Prevalence of Women with Dense Breasts in Korea: Results from a Nationwide Cross-sectional Study

Hye-Mi Jo; Eun Hye Lee; Kyungran Ko; Bong Joo Kang; Joo Hee Cha; Ann Yi; Hae Kyoung Jung; Jae Kwan Jun

doi:10.4143/crt.2018.297

Articles

Page Path: HOME > Cancer Res Treat > Volume 51(4); 2019 > Article

Original Article Prevalence of Women with Dense Breasts in Korea: Results from a Nationwide Cross-sectional Study: Hye-Mi Jo, MPH¹, Eun Hye Lee, MD, PhD², Kyungran Ko, MD, PhD³, Bong Joo Kang, MD, PhD⁴, Joo Hee Cha, MD, PhD⁵, Ann Yi, MD, PhD⁶, Hae Kyoung Jung, MD⁷, Jae Kwan Jun, MD, PhD^1,; Cancer Research and Treatment : Official Journal of Korean Cancer Association 2019;51(4):1295-1301.
DOI: https://doi.org/10.4143/crt.2018.297
Published online: January 29, 2019

¹National Cancer Control Institute, National Cancer Center, Goyang, Korea

²Department of Radiology, Soonchunhyang University Bucheon Hospital, Soonchunhyang University College of Medicine, Bucheon, Korea

³Center for Breast Cancer, National Cancer Center Hospital, National Cancer Center, Goyang, Korea

⁴Department of Radiology, College of Medicine, The Catholic University of Korea, Seoul, Korea

⁵Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea

⁶Department of Radiology, Gangnam Healthcare Center, Seoul National University Hospital, Seoul, Korea

⁷Department of Radiology, CHA Bundang Medical Center, CHA University, Seongnam, Korea

Correspondence: Jae Kwan Jun, MD, PhD National Cancer Control Institute, National Cancer Center, 323 Ilsan-ro, Ilsandong-gu, Goyang 10408, Korea
Tel: 82-31-920-2184 Fax: 82-31-920-2929 E-mail: jkjun@ncc.re.kr

• Received: May 23, 2018 • Accepted: January 27, 2019

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

9,412 Views
297 Download
25 Web of Science
25 Crossref
24 Scopus

prev next

Full Article

Download PDF

Abstract

Purpose
Women with dense breast are known to be at high risk for breast cancer, but their prevalence and number of Korean women are unknown. The current study was to investigate the distribution of mammographic breast density by age of women undergoing screening mammography, and to estimate the prevalence of Korean women with dense breasts, quantitatively.
Materials and Methods
For obtaining a nationwide representative sample, 6,481 mammograms were collected from 86 screening units participated in the National Cancer Screening Program for breast cancer. Based on the American College of Radiology Breast Imaging Reporting and Data System classification, breast density was evaluated by six breast radiologists, qualitatively. We applied these breast density distributions to age-specific counts of the Korean women population derived to mid-year 2017 to estimate the number of Korean women with dense breasts.
Results
Overall, 54.4% (95% confidence interval [CI], 52.9% to 55.8%) of women 40 to 69 years of age had heterogeneously or extremely dense breasts, and this proportion was inversely associated with age. Based on the age distribution of Korean women, we estimated that 6,083,000 women (95% CI, 5,919,600 to 6,245,600) age 40-69 years in Korean have dense breasts. Women aged 40-49 years (n=3,450,000) accounted for 56.7% of this group.
Conclusion
More than half of Korean women aged 40 and over have dense breasts. To prevent breast cancer effectively and efficiently, it is necessary to develop a new personalized prevention strategy considering her status of breast density.
Key words: Breast density, Prevalence, Mammography, Korea

Introduction

The mammographic breast density (MBD) is the extent of the radio-opacified fibroglandular tissue of breast in the mammogram. The MBD, measured qualitatively or quantitatively, is well-known as a risk factor for breast cancer in Western women [1,2]. Recent studies have reported that it might be a risk factor in Asian women [3,4].

It is used as a surrogate marker of breast cancer prevention [5] and makes a model for predicting a risk of breast cancer in individuals more accurate [6]. Moreover, in women with dense breast, the MBD makes it difficult to detect breast cancer, which decreases the accuracy of mammography screening [7,8].

Age is most likely associated with the MBD, which is decreased by age. In addition, anthropometric, parity, and hormonal factors known as the risk factor of breast cancer are also associated with the MBD [9]. Although many epidemiological studies including immigration studies reported that there are ethnic and geographic differences in the MBD [10-14], most of them have a problem of small sample size and representativeness of populations. In Asian women, the proportions of women with dense breast may be higher than those of western women.

Many Korean women are known to have small breast volume and a higher proportion of dense breasts [15,16]. However, to date, there is no study on the distribution of breast density using a large-scale and representative sample of asymptomatic Korean women. The objective of this study, therefore, is to examine the distribution of MBD by age in women undergoing screening mammography and estimate the prevalence of Korean women with dense breasts. Using the recent studies conducted with a representative sample [17,18], we sought to examine differences between Korean women and other countries.

