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Research Article
Effect of Baseline Breast Density on Breast Cancer Incidence,
Stage, Mortality, and Screening Parameters: 25-Year
Follow-up of a Swedish Mammographic Screening
Sherry Yueh-Hsia Chiu
1
, Stephen Duffy
2
, Amy Ming-Fang Yen
3,4
, Laszlo Tabár
5
,
Robert A. Smith
6
, and Hsiu-Hsi Chen
3,4,7
Abstract
Background: We aimed to quantitatively assess the effect of baseline breast density on the incidence, stage,
and mortality, and also the natural course of the disease, considering the sensitivity of mammography to
clarify its causal or masking effect.
Methods: In total, 15,658 women ages 45 to 59 years from the Kopparberg randomized controlled trial in
Sweden were prospectively followed from 1977 until 2004 to ascertain breast cancer incidence and death.
Dense breast tissue collected at the beginning of the study was defined as pattern IV or V by the Tabár clas-
sification. Conventional risk factors were also collected at baseline. The three-state Markov model was used to
estimate the preclinical incidence rate and the mean sojourn time given the fixed sensitivity.
Results: Dense breast tissue was significantly associated with breast cancer incidence [relative risk (RR)
= 1.57 (1.18-1.67)] and with breast cancer mortality [RR = 1.91 (1.26-2.91)] after adjusting for other risk
factors. Cumulative incidence rates irrespective of nonadvanced and advanced breast cancer were higher
in dense breast tissue compared with nondense tissue but no difference in survival was detected between
dense and nondense breast tissue. Dense breast tissue had a higher preclinical incidence rate (causal effect)
and shorter mean sojourn time (masking effect) compared with nondense breast tissue by controlling the
sensitivity of mammography.
Conclusion: We corroborated the effect of baseline breast density with a higher incidence and mortality
and also showed its contribution to a masking effect with long-term follow-up data.
Impact: Results suggest that the screening policy with a predominantly shorter screening interval and with
alternative imaging techniques might be indicated in women with dense breast tissue. Cancer Epidemiol
Biomarkers Prev; 19(5); 1219–28. ©2010 AACR.
Introduction
The positive association between breast density and
breast cancer risk has been reported by a meta-analysis
study (1) and several well-designed longitudinal studies
in recent years (2-6). The underlying biological role is
upheld by its associated factors including younger age
(7), premenopausal status, exogenous hormone use (8, 9),
conventional risk factors (10), and genetic influence
(11, 12), possibly through mutagenesis and mitogenesis
mechanisms (13, 14). In addition to biological causal
effects, dense breasts also have a masking effect that
leads to a high rate of interval cancers due to a lower
sensitivity, particularly in young women (5, 15).
In spite of these findings, very few studies have eluci-
dated the effect of mammographic density measured at
baseline (prediagnostic mammograms) on incidence,
stage, mortality, and mammography screening sensitivity
related to masking effects using very long follow-up data.
Doing so is helpful for predicting incidence, stage, and
Authors' Affiliations:
1
Department and Graduate Institute of Health Care
Management, Chang Gung University, Tao-Yuan, Taiwan;
2
Cancer
Research UK Centre for Epidemiology, Mathematics and Statistics,
Wolfson Institute for Preventive Medicine, London, United Kingdom;
3
Division of Biostatistics, Graduate Institute of Epidemiology and
4
Centre of Biostatistics Consultation, College of Public Health, National
Taiwan University, Taipei, Taiwan;
5
Department of Mammography, Falun
Central Hospital, Falun, Sweden;
6
American Cancer Society, Atlanta,
Georgia; and
7
Tampere School of Public Health, University of Tampere,
Tampere, Finland
Author Contributions: S. Yueh-Hsia Chiu contributed to the analysis of
data and the drafting of the manuscript. S. Duffy contributed to the sta-
tistical analysis, interpretation, and revision of the manuscript. A.M-F.
