calculates the percent density. This may
be useful in the future if additional imaging modalities are found
to be beneficial in patients with dense breasts. Computer programs
can accurately and reproducibly calculate the percentage of breast
density in any digital mammogram.
Breast density and breast cancer risk
The increased risk of breast cancer in dense breasts has been
reported to be between 1.8 and 6 times higher than in normal
breasts. Boyd et al
studied 571 monozygotic and 380 dizygotic twins and concluded that
twins with dense breasts had a four-fold increased risk of
malignancy when compared with twins with less dense breasts.
Factors that affect breast density include age, hormonal therapy,
diet, menopause, weight, number of live births, and genetics.
Hormonal therapy has been shown to affect breast density.
Greendale et al
have reported that breast density increases 4.6% to 4.8% when
estrogen and progesterone are given together. A similar increase
was not seen with estrogen alone. Carney et al
studied 463,378 mammograms and found that hormonal therapy
increased breast density and decreased the sensitivity of
mammography. Hormonal therapy was not an independent predictor of
accuracy in mammography, but only influenced accuracy based on the
increased density of the breast tissue.
have compared the effect digital imaging (DI) has on the
classification of the density of breast tissue and the effect it
has on detecting lesions. Venta
studied 692 patients and Lewin
495 patients with both film screen (FS) and digital mammography.
showed no difference in the number of patients reported as having
dense breasts versus nondense breasts. Venta showed a slightly
decreased number of patients reported to have dense breasts on DI
when compared with FS. Lewin also showed that the recall rate was
slightly greater for film screen than digital imaging (13.8% FS
versus 11.5% DI). Although the positive predictive value for
digital mammography was reported as 30%, and 19% for FS, this was
not statistically significant.
Although CAD has been developed to improve the rate of
diagnosing early cancers, its effectiveness in dense breasts has
also been investigated. Brem et al
have studied 1059 cases with the CAD system (version 4.0, CADx
Medical Systems, Quebec, Canada). They considered fatty and
scattered fibroglandular densities as nondense breasts and
heterogeneously dense and extremely dense breasts as dense in their
analysis. Computer-aided detection was able to detect 89% of all
cancers with no significant difference between the cancers detected
in dense and nondense breasts.
Ho et al
studied 264 bilateral examinations with CAD (Second Look, Version
1.1, CADx Medical Systems) and found a statistically significant
decrease in the sensitivity of CAD in dense breasts. Computer-aided
detection identified approximately 94% of abnormalities in grade 1
and 2 breasts, but this decreased to 85% in Grade 3 breasts and 64%
in Grade 4 breasts.
reviewed 677 cases of breast cancer manifesting as masses on
mammography and evaluated them with CAD (Image Checker M1000 V2.0;
R2 Technology, Los Angeles, CA). There was no significant
difference in the performance of CAD in dense versus fatty breasts.
Sensitivity ranged from 71% to 78%.
Birdwell et al
retrospectively reviewed 110 cases of breast cancer. Dense breasts
were believed to be the cause of missed lesions in 34% of the
cases. Computer-aided detection (R2 Technology V2.0; R2 Technology)
marked 30 (86%) of the missed calcifications, 58 (73%) of the
missed masses, and 62% (grade 1) to 86% (grade 3) of malignancies.
There was no significant difference in the performance of CAD in
dense and nondense breasts (
The discrepancies in the studies may be related to the earlier
version of software and the small number of patients studied by Ho
Evaluation of the newer software shows no significant difference in
the performance of the CAD systems in dense and fatty breasts.
Ultrasound has been suggested and studied as a way to increase
the detection of malignant lesions in dense breasts. Kolb et al
has recently studied the sensitivity of mammography, ultrasound,
and physical examination on the detection of breast cancer and
compared the results in breasts of different densities. He studied
11,130 women undergoing 27,825 screening exams. After studying the
first 700 patients with fatty breasts and finding no additional
cancers detected by ultrasound, no further fatty breasts underwent
ultrasound. He concluded that physical examination was slightly
more sensitive in dense breasts (35%) than in fatty breasts (22%).
While mammography was 98% sensitive in fatty breasts, this
decreased to 48% in grade 4 breasts. When grades 2 through 4 were
analyzed, the sensitivity for mammography was 64%. The sensitivity
of ultrasound averaged 75% in patients with dense breasts (grades 2
through 4). When ultrasound and mammography were combined, there
was 100% sensitivity in grade 2 breasts. The combined sensitivity
of mammography and ultrasound in grades 2 through 4 was 97%.
Crystal et al
studied 1517 women who had dense breasts with normal mammograms and
physical examinations. They detected no additional cancers in the
category 2 breasts (156 patients). Five additional malignancies
were identified in category 3 breasts (1149 patients), and 2
additional malignancies were detected in grade 4 breasts (212
patients). While the number of additional cancers identified was
small, there was a significant difference between low- and
high-risk patients. They showed a statistically increased incidence
of detecting cancers when screening high-risk patients with both
ultrasound and mammography (1.3%) when compared with baseline risk
also examined 4236 patients with both ultrasound and mammography.