Materials and Methods

1. Study population

The Korean Mammographic Density (KoMAD) study was designed to estimate the prevalence of dense breast representatively and to determine the association between breast density and breast cancer risk in Korean women. It was conducted from January 2012 to December 2014. The design and study enrolment have been fully described in detail previously [4,19]. Briefly, the eligible population of this study consisted of women aged 40 years and over who participated in the National Cancer Screening Program (NCSP) in 2009. To prevent over-representation of specific regions, the participants were randomly selected after stratification by 16 administrative districts and the proportion of women aged ≥ 40 years in each region. To ensure accessibility of mammograms, screening units were restricted to a total amount of mammograms annually. Imaging data were collected from a total of 86 screening units representing their respective district regions. The mammographic images were obtained from the screening units in the form of original analog film copy or Digital Imaging and Communication in Medicine (DICOM) file. Among the selected women, 962 women (12.9%) whose were not stored craniocaudal and mediolateral oblique images of both breasts, or whose was not re-readable film status, were excluded. Finally, mammograms for 6,481 women were included in this study.

2. Mammography reading

The MBD was measured qualitatively according to the American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS) classification (4th edition) of breast density [20]. Six radiologists who are active members of the Korean Society of Breast Imaging and breast specialists in general hospitals participated in this study. To reduce variability in evaluating MBD, six breast radiologists were trained in the ACR BI-RADS classification of breast density. Prior to read mammography images in this study, the inter-radiologist agreement of ACR BI-RADS classification was evaluated. Based on pilot test, radiologists showed substantial agreement for measurement of MBD: the weighted kappa value for inter-radiologist agreement was 0.83 (95% confidence interval [CI], 0.80 to 0.86) [21]. They were paired into three groups to read MBD. If two radiologists’ read were not consistent, they discussed to reach an agreement. Each BI-RADS density grade was classified as follows [20]: almost entirely fat (0%-25% glandular); scattered fibroglandular densities (26%-50% glandular); heterogeneously dense (51%-75% glandular); and extremely dense (76%-100% glandular). Density classifications were grouped almost entirely fat and scattered fibroglandular densities as “fatty breasts” and heterogeneously or extremely dense as “dense breasts.”

3. Statistical analysis

We used the distribution of MBD by age groups for estimating prevalence of breast density and its 95% CI in Korean women. For estimating counts of women with a dense breast by age group, we obtained data from the 2017 Korean Census. By multiplying the weighted frequency by the corresponding mid-year population, we calculated a weighted frequency for each age group. All other statistical analyses were conducted using SAS Statistical Software, ver. 9.2 (SAS Institute Inc., Cary, NC).

4. Ethical statement

With permission from the Ministry of Health and Welfare, informed consent for this study was not required, which was approved by the Institutional Review Board (IRB) of the National Cancer Center, IRB number NCC2014-0065.

Results

Table 1 shows the characteristics of the study participants and their breast densities. About 11.1% of the participants reported having used hormones. This study included 1,212 women (18.7%) in the “almost entirely fat,” 1,862 women (28.7%) in the “scattered fibroglandular densities,” 2,212 women (34.1%) in the “heterogeneously dense,” and 1,195 women (18.4%) in the “extremely dense” groups.

Fig. 1 shows the distribution of breast density measured by four BI-RADS categories for women aged 40 years and older in this study. The highest proportions by each age group were as follows: extremely dense (44.2%) for aged 40-44 years, heterogeneously dense (43.7%) for aged 45-54 years, scattered fibroglandular tissue (40.5%) for aged 55-64 years, and almost entirely fat (53.2%) for aged 65 years and older. The proportions of dense breasts declined with increasing age. Similar trends were observed when stratified by health insurance status (S1 Fig.). It sharply decreased in Korean women aged 55-59 years. Women with dense breasts in their 40s showed more than 70%, but they fell to less than 10% from the age of 65. Overall, 45.6% (95% CI, 44.2% to 47.0%) in the Korean women aged 40 years and older, which was estimated at 6,420,226 women (95% CI, 6,223,110 to 6,617,340) had heterogeneously or extremely dense breasts (Table 2). For women aged 40 to 69 years, 54.4% (95% CI, 52.9 to 55.8) and 6,083,000 women (95% CI, 5,919,600 to 6,245,600) were estimated, respectively.

Our results were compared with prior studies conducted in United States and China is presented in Fig. 2. Similar to our results, the prevalence of MBD in United States and Chinese women decreased with increasing age. Until the age of 60, the prevalence of MBD of Korean women was higher than that of U.S. women. At age 60, U.S. women with dense breasts began to outstrip Korean women with dense breasts. Within comparable age groups, the trend in Chinese women was similar to that in Korean women.