Yen contributed to statistical analysis and the programming of the likeli-
hood function. L. Tabár contributed to the study design, data collection,
and interpretation of results. R.A. Smith made a contribution to the inter-
pretation of the findings and revision of the manuscript. H-H. Chen con-
tributed to the overall framework of the manuscript, study design,
statistical analysis, and revision of the manuscript. All authors have read
and approved the manuscript.
Corresponding Author: Hsiu-Hsi Chen, Division of Biostatistics, Gradu-
ate Institute of Epidemiology/Centre of Biostatistics Consultation, Col-
lege of Public Health, National Taiwan University, Room 533, no. 17,
Hsuchow Road, Taipei, 100, Taiwan. Phone: 886-2-33668033; Fax:
886-2-33668042. E-mail: chenlin@ntu.edu.tw
doi: 10.1158/1055-9965.EPI-09-1028
©2010 American Association for Cancer Research.
Cancer
Epidemiology,
Biomarkers
& Prevention
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death associated with breast cancer among women with
dense breasts even as their breast tissue composition
changes with time. To separate biological factors from
the masking effect, we proposed an alternative approach
by constructing a three-state disease natural history
model [true normal, preclinical screen-detectable phase
(PCDP), and clinical phase; see Fig. 1] supported by the
corresponding data with mammographically normal,
mammographically detected, and symptomatic cancers
such as interval cancer, respectively, to estimate the two
parameters, preclinical incidence rate (the rate of entering
into the PCDP), and the mean sojourn time (MST; average
duration of staying at the PCDP phase) given the sensi-
tivity of mammography.
The purpose of this study was first to assess the
cumulative incidence of all breast cancer and advanced
cancers (defined by node positive, tumor size >2 cm in
diameter or histologic grade of ≥2), case fatality, and
population mortality from breast cancer by the density
of breast tissue. We then estimated the preclinical inci-
dence rate, age-specific MST, and the average duration
of the preclinical detectable period by breast density
based on the three-state disease natural history model
in a screened cohort of 15,658 women ages 45 to 59
years at entry. Using this information, we addressed
the following two questions. Is the preclinical incidence
rate higher in dense breast that nondense breast? Does
dense breast tissue have a shorter MST than nondense
breast tissue given a constant sensitivity?
The high preclinical incidence rate has been postu-
lated to be a reflection of an increased risk of breast
cancer (causal effect) and the shorter MST makes the
detection of breast cancer by mammography difficult
(masking effect).
Subjects and Methods
Study subjects and methods of cancer detection. The
study population was composed of women ages 45 to
59 years at entry in the Kopparberg randomized con-
trolled trial, one of the Swedish Two-County Trials for
mammography screening for breast cancer that began
in 1977. The details of study design and main results
were included in the first article published in 1985 (16).
A series of successive follow-up results on the compari-
son of mortality between the invited and the uninvited
groups have been described in full elsewhere (17-19). In
brief, women ages 40 to 74 years were randomized into
invitation for screening (active study population) or no
invitation (passive study population) between 1977 and
1981. During the trial period, women in the active study
population provided information on breast density while
they underwent mammographic examination. Note that
after the trial, women enrolled in this study were conti-
nuously invited to have periodic service screenings until
70 years of age. Breast cancer cases and deaths were
therefore ascertained by prospectively following the en-
rolled women over time until 2004 with an average of
25 years of follow-up.
We restricted our analysis to the 16,703 women ages
45 to 59 years, as estimation was very unstable in the
40- to 44-years age group due to small numbers of
Figure 1. The three-state natural
history model and the observed
data from population-based
screening with mammography for
breast cancer.
Chiu et al.
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cases; <4% of women ages 60 years and older were in
the dense category. Of 16,703, 15,658 (80.6%) women, in-
cluding 4,664, 5,181, and 5,813 for women ages 45 to 49,
50 to 54, and 55 to 59 years, respectively, had complete
information on the classification of their first mammo-
graphic density pattern. This formed the data set for
the following analysis. All breast cancer cases detected
by different methods were classified as three detection
modes, mammographically detected cancers, interval
cancer, and refuser cases, all of which are regarded as
clinically detected modes (Fig. 1). The first is defined
as participants who had breast cancers detected with
mammography upon invitation. This mode is further di-
vided into first and subsequent screen-detected cases. The
second is symptomatic cancers that occurred due to clin-
ical symptoms or signs in between two regular screens
among participants. The third is related to breast cancers
diagnosed due to clinical symptoms and signs among
nonparticipants.