They noted that mammography detected 80% of cancers in grade 1 and
2 breasts, while sonography detected 88% of cancers. In grades 3
and 4, the sensitivity of mammography decreased to 56%, while
sonography remained stable at 88%.
These studies suggest that ultrasound is useful in detecting
malignancies in dense breasts, especially in women who are at high
risk for breast cancer. An example of malignancy detected by
ultrasound and not seen on mammography is shown in Figure 1.
Multiple investigators have studied the effectiveness of breast
MRI in diagnosing breast cancer. These studies included high-risk
patients who have been diagnosed with one cancer and patients with
increased familial risk. Many of these patients have dense breasts
on mammography, making screening difficult.
studied the ipsilateral breast in 70 patients who had breast
cancer. They found additional lesions in patients with dense
breasts (grades 2 through 4) and no additional lesions in fatty
breasts. Additional lesions were found in the same quadrant in 20%
of patients, different quadrants in 4% of patients, and both the
same and different quadrants in 3% of patients. This additional
information resulted in changes in treatment in 7% of cases.
studied 104 women with suspicious lesions on mammography. These
women underwent MRI and ultrasound. Mammography correctly diagnosed
48% of the patients. Mammography and ultrasound correctly diagnosed
63% of patients and MRI diagnosed 81% of the patients. A total of
13 patients had malignancies identified on ultrasound that were not
visualized on mammography, and these were shown to have a
significant increase in breast density when compared with those
diagnosed on mammography. Seven additional lesions were found on
MRI when compared with the combination of mammography and
ultrasound, and these also had a significant increase in breast
density. In an additional 13 patients, the MRI images provided the
best images of the tumor. The addition of ultrasound as an imaging
modality resulted in unnecessary surgeries in 2% of the patients.
When MRI was compared with the combination of mammography and
ultrasound, an additional 8% of patients underwent unnecessary
Lee et al
evaluated the contralateral breast in 182 patients with breast
cancer and found additional lesions identified in dense breasts.
Additional true-positive lesions were found in 28% of grade 2
density breasts, 57% of grade 3 density breasts, and 14% of grade 4
density breasts. No additional lesions were found in fatty
Various studies have been completed in high-risk patients.
evaluated 109 high-risk patients whose breasts were 50% dense on
mammography with both MRI and mammography. Additional cancers were
found in 3 (2.8%) patients and false-positive results were seen in
6 patients. Stoutjeskdijk
studied 179 high-risk patients with mammography and MRI and found
cancer in 7 additional patients. Warner et al
evaluated the sensitivity and specificity of physical examination,
mammography, and sonography in 196 high-risk patients. They found
that MRI was 100% sensitive with a positive predictive value (PPV)
of 26%, while ultrasound had a sensitivity of 60% and PPV of 19%.
Mammography had a sensitivity of 33% and a PPV of 66%. Kuhl
studied 192 high-risk patients and also found MRI to be 100%
sensitive with a PPV of 75%. Mammography and ultrasound together
had a sensitivity of 53%. Mammography alone had a sensitivity of
44% and a PPV of 50%.
The above studies indicate that MRI has been shown to locate
additional lesions. It appears to have a sensitivity between 80%
and 100% in various studies. Further studies are needed to more
accurately characterize the lesions detected by MRI to prevent
unnecessary biopsies and allow lesions, which are localized by MRI,
to be biopsied. Figure 2 presents an example of a lesion identified
on MRI, but not on the mammogram.
Radionuclide imaging modalities that have been evaluated in
dense breasts include sestamibi and PET scanning. Khalkhali et al
has compared the sensitivity of sestamibi in women with dense and
fatty breasts and found similar sensitivities (71%) and
specificities (79%) between the two. Accuracy was also similar in
both fatty and dense breasts (76%).
The total-body PET imaging studies that are currently being
performed will not likely have use in diagnosing malignancies since
lesions <1 cm are not easily detected. Coleman et al has done
some experimental work with PET/mamography scanning by which
smaller lesions have been visualized (R. Edward Coleman, MD,
personal communication, May 2003). This may prove useful for
diagnosing malignancies in the future.
Patients with dense breasts represent a diagnostic dilemma. In
addition to the increased density resulting in malignancies being
obscured, patients with dense breasts have increased incidence of
breast cancer. Since mammography has been shown to have a decrease
in sensitivity from 98% in fatty breasts to 42% in dense breasts,
additional imaging modalities need to be investigated to improve
the diagnostic accuracy of imaging in breast cancer. The accuracy
of CAD appears similar in dense and nondense breasts and may prove
useful. When ultrasound and mammography have been used in
combination, the sensitivity in dense breasts appears to increase
to 97%. MRI has been reported to increase sensitivity, but also
reveals more non-malignant lesions. While sestamibi is equally
effective in dense and fatty breasts, its accuracy remains low.
PET/mammography may prove useful in the future. Further studies are
needed to determine the best additional modalities to use to
evaluate women with dense breasts.