Discussion

We quantitatively estimated Korean women with dense breasts, which might be one of major risk factors for breast cancer. Among all Korean women aged 40 years and over, who are the target population of the NCSP for breast cancer, we estimated that the proportion of women with heterogeneously or extremely dense breasts is approximately 45.6%, corresponding to approximately 6,420,226 Korean women. Restricted to aged 40-69 years, which most guidelines for breast cancer screening recommend, the proportion was increased to 54.4% (6,083,000 women).

In comparison to Sprague et al.'s study [17], we found differences in the distribution of mammographic density according age group between women in Korean and United States. There result of the overall tendency with aging was similar to our results. However, the proportion of women with dense breasts in Korean was higher than 80% until their late 40s, and then there was a sharp decrease in the proportion upper 50 years. By comparing the characteristic change in breast, it is that Korean women in aged between 40 and 54 years were a much greater proportion of dense breasts than U.S. women, and that the transfer from dense breasts to fatty breasts tends to change more rapidly than U.S. women. Our results in the proportion of breast density according age group are similar to prior studies in China and Japan. The proportion of women with dense breasts in China and Japan was higher in under 50 years of age, and then breast density tends to decrease with increasing age. But, Ishihara et al. [22] has limitation in making comparisons with our results because a quoted age standards are different and absolute density was not evaluation in their study.

Since 2002, the NCSP for breast cancer has been conducted for all Korean women aged 40 years old and over, biennially. To date, the participation rate is about 52% and more than 3.3 million women are receiving screening mammography each year [23]. Higher recall rates of NCSP, especially over 20% in aged 40s than those of other screening programs in Western countries was reported [24]. Women with extremely dense breast were 7 times more likely to recall than women with almost entirely fatty breast [25]. It might be due to risk of interval cancer in women with dense breast and concerns about subsequent medical litigation.

An important effect of high breast density is the risk of a false-negative mammography finding due to the masking effect of dense tissue [7,8]. An interval cancer diagnosed in woman with dense breasts has led to legislation in the United States that mandates disclosure of breast density information to women undergoing mammography screening. Since breast density notification law was first enacted in Connecticut in 2009, it has been put into effect in 35 states and requires direct screen notification on mammographic results indicating the presence of dense breasts [26]. It has been estimated that more than 25 million women with dense breasts in the United States could be affected by the breast density notification laws [17]. On the other hand, in Korea, the NCSP started to record breast density information in 2009, but it does not notified to participants.

Some limitations of this study regarding interpreting result exist. First, we evaluated MBD according BI-RADS density classification qualitatively. Although the BI-RADS classification is widely used, its assessments depend on the ability and experience of radiologists and may show reader variability. In order to overcome the limitations of qualitative methods, quantitative breast density analysis is needed with more objective and higher reproducibility, but supplying digital radiographic system for mammography is essential. Second, we could not consider the other factors that can influence breast density in this study, such as the body mass index, reproductive and hormonal factors, or socioeconomic status. However, our study minimized the effects of these external factors by applying a representative sampling strategy. Third, there were discrepancies between the time when the survey was conducted and the yearly population data used for estimation of the counts of women with dense breasts. Although there is no evidence that the prevalence of MBD by age changes with the calendar year, further studies are required to determine the changes in breast density over time through repeated measurements.

Despite these limitations, our study is meaningful because this is nationwide survey for estimating the distribution of MBD among Korean women and counting women with dense breasts quantitatively. It could be utilized as principal evidence for developing the preventing strategy for breast cancer such as screening.

Our results indicate the underlying prevalence of dense breasts among the Korean women aged 40 years and older. More than half of the Korean women have dense breasts. In spite of limited evidences [27], Korean women with dense breasts are recommended supplemental screening such as breast ultrasonography. More than 19,000 breast cancers are newly diagnosed each year and it is expected that the number will continue to increase in near future [28,29]. To prevent breast cancer effectively and efficiently, it is necessary to develop a new personalized prevention strategy considering her status of breast density.

Electronic Supplementary Material

Supplementary materials are available at Cancer Research and Treatment website (https://www.e-crt.org).

crt-2018-297-suppl.pdf

NOTES

Conflict of interest relevant to this article was not reported.

Acknowledgements

This research was supported by a grant of the Korea Health Technology R&D project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HC15C1257).

Fig. 1.

Distribution of breast density categories of American College of Radiology (ACS) Breast Imaging Reporting and Data System (BI-RADS) by age in Korean women.

Fig. 2.

Comparison of prevalences of dense breasts between Korean women, Chinese women [18], and U.S. women [17] by age.

Table 1.