Classification of breast density and conventional risk
factors. At the inception of study, low-dose film-screen
mammography with a mediolateral oblique view was
applied to all participants in all of Kopparberg County
(20, 21) by way of mobile units. Examinations were im-
plemented by three technicians and read by the profes-
sional radiologist of the Department of Mammography
at the Central Hospital in Falun, Sweden, pursuant to
the standard procedure set up by Tabár (22). Breast den-
sity was collected at the beginning of the screening and
classified as dense (Tabár patterns IV and V) or non-
dense (Tabár patterns I-III; ref. 23). The Tabár patterns
IV and V correspond to Wolfe patterns P2 and DY,
excluding QDY (7). In addition, information on age at
menarche, body mass index (BMI), age at first full-term
pregnancy, and menopausal status was obtained from
the active study population members with a question-
naire at the beginning of screening.
Natural history model, sensitivity of mammography,
and observed data. By superimposing the findings of
mammographies and different methods of detecting
cancers into the temporal natural history of breast can-
cer, Fig. 1 shows the observed data (the square symbols)
and three states of disease natural history (ovals) includ-
ing “true normal,”“PCDP,”and “clinical phase.”Three
observed data sets correspond to three true states, in-
cluding “mammographically normal,”which was com-
posed of “true normal”and “false-negative cases missed
at screening,”mammographically detected cases, and
clinically detected cases (including interval cancers,
false-negative cases, and newly incident cases, and the
refuser cases). Mammographically detected cancers from
the initial and subsequent screenings provide informa-
tion about the preclinical incidence rate (λ
1
), sensitivity
of mammography, and MST of staying at the PCDP
phase. Interval cancers provide information on the pre-
clinical incidence rate, the transition rate from the PCDP
to the clinical phase (λ
2
), and the sensitivity of mam-
mography. The refuser cases contribute to the preclinical
incidence rate and the transition rate from the PCDP to
the clinical phase without being affected by the sensiti-
vity of mammography.
Statistical analysis. All subjects in this study were fol-
lowed over time until the end of 2004. Outcomes of
breast cancer incidence and mortality were ascertained
through the linkage of our main data set with the cancer
registry and the trial committee report. Incidence of and
mortality from breast cancer were analyzed by Poisson
regression (24). Survival analysis was done using the
Cox proportional hazards regression model (25). Re-
garding age adjustment in the multivariable regression
model, due to our prospective study design with breast
density classified at baseline, age at recruitment was
used in the Poisson regression analysis whereas the age
at diagnosis was used in the Cox proportional hazards
regression model.
For the three-state natural history model delineated in
Fig. 1, estimating the two parameters, the preclinical in-
cidence rate (λ
1
) and the MST (1/λ
2
), is of great interest,
along with the average duration of staying at PCDP,
making allowances for sensitivity by making full use of
data on mammographically detected cancer, interval can-
cers, and cancers from the refuser group based on the
Chen et al. method (26). The former parameter was used
to assess the effect of breast density on the increased risk
of breast cancer (causal effect) and the latter was used to
clarify whether dense breast tissue might make the detec-
tion of cancer by mammography difficult (masking ef-
fect), following the two hypotheses described above.
The upshot for the link between the shorter MST and
masking effect of dense breast is that because the MST
represents the transition rate of progression from the
PCDP to the clinical phase, it is not only determined by
the underlying growth rate of breast tumors, but also can
be affected by the sensitivity of the screening tool. Be-
cause sensitivity is correlated with MST (see Fig. 1),
disentangling both parameters only on the basis of the
directly observed data is very difficult, as underscored
in Fig. 1. Given a specific growth rate of breast tumors,
the higher the sensitivity of the screening tool (that is, ad-
vanced imaging technique), the longer the MST and the
earlier the detection of breast cancer would be achieved
and vice versa. On the other hand, given a specific
screening tool, the more rapidly growing cancer in dense
breast tissue, the shorter the estimated MST would be.