General characteristics of the study population

	No. (%) (n=6,481)
Age, mean±SD (yr)	55.01±11.0
Age at menarche (yr)
≤ 13	546 (8.4)
14-16	3,605 (55.6)
≥ 17	2,279 (35.2)
Missing	51 (0.8)
No. of live births
0	286 (4.4)
1	699 (10.8)
≥ 2	5,471 (84.4)
Missing	25 (0.4)
Breast feeding (mo)
Never	774 (11.9)
< 12	2,128 (32.8)
≥ 12	3,578 (55.2)
Missing	1 (0.0)
Age at menopause (yr)
Premenopausal	83 (1.3)
< 45	259 (4.0)
45-54	3,490 (53.9)
≥ 55	411 (6.3)
Missing	2,238 (34.5)
Hormone replacement therapy
Never	5,433 (83.8)
Ever	720 (11.1)
Missing	328 (5.1)
Oral contraceptive use (mo)
Never	5,224 (80.6)
< 12	622 (9.6)
≥ 12	320 (4.9)
Missing	315 (4.9)
Past history of benign disease
Yes	528 (8.1)
No	5,459 (84.2)
I do not know	459 (7.1)
Missing	35 (0.5)
Breast density
Almost entirely fat	1,212 (18.7)
Scattered fibroglandular densities	1,862 (28.7)
Heterogeneously dense	2,212 (34.1)
Extremely dense	1,195 (18.4)

SD, standard deviation.

Table 2.

Estimated number of Korean women in each of the ACR BI-RADS breast density categories

Age group (yr)	ACR BI-RADS breast density category^a)				Dense breasts^b)
Age group (yr)	Almost entirely fat	Scattered fibroglandular densities	Heterogeneously dense	Extremely dense	Dense breasts^b)
40-44	3.09 (1.94-4.92)	26.92 (23.16-31.17)	82.91 (77.18-88.75)	89.36 (83.47-95.31)	172.25 (167.82-176.21)
45-49	7.57 (5.49-10.40)	44.80 (39.62-50.47)	102.82 (96.12-109.59)	68.91 (62.82-75.34)	171.73 (165.79-177.24)
50-54	16.83 (14.16-19.95)	63.92 (59.18-68.84)	83.81 (78.77-88.96)	37.20 (33.31-41.42)	121.02 (115.87-126.04)
55-59	41.62 (35.18-48.92)	84.54 (76.17-93.21)	68.98 (61.01-77.48)	16.26 (11.97-21.92)	85.26 (76.76-94.01)
60-64	53.35 (47.77-59.26)	66.40 (60.46-72.51)	36.70 (31.88-42.02)	5.02 (3.25-7.72)	41.71 (36.62-47.25)
65-69	48.66 (42.68-54.87)	48.55 (42.60-54.72)	19.42 (15.28-24.40)	0.85 (0.27-2.67)	20.27 (16.05-25.32)
70-74	50.40 (45.69-55.06)	32.23 (27.95-36.82)	12.88 (10.03-16.41)	0.69 (0.22-2.16)	13.59 (10.64-17.17)
75-79	51.22 (46.17-56.09)	28.07 (23.63-32.93)	8.13 (5.63-11.60)	0.88 (0.19-3.82)	9.02 (6.31-12.72)
≥ 80	64.13 (54.45-73.04)	29.29 (21.67-38.37)	11.49 (6.27-20.18)	NA	11.49 (6.27-20.18)
40-69^c),d)	170.91 (158.76-183.82)	335.55 (324.22-354.72)	389.95 (397.30-405.23)	212.51 (206.38-230.84)	608.30 (591.96-624.56)

ACR BI-RADS, American College of Radiology Breast Imaging Reporting and Data System; NA, not available.

^a) Units: 10,000 women and 95% confidence intervals,

^b) Heterogeneously dense or extremly dense,

^c) The recommendations by National guidelines for breast cancer screening [27],

^d) Estimates may be different from sum of each cells due to rounding, estimated for data based on 2017 Korean Census counts.

REFERENCES

1. Boyd NF, Martin LJ, Yaffe MJ, Minkin S. Mammographic density and breast cancer risk: current understanding and future prospects. Breast Cancer Res. 2011;13:223.Article PubMed PMC PDF
2. Astley SM, Harkness EF, Sergeant JC, Warwick J, Stavrinos P, Warren R, et al. A comparison of five methods of measuring mammographic density: a case-control study. Breast Cancer Res. 2018;20:10.Article PubMed PMC PDF
3. Bae JM, Kim EH. Breast density and risk of breast cancer in Asian women: a meta-analysis of observational studies. J Prev Med Public Health. 2016;49:367–75. Article PubMed PMC PDF
4. Park B, Cho HM, Lee EH, Song S, Suh M, Choi KS, et al. Does breast density measured through population-based screening independently increase breast cancer risk in Asian females? Clin Epidemiol. 2018;10:61–70. Article PubMed
5. Nyante SJ, Sherman ME, Pfeiffer RM, Berrington de Gonzalez A, Brinton LA, Aiello Bowles EJ, et al. Prognostic significance of mammographic density change after initiation of tamoxifen for ER-positive breast cancer. J Natl Cancer Inst. 2015;107:dju425.Article PubMed PMC PDF
6. Tice JA, Cummings SR, Smith-Bindman R, Ichikawa L, Barlow WE, Kerlikowske K. Using clinical factors and mammographic breast density to estimate breast cancer risk: development and validation of a new predictive model. Ann Intern Med. 2008;148:337–47. Article PubMed PMC
7. Trentham-Dietz A, Kerlikowske K, Stout NK, Miglioretti DL, Schechter CB, Ergun MA, et al. Tailoring breast cancer screening intervals by breast density and risk for women aged 50 years or older: collaborative modeling of screening outcomes. Ann Intern Med. 2016;165:700–12. Article PubMed PMC
8. Holm J, Humphreys K, Li J, Ploner A, Cheddad A, Eriksson M, et al. Risk factors and tumor characteristics of interval cancers by mammographic density. J Clin Oncol. 2015;33:1030–7. Article PubMed
9. Boyd NF, Rommens JM, Vogt K, Lee V, Hopper JL, Yaffe MJ, et al. Mammographic breast density as an intermediate phenotype for breast cancer. Lancet Oncol. 2005;6:798–808. Article PubMed
10. Maskarinec G, Nagata C, Shimizu H, Kashiki Y. Comparison of mammographic densities and their determinants in women from Japan and Hawaii. Int J Cancer. 2002;102:29–33. Article PubMed
11. Maskarinec G, Pagano I, Chen Z, Nagata C, Gram IT. Ethnic and geographic differences in mammographic density and their association with breast cancer incidence. Breast Cancer Res Treat. 2007;104:47–56. Article PubMed PDF
12. Mariapun S, Li J, Yip CH, Taib NA, Teo SH. Ethnic differences in mammographic densities: an Asian cross-sectional study. PLoS One. 2015;10:e0117568Article PubMed PMC
13. McCormack VA, Perry N, Vinnicombe SJ, Silva Idos S. Ethnic variations in mammographic density: a British multiethnic longitudinal study. Am J Epidemiol. 2008;168:412–21. Article PubMed PDF
14. Habel LA, Capra AM, Oestreicher N, Greendale GA, Cauley JA, Bromberger J, et al. Mammographic density in a multiethnic cohort. Menopause. 2007;14:891–9. Article PubMed
15. Kim SH, Kim MH, Oh KK. Analysis and comparison of breast density according to age on mammogram between Korean and western women. J Korean Radiol Soc. 2000;42:1009–14. Article
16. Youn I, Choi S, Kook SH, Choi YJ. Mammographic breast density evaluation in Korean women using fully automated volumetric assessment. J Korean Med Sci. 2016;31:457–62. Article PubMed PMC
17. Sprague BL, Gangnon RE, Burt V, Trentham-Dietz A, Hampton JM, Wellman RD, et al. Prevalence of mammographically dense breasts in the United States. J Natl Cancer Inst. 2014;106:dju255.Article PubMed PMC PDF
18. Dai H, Yan Y, Wang P, Liu P, Cao Y, Xiong L, et al. Distribution of mammographic density and its influential factors among Chinese women. Int J Epidemiol. 2014;43:1240–51. Article PubMed PMC PDF
19. Jun JK, Kim MJ, Choi KS, Suh M, Jung KW. Development of a sampling strategy and sample size calculation to estimate the distribution of mammographic breast density in Korean women. Asian Pac J Cancer Prev. 2012;13:4661–4. Article PubMed PDF
20. American College of Radiology. Breast Imaging Reporting and Data System. 4th ed. Reston, VA: American College of Radiology; 2003.
21. Jo HM, Song S, Lee EH, Ko K, Kang BJ, Cha JH, et al. Interpretive volume and inter-radiologist agreement on assessing breast density. J Korean Soc Breast Screening. 2018;15:15–22.
22. Ishihara S, Taira N, Kawasaki K, Ishibe Y, Mizoo T, Nishiyama K, et al. Association between mammographic breast density and lifestyle in Japanese women. Acta Med Okayama. 2013;67:145–51. PubMed
23. Suh M, Song S, Cho HN, Park B, Jun JK, Choi E, et al. Trends in participation rates for the national cancer screening program in Korea, 2002-2012. Cancer Res Treat. 2017;49:798–806. Article PubMed PDF
24. Lee EH, Kim KW, Kim YJ, Shin DR, Park YM, Lim HS, et al. Performance of screening mammography: a report of the Alliance for Breast Cancer Screening in Korea. Korean J Radiol. 2016;17:489–96. Article PubMed PMC
25. Kim YJ, Lee EH, Jun JK, Shin DR, Park YM, Kim HW, et al. Analysis of participant factors that affect the diagnostic performance of screening mammography: a report of the Alliance for Breast Cancer Screening in Korea. Korean J Radiol. 2017;18:624–31. Article PubMed PMC
26. Are You Dense Advocacy Inc. [Internet]. Woodbury, CT: Are You Dense Advocacy Inc.; 2018. [cited 2018 Apr 13]. Available from: http://www.areyoudenseadvocacy.org
27. Lee EH, Park B, Kim NS, Seo HJ, Ko KL, Min JW, et al. The Korean guideline for breast cancer screening. J Korean Med Assoc. 2015;58:408–19. Article
28. Jung KW, Won YJ, Kong HJ, Lee ES; Community of Population-Based Regional Cancer Registries. Cancer statistics in Korea: incidence, mortality, survival, and prevalence in 2015. Cancer Res Treat. 2018;50:303–16. Article PubMed PMC PDF
29. Jung KW, Won YJ, Kong HJ, Lee ES. Prediction of cancer incidence and mortality in Korea, 2018. Cancer Res Treat. 2018;50:317–23. Article PubMed PMC PDF