This suggests that the variation in sensitivity and natural
growth rate that leads to a masking effect related to
breast density is shared with each other. Both variations
can be reflected by the estimated MST when the sensiti-
vity is controlled or by estimating sensitivity when the
MST is fixed in either way. The technique for estimating
one parameter by controlling other parameters has the
advantage of reducing identifiable problems due to a cor-
relation between MST and sensitivity. We estimated the
preclinical incidence rate and the MST by age and density
by adjusting sensitivity with the parameter that was
estimated by applying the conventional proportional
Incidence and Mortality of Breast Cancer by Density
Cancer Epidemiol Biomarkers Prev; 19(5) May 2010www.aacrjournals.org 1221
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incidence method to calculate 1 minus the ratio of the
rate of interval cancer to the expected incidence rate in
the absence of screening. The maximum likelihood esti-
mation method was applied to estimating the two para-
meters and their 95% confidence intervals (CI).
Results
Table 1 shows breast tissue density by age group. Den-
sity declined with age and 19.9% of those ages 45 to 49
years had dense breast tissue compared with 6.6% of
those ages 55 to 59 years. The overall prevalence of dense
breast tissue was 12.7%. Table 1 also shows the number
of cancers and the proportion of interval cancers among
total breast cancers by age and density. Tumors in dense
tissue were significantly more likely to be interval cancer
(P= 0.02).
Table 2 shows the estimated effects of density and other
factors on breast cancer incidence. In the multivariable
model, the adjusted relative risk for dense versus non-
dense breast was 1.57 (95% CI, 1.23-2.01). Figure 2A
shows the cumulative incidence of breast cancer by den-
sity. Figure 2B and C show the cumulative incidence
stratified by age of entry, which revealed that the effect
of dense breast was more remarkable in women ages 45
to 49 years compared with those ages 50 to 59 years due
to the substantial change in breast density in the latter
age group after long-term follow-up. Figure 3A to C
show the cumulative incidence of tumors larger than
2 cm, of node-positive tumors, and histologic grade 2 or
3 tumors by breast density, with the adjusted relative
risks, taking BMI and age into account. All were statisti-
cally significant at incidences of 1.79 (95% CI, 1.22-2.63)
for tumors >20 mm in size, 2.46 (95% CI, 1.66-2.62) for
cancer with lymph node involvement, and 1.80 (95%
CI, 1.39-2.34) for histologic grade 2 or 3 tumors.
Figure 3D to F also show the cumulative incidence of
the counterparts of three tumor attributes (<2 cm, node
negative, and grade 1), respectively. The corresponding
adjusted relative risks were also statistically significant,
1.62 (95% CI, 1.27-2.06), 1.52 (95% CI, 1.18-1.96), and
1.37 (95% CI, 0.97-1.96), with tumors <20 mm in size,
node negative, and histologic grade 1, respectively.
Density was suggestively, but not significantly, associ-
ated with survival (P= 0.103; Fig. 2D). The unadjusted
Cox regression relative hazard ratio associated with
dense tissue was 1.41 (95% CI, 0.92-2.14). Adjusting for
age, tumor size, node status, grade, and BMI, the relative
hazard was 1.75 (95% CI, 0.99-3.10). By stratification of
three tumor attributes, the differences in the survival
curves between dense and nondense breast tissue were
not significant not only for tumors >20 mm in size [haz-
ard ratio (HR), 1.14; 95% CI, 0.63-2.08], with node in-
volvement (HR, 1.28; 95% CI, 0.75-2.17), and of
histologic grade 2 or 3 (HR, 1.50; 95% CI, 0.96-2.35),
but also for tumors <20 mm in size (HR, 1.69; 95% CI,
0.90-3.17), node-negative (HR, 1.19; 95% CI, 0.55-2.55),
and histologic grade 1 (HR, 2.04; 95% CI, 0.53-7.79). We
also examined the heterogeneity of the comparison be-
tween dense breasts and nondense breasts stratified by
each tumor attribute and found a lack of statistical signi-
ficance with respect to tumor size (χ
2(1)
= 0.05; P= 0.82),
node status (χ
2(1)
= 2.67; P= 0.10), and histologic grade
(χ
2(1)
= 0.35, P= 0.55).