Figure & Data

REFERENCES

Citations

Citations to this article as recorded by

Study on ABUS Applicability for Rural Women With Low Health Resources in China—Large‐Scale Population, Non‐Opportunistic Screening
Meili Niu, Jie Zheng, Xiaoyang Feng, Huijiao Yan, Mengmeng Jia, Shuzhen Qi, Youlin Qiao, Fanghui Zhao, Xiangxian Feng, Zhifang Li
Journal of Ultrasound in Medicine.2025;[Epub] CrossRef
Development and validation of nomograms using photoacoustic imaging and 2D ultrasound to predict breast nodule benignity and malignancy
Jing Chen, Zhibin Huang, Hui Luo, Guoqiu Li, Zhimin Ding, Hongtian Tian, Shuzhen Tang, Sijie Mo, Jinfeng Xu, Huaiyu Wu, Fajin Dong
Postgraduate Medical Journal.2024; 100(1183): 309. CrossRef
Screening mammography performance according to breast density: a comparison between radiologists versus standalone intelligence detection
Mi-ri Kwon, Yoosoo Chang, Soo-Youn Ham, Yoosun Cho, Eun Young Kim, Jeonggyu Kang, Eun Kyung Park, Ki Hwan Kim, Minjeong Kim, Tae Soo Kim, Hyeonsoo Lee, Ria Kwon, Ga-Young Lim, Hye Rin Choi, JunHyeok Choi, Shin Ho Kook, Seungho Ryu
Breast Cancer Research.2024;[Epub] CrossRef
Enhancing Breast Cancer Risk Prediction with Machine Learning: Integrating BMI, Smoking Habits, Hormonal Dynamics, and BRCA Gene Mutations—A Game-Changer Compared to Traditional Statistical Models?
Luana Conte, Emanuele Rizzo, Emanuela Civino, Paolo Tarantino, Giorgio De Nunzio, Elisabetta De Matteis
Applied Sciences.2024; 14(18): 8474. CrossRef
Assessment of the Breast Density Prevalence in Swiss Women with a Deep Convolutional Neural Network: A Cross-Sectional Study
Adergicia V. Kaiser, Daniela Zanolin-Purin, Natalie Chuck, Jennifer Enaux, Daniela Wruk
Diagnostics.2024; 14(19): 2212. CrossRef
A Large Cohort Study of Height and Mammographic Density in Relation to Breast Cancer Risk among Korean Women
Ga Eun Park, Sung Hun Kim, Kyungdo Han
Cancer Epidemiology, Biomarkers & Prevention.2024; 33(11): 1484. CrossRef
Association of Breast Cancer Family History With Breast Density Over Time in Korean Women
Thi Xuan Mai Tran, Yoosoo Chang, Soyeoun Kim, Huiyeon Song, Seungho Ryu, Boyoung Park
JAMA Network Open.2023; 6(3): e232420. CrossRef
Screening Outcomes of Supplemental Automated Breast US in Asian Women with Dense and Nondense Breasts
Mi-ri Kwon, Ji Soo Choi, Mi Yeon Lee, Sinae Kim, Eun Sook Ko, Eun Young Ko, Boo Kyung Han
Radiology.2023;[Epub] CrossRef
Deep Learning Analysis of Mammography for Breast Cancer Risk Prediction in Asian Women
Hayoung Kim, Jihe Lim, Hyug-Gi Kim, Yunji Lim, Bo Kyoung Seo, Min Sun Bae
Diagnostics.2023; 13(13): 2247. CrossRef
Understanding of Dense Breast and Perception of Breast Cancer Risk of Nurses with or without Dense Breast Notification
Seon Mi Yoon, Jeong Hee Kang
Asian Oncology Nursing.2023; 23(2): 75. CrossRef
Artificial Intelligence for Breast Cancer Screening in Mammography (AI-STREAM): A Prospective Multicenter Study Design in Korea Using AI-Based CADe/x
Yun-Woo Chang, Jin Kyung An, Nami Choi, Kyung Hee Ko, Ki Hwan Kim, Kyunghwa Han, Jung Kyu Ryu
Journal of Breast Cancer.2022; 25(1): 57. CrossRef
Breast cancer detection across dense and non-dense breasts: Markers of diagnostic confidence and efficacy
Ibrahim Hadadi, William Rae, Jillian Clarke, Mark McEntee, Ernest Ekpo
Acta Radiologica Open.2022;[Epub] CrossRef
Association of Mammographic Density and Benign Breast Calcifications Individually or Combined with Hypertension, Diabetes, and Hypercholesterolemia in Women ≥40 Years of Age: A Retrospective Study
Maha Al-Mohaissen, Arwa Alkhedeiri, Ohoud Al-Madani, Terry Lee, Anas Hamdoun, Mohammad Al-Harbi
Journal of Investigative Medicine.2022; 70(5): 1308. CrossRef
Mammographic breast density, body mass index and risk of breast cancer in Korean women aged 75 years and older
Thi Xuan Mai Tran, Soyeoun Kim, Huiyeon Song, Boyoung Park
International Journal of Cancer.2022; 151(6): 869. CrossRef
The mammographic breast density distribution of Finnish women with breast cancer and comparison of breast density reporting using the 4th and 5th editions of the Breast Imaging-Reporting and Data System
Aki Nykänen, Hidemi Okuma, Anna Sutela, Amro Masarwah, Ritva Vanninen, Mazen Sudah
European Journal of Radiology.2021; 137: 109585. CrossRef
Impact of Awareness of Breast Density on Perceived Risk, Worry, and Intentions for Future Breast Cancer Screening among Korean Women
Anh Thi Ngoc Tran, Ji Hae Hwang, Eunji Choi, Yun Yeong Lee, Mina Suh, Chan Wha Lee, Yeol Kim, Kui Son Choi
Cancer Research and Treatment.2021; 53(1): 55. CrossRef
Digital Mammography as a Screening Tool in Korea
Soo Yeon Song, Seri Hong, Jae Kwan Jun
Journal of the Korean Society of Radiology.2021; 82(1): 2. CrossRef
Clinical Implications of Geometric and Dosimetric Uncertainties of Inter- and Intra-Fractional Movement during Volumetric Modulated Arc Therapy for Breast Cancer Patients
Jason Joon Bock Lee, Ik Jae Lee, Yeonho Choi, Mi Jin Jeon, Il Hun Jung, Ho Lee
Cancers.2021; 13(7): 1651. CrossRef
Fatty liver disease and changes in dense breasts in pre- and postmenopausal women: the Kangbuk Samsung Health Study
Yoosun Cho, Yoosoo Chang, Hyun-Suk Jung, Chan-Won Kim, Hyungseok Oh, Eun Young Kim, Hocheol Shin, Sarah H. Wild, Christopher D. Byrne, Seungho Ryu
Breast Cancer Research and Treatment.2021; 190(2): 343. CrossRef
Multifocality and Multicentrality in Breast Cancer: Comparison of the Efficiency of Mammography, Contrast-Enhanced Spectral Mammography, and Magnetic Resonance Imaging in a Group of Patients with Primarily Operable Breast Cancer
Katarzyna Steinhof-Radwańska, Andrzej Lorek, Michał Holecki, Anna Barczyk-Gutkowska, Anna Grażyńska, Joanna Szczudło-Chraścina, Oskar Bożek, Justyna Habas, Karol Szyluk, Paweł Niemiec, Iwona Gisterek
Current Oncology.2021; 28(5): 4016. CrossRef
The Relationship Between Breast Density Change During Menopause and the Risk of Breast Cancer in Korean Women
Danbee Kang, Ji-Yeon Kim, Ji-Young Kim, Han Song Mun, Sook Ja Yoon, Jieun Lee, Gayeon Han, Young-Hyuck Im, Soo-Young Shin, Se Kyung Lee, Jong-Han Yu, Kyung-Hyun Lee, Mincheol Kim, Dohyun Park, Yoon-Ho Choi, Ok Soon Jeong, Jean Hyoung Lee, Se Yong Jekal, J
Cancer Prevention Research.2021; 14(12): 1119. CrossRef
Clinical Application of Automated Breast Ultrasound
Sung Hun Kim
Clinical Ultrasound.2021; 6(2): 35. CrossRef
Association of the Interaction Between Mammographic Breast Density, Body Mass Index, and Menopausal Status With Breast Cancer Risk Among Korean Women
Thi Xuan Mai Tran, Seong-Geun Moon, Soyeoun Kim, Boyoung Park
JAMA Network Open.2021; 4(12): e2139161. CrossRef
Association of Screening by Thyroid Ultrasonography with Mortality in Thyroid Cancer: A Case–Control Study Using Data from Two National Surveys
Jae Kwan Jun, Soon-Young Hwang, Seri Hong, Mina Suh, Kui Son Choi, Kyu-Won Jung
Thyroid.2020; 30(3): 396. CrossRef
Prevalencia de tejido mamario denso en una población en la ciudad de Bucaramanga, Colombia
María Alejandra Baquero Serrano , Luis Andrés López Martínez , Silvia Nathalia Vera Campos, Sergio Andrés Rosales Rueda , Natalia Jaramillo Botero, Miguel Ochoa Vera
Revista Colombiana de Cancerología.2020; 24(3): 119. CrossRef

PubReader
ePub Link

Cite

CITE: export Copy Download Format; Close

Download Citation

Download a citation file in RIS format that can be imported by all major citation management software, including EndNote, ProCite, RefWorks, and Reference Manager.