Figure 2E shows the cumulative mortality from breast
cancer by density. The relative risk of breast cancer
death (28 and 99 deaths for dense and nondense breast
tissue, respectively) for dense versus nondense tissue
was 1.91 (95% CI, 1.26-2.91). The increased mortality
was, to a greater extent, due to the increased incidence
and, to a lesser extent, due to the (nonsignificant) poorer
survival with dense breasts in the light of findings from
two components that determine mortality, incidence,
and survival.
The overall sensitivity based on proportional incidence
method was 79.5% (73.3-85.8%). Table 3 shows that
dense breasts had lower sensitivity than nondense
breasts [62.8% (95% CI, 47.2-78.3%) versus 82.0% (95%
Table 1. Prevalence of dense breast tissue and the proportion of interval cancer among total breast
cancer by density and age
Age group (y) Item Density classification Total (%) % of density
Nondense (%) Dense (%)
45-49 No. of participants 3,735 929 4,664 19.9
Interval cancer/total BC cases 48/182 (26.4) 23/79 (29.1) 71/261 (27.2)
50-54 No. of participants 4,508 673 5,181 13.0
Interval cancer/total BC cases 38/261 (14.6) 8/42 (19.0) 46/303 (15.2)
55-59 No. of participants 5,427 386 5,813 6.6
Interval cancer/total BC cases 36/281 (12.8) 6/28 (21.4) 42/309 (13.6)
Total No. of participants 13,670 1,988 15,658 12.7
Interval cancer/total BC cases 122/724 (16.9) 37/149 (24.8) 159/873 (18.2)
Abbreviation: BC, breast cancer.
Chiu et al.
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CI, 75.2-88.8%)]. The results also show a lower sensitivity
of dense breast tissue compared with nondense breast
tissue regardless of age. Table 3 shows the results of as-
sessing the two hypotheses related to the preclinical in-
cidence rate (λ
1
)andMST(1/λ
2
) between dense and
nondense breast tissue by age, given a sensitivity of
79.5% obtained from the proportional incidence method.
The preclinical incidence rates were higher in dense
breast tissue than nondense breast tissue by 1.65-fold.
The difference reached statistical difference as no over-
lapping of 95% CIs occurred between the two groups.
Similar findings were observed based on the three age
groups. However, only the result of young women ages
45 to 49 years was statistically significant. By fixing sen-
sitivity as a constant parameter, we also found that dense
breast tissue tended to have shorter MST than nondense
tissue although with a lack of statistical significance (95%
CIs overlapped between the two groups).
Discussion
Using long-term follow-up population-based screening
data, this study confirmed the effect of breast density
measured at baseline on an increased risk of breast can-
cer, which was supported by several significant findings:
higher adjusted relative risks of dense versus nondense
breast tissue in either the overall breast cancer or nonad-
vanced breast cancer (less affected by a masking effect
and supporting a causal effect) and also preclinical inci-
dence rates estimated from the three-state disease natural
history model. A masking effect, to a lesser extent, also
showed a higher risk of finding advanced breast cancer
(more likely to be affected by a masking effect) and also a
shorter MST for women with dense breasts, which makes
detection of cancers by mammography difficult.
We also found that dense tissue was significantly asso-
ciated with increased mortality from breast cancer. This
was partly due to its association with a higher incidence
of the disease and partly to the nonsignificant association
with poorer survival. Multiplying the relative risk for
incidence, 1.40, by the relative hazard from the Cox reg-
ression, also 1.41, gives 1.97, which was close to the ob-
served mortality relative risk of 1.91. This result was
slightly higher but consistent with the mortality result
based on the data from a Demark mammographic screen-
ing with 10 years of follow-up (6). The association of
dense breast tissue with aggressive tumors was similar
to the study of Aiello et al. (27). Within that study, large
tumors (>1.0 cm) or lymph node positivity was found in
women with dense breasts, especially in screen-detected
cancers (27). However, the results on the histologic grade
in their study were different from ours, which may
have been due to a disparity between study designs.
Consistent with the stronger association of density with
node-positive or poor differentiation of breast cancers,
survival was poorer in those with dense tissue, although
this difference was not significant. Note that the asso-
ciation of density with survival becomes stronger after
adjusting for these factors, which suggests that the
Table 2. Univariate and multivariate analysis of risk factors for breast cancer incidence
Variable Group Univariate Multivariate
Age at recruitment, y 45-49 1.00 1.00
50-54 1.05 (0.88-1.24) 1.25 (0.98-1.59)
55-59 0.95 (0.80-1.12) 0.98 (0.74-1.30)
Age at menarche, y ≥14 1.00 1.00
<14 1.20 (1.01-1.42)* 1.15 (0.95-1.39)
Pattern Nondense 1.00 1.00
Dense 1.45 (1.21-1.74)
†
1.57 (1.23-2.01)
‡
BMI <25 1.00 1.00
≥25 1.20 (1.03-1.40)* 1.23 (1.01-1.50)*
AFFTP, y <20 1.00 1.00
21-25 0.97 (0.80-1.17) 0.96 (0.74-1.24)
26-30 1.12 (0.90-1.17) 0.96 (0.71-1.31)
≥31 1.32 (1.02-1.71)* 1.16 (0.81-1.67)
Never 1.00 (0.78-1.29) 0.87 (0.61-1.24)
Menopausal Yes 1.00 1.00
No 1.15 (1.00-1.32)* 1.10 (0.88-1.38)
Abbreviation: AFFTP: age at first full-term pregnancy.
*0.01 ≤P< 0.05.
†
P< 0.0001.
‡
0.0001 ≤P< 0.01.
Incidence and Mortality of Breast Cancer by Density
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possible association of density with poorer survival may
not be due to an association with these characteristics.
Van Gils et al. (28, 29) also found a suggestive but non-
significant survival disadvantage in dense breasts. In
contrast, the case fatality in Denmark was lower in dense
than nondense breasts (6), which may be attributable to
different age groups and shorter follow-up periods.
Note that the relative risk for developing breast cancer
in our study was lower than that in other antecedent
studies (5, 6, 15, 30). Two unique characteristics account
for this inconsistency. Our prospective cohort design was
made to classify breast density (exposure) at the entry of
study rather than the date closer to diagnosis (age of in-
cident cases) adopted in most of the previous studies.
The other characteristic is that our cohort had 25 years
of long-term follow-up, which was longer than in other
studies (6, 30). The merit of such a study design with
long-term follow-up can predict the risk of dense breast
even if a woman's breast changes. However, the relative
risk regarding the effect of dense breast on breast cancer
risk was lower that that obtained in the previous study.
The reason is that the change in breast density due to
aging for women who were classified as having dense
breasts at recruitment increased after their dense breast
tissue changed to fat tissue. Such a dynamic change is
particularly remarkable for a long-term follow-up cohort.
Two other explanations may account for the lower rela-
tive risk, including suboptimal threshold for density
classification and the quality of mammography. By com-
bining pattern III into pattern IV or V according to the
Tabár classification, the results after reanalysis show
the relative rate slightly increased from 1.45 (1.21-1.74)
to 1.57 (1.38-1.64). The low quality of a mammogram
may also account for the lower relative risk. However,
this is unlikely as the mortality reduction was 35% in
the Two-County Trial, which was higher than in other
Figure 2. Cumulative curves of (A)
incidence for overall, (B) incidence
for age 45 to 49 years, (C)
incidence for age 50 to 59 years,
(D) survival, and (E) mortality
of breast cancer by density.
Chiu et al.
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on February 13, 2017. © 2010 American Association for Cancer Research.cebp.aacrjournals.org Downloaded from
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studies (31). Because the age at recruitment rather than
age at diagnosis was used in our unique prospective cohort
design as mentioned above and screening advanced the
date of diagnosis, the incidence rate increasing with age
was less remarkable after long-term follow-up (Table 2).
Our large population-based screening data also pro-
vide an opportunity to clarify the argument of whether
the positive association between dense breast tissue and
the increasing incidence of breast cancer is due to a mas-
king effect (32) or the risk of breast cancer in association
with breast density (causal effect; ref. 33). Using interval
cancers only would underestimate the causal effect be-
cause they are composed of two types of breast cancers,
false-negative cases masked at screening and newly diag-
nosed symptomatic cancers occurring after the last screen
with true-negative findings. To separate false-negative
cases from newly incident breast cancer, the previous
study excluded interval cancer at the first year because
the last negative screen assumed these excluded cases
were false-negative cases as a result of a masking effect
(1). This is not exactly true as false-negative cases may
become symptomatic cancers after >1 year and newly in-
cident breast cancer may also occur within 1 year. Day
(34) therefore suggests considering the time dimension
by correcting for the sensitivity based on the MST as es-
timated in our study. On the other hand, overestimation
would occur if only breast cancers detected at screen are
used. By the application of the three-state Markov model
Figure 3. Cumulative incidence of
tumor attributes: (A) tumor size of
>20 mm, (B) nodes positive, (C)
histologic grade of 2 and 3, (D)
tumor size of ≤20 mm, (E) nodes
negative, and (F) histologic grade
of 1.
Incidence and Mortality of Breast Cancer by Density
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to the information obtained from both breast cancers, our
results not only showed a higher preclinical incidence in
dense breast tissue but also a shorter sojourn time in
dense breasts given that the sensitivity was controlled
(seeTable3).Thesefindingswerecommensuratewith
theeffectofdensebreastsonincreasingtheincidence
and mortality of breast cancer with long-term follow-up.
In addition, making use of full information on mammo-
graphically detected cancers (prevalent screen and subse-
quent screen), interval cancers, and cancers from the
refuser group can also reduce the threat of length bias
as prevalent screen-detected cancers are more likely to
include a long sojourn time, whereas interval cancer or
cancers from the refuser group are characterized by ag-
gressive cancers with a shorter sojourn time.
Regarding the masking effect, dense tissue was associ-
ated with a reduced sojourn time. This finding was con-
sistent with the results of the Nijmegen study (28), which
showed that the occurrence of interval cancers after an
initial examination preponderated in the first 2 years
for dense breast tissue compared with nondense breast
tissue and were not remarkable 2 years after the initial
examination. This result supports the possibility of a
masking bias effect by indicating that the average time
for interval cancer diagnosis after the initial examination
was 2.79 years, which was lower than 3.14 years for non-
dense breasts.
The consideration of phenotype of breast density may
aid health policy makers to stratify the risk of breast can-
cer for the enhancement of efficiency in the prevention of
breast cancer (30, 35-40). More importantly, our three-
state modeling approach provides a new insight in
recommending a screening policy with interscreening
intervals and more sensitive screening methods by esti-
mating the proportions of two components of interval
cancer false-negative cases and newly incident cases,
which cannot be directly observed and have not yet been
addressed in previous studies. The novel modeling ap-
proach is also helpful for projecting the effect of inter-
screening intervals on the proportion of interval cancers
among all diagnosed breast cancers. In the simulation,
the proportion of interval cancers with a 1-year interscre-
ening interval was 22.4% (including 15.9% newly diag-
nosed cases and 6.5% arising from false-negative cases)
for dense breast tissue and 13.6% (including 10% newly
diagnosed cases and 3.7% arising from false-negative
cases) for nondense breast tissue. As far as the effect of
interscreening intervals is concerned, the proportion of
interval cancers for dense breasts was reduced from
44.2% (including 36.9% newly diagnosed cases and
7.3% arising from false-negative cases to 22.4%) for dense
breast as mentioned above if interscreening intervals
were changed from a triennial program to an annual pro-
gram. This 22% reduction in newly diagnosed interval
cancers is partly due to the higher preclinical incidence
rate and partly due to shorter MST. It could be argued
that a fraction of breast tumors, particularly with breast
densities >75% as illustrated in the Boyd et al. study (5),
cannot be detected by mammography even when annual
screening is applied. In light of the results of our mode-
ling, dense breasts that do not allow for detection with
the application of annual screening accounted for 7% of
all interval cancers, which is only slightly lower than 11%
estimated from Table 3 of Boyd et al. study (5), that can
be only identified using alternative imaging techniques,
such as digital mammography, ultrasonography, and
magnetic resonance imaging.
One limitation in our study is that we classified
breast density in a qualitative manner rather than using
quantitative assessment based on the percentage of
dense tissue using visual assessment or planimetry as
reviewed by Harvey and Bovbjerg (41) and at least
six categories (<25%, 25-50%, 50-75%, and ≥75%) used
in a nested case-control study (5). Accordingly, the
Table 3. Estimated results of the proportional incidence method and three-state Markov model by breast
density and age
Group Age of randomization,
years
Proportional incidence method
for sensitivity [1 −(I/E) × 100%]
Three-state Markov model
Preclinical incidence rate (λ
1
) MST* (95% CI; y)
Nondense Breast 82.0% (75.2-88.8%) 0.0020 (0.0018-0.0022) 3.29 (2.84-3.90)
Dense Breast 62.8% (47.2-78.3%) 0.0033 (0.0026-0.0039) 2.04 (1.53-3.10)
Overall 79.5% (73.3-85.8%) 0.0022 (0.0020-0.0024) 3.02 (2.64-3.52)
Nondense 45-49 76.8% (64.9-88.8%) 0.0015 (0.0012-0.0018) 2.68 (1.98-4.18)
Breast 50-54 83.8% (72.1-95.5%) 0.0021 (0.0018-0.0025) 3.27 (2.56-4.49)
55-59 88.7% (78.4-99.1%) 0.0023 (0.0020-0.0027) 3.60 (2.93-4.65)
Dense 45-49 55.3% (35.0-75.6%) 0.0032 (0.0022-0.0041) 1.78 (1.18-3.63)
Breast 50-54 77.7% (48.9-100.0%) 0.0028 (0.0017-0.0038) 1.90 (1.16-5.17)
55-59 73.7% (38.5-100.0%) 0.0042 (0.0026-0.0058) 2.82 (1.71-7.91)
Abbreviations: I, incidence rate of interval cancer; E, expected incidence rate in the absence of screening (control group).
*The MST was estimated by fixing sensitivity as a constant equal to 79.5%.
Chiu et al.
Cancer Epidemiol Biomarkers Prev; 19(5) May 2010 Cancer Epidemiology, Biomarkers & Prevention1226
on February 13, 2017. © 2010 American Association for Cancer Research.cebp.aacrjournals.org Downloaded from
Published OnlineFirst April 20, 2010; DOI: 10.1158/1055-9965.EPI-09-1028
dose-responseforthetrendonthedegreeofbreast
density in association with the risk for breast cancer
cannot be obtained. This limitation may be addressed
in a future study if such information can be obtained.
In conclusion, our study showed that dense breast tis-
sue not only increases breast cancer risk but also leads
to more aggressive tumors and mortality from breast
cancer. This finding was further supported by higher
preclinical incidence, due to the causal effect, and also
a shorter window of opportunity for detecting cancer
by mammography with difficulty (that is, the masking
effect) in dense breast tissue. These results suggest the
screening policy with a predominantly shorter screening
interval (reducing the newly incident interval cancers)
and with advanced imaging techniques (reducing
false-negative interval cancers) as indicated in women
with dense breasts to reduce interval cancers and
aggressive tumors.
Disclosure of Potential Conflicts of Interest
No potential conflicts of interest were disclosed.
Grant Support
American Cancer Society, USA, and in part by the Cancer Research UK
Centre for Epidemiology, Mathematics and Statistics, Wolfson Institute
for Preventive Medicine, London, United Kingdom.
The costs of publication of this article were defrayed in part by the
payment of page charges. This article must therefore be hereby marked
advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate
this fact.
Received 10/02/2009; revised 02/03/2010; accepted 02/25/2010;
published OnlineFirst 04/20/2010.
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