Format:

RIS — For EndNote, ProCite, RefWorks, and most other reference management software
BibTeX — For JabRef, BibDesk, and other BibTeX-specific software

Include:

Citation for the content below
Citation and abstract for the content below

Prevalence of Women with Dense Breasts in Korea: Results from a Nationwide Cross-sectional Study

Cancer Res Treat. 2019;51(4):1295-1301. Published online January 29, 2019

DOI: https://doi.org/10.4143/crt.2018.297

XML Download

Abstract
Introduction
Materials and Methods
Results
Discussion
Electronic Supplementary Material
NOTES
REFERENCES

Prevalence of Women with Dense Breasts in Korea: Results from a Nationwide Cross-sectional Study

Fig. 1. Distribution of breast density categories of American College of Radiology (ACS) Breast Imaging Reporting and Data System (BI-RADS) by age in Korean women.

Fig. 2. Comparison of prevalences of dense breasts between Korean women, Chinese women [18], and U.S. women [17] by age.

Fig. 1.

Fig. 2.

Prevalence of Women with Dense Breasts in Korea: Results from a Nationwide Cross-sectional Study

	No. (%) (n=6,481)
Age, mean±SD (yr)	55.01±11.0
Age at menarche (yr)
≤ 13	546 (8.4)
14-16	3,605 (55.6)
≥ 17	2,279 (35.2)
Missing	51 (0.8)
No. of live births
0	286 (4.4)
1	699 (10.8)
≥ 2	5,471 (84.4)
Missing	25 (0.4)
Breast feeding (mo)
Never	774 (11.9)
< 12	2,128 (32.8)
≥ 12	3,578 (55.2)
Missing	1 (0.0)
Age at menopause (yr)
Premenopausal	83 (1.3)
< 45	259 (4.0)
45-54	3,490 (53.9)
≥ 55	411 (6.3)
Missing	2,238 (34.5)
Hormone replacement therapy
Never	5,433 (83.8)
Ever	720 (11.1)
Missing	328 (5.1)
Oral contraceptive use (mo)
Never	5,224 (80.6)
< 12	622 (9.6)
≥ 12	320 (4.9)
Missing	315 (4.9)
Past history of benign disease
Yes	528 (8.1)
No	5,459 (84.2)
I do not know	459 (7.1)
Missing	35 (0.5)
Breast density
Almost entirely fat	1,212 (18.7)
Scattered fibroglandular densities	1,862 (28.7)
Heterogeneously dense	2,212 (34.1)
Extremely dense	1,195 (18.4)

Age group (yr)	ACR BI-RADS breast density category^a)				Dense breasts^b)
Age group (yr)	Almost entirely fat	Scattered fibroglandular densities	Heterogeneously dense	Extremely dense	Dense breasts^b)
40-44	3.09 (1.94-4.92)	26.92 (23.16-31.17)	82.91 (77.18-88.75)	89.36 (83.47-95.31)	172.25 (167.82-176.21)
45-49	7.57 (5.49-10.40)	44.80 (39.62-50.47)	102.82 (96.12-109.59)	68.91 (62.82-75.34)	171.73 (165.79-177.24)
50-54	16.83 (14.16-19.95)	63.92 (59.18-68.84)	83.81 (78.77-88.96)	37.20 (33.31-41.42)	121.02 (115.87-126.04)
55-59	41.62 (35.18-48.92)	84.54 (76.17-93.21)	68.98 (61.01-77.48)	16.26 (11.97-21.92)	85.26 (76.76-94.01)
60-64	53.35 (47.77-59.26)	66.40 (60.46-72.51)	36.70 (31.88-42.02)	5.02 (3.25-7.72)	41.71 (36.62-47.25)
65-69	48.66 (42.68-54.87)	48.55 (42.60-54.72)	19.42 (15.28-24.40)	0.85 (0.27-2.67)	20.27 (16.05-25.32)
70-74	50.40 (45.69-55.06)	32.23 (27.95-36.82)	12.88 (10.03-16.41)	0.69 (0.22-2.16)	13.59 (10.64-17.17)
75-79	51.22 (46.17-56.09)	28.07 (23.63-32.93)	8.13 (5.63-11.60)	0.88 (0.19-3.82)	9.02 (6.31-12.72)
≥ 80	64.13 (54.45-73.04)	29.29 (21.67-38.37)	11.49 (6.27-20.18)	NA	11.49 (6.27-20.18)
40-69^c),d)	170.91 (158.76-183.82)	335.55 (324.22-354.72)	389.95 (397.30-405.23)	212.51 (206.38-230.84)	608.30 (591.96-624.56)

Table 1. General characteristics of the study population

SD, standard deviation.

Table 2. Estimated number of Korean women in each of the ACR BI-RADS breast density categories

ACR BI-RADS, American College of Radiology Breast Imaging Reporting and Data System; NA, not available.

Units: 10,000 women and 95% confidence intervals,

Heterogeneously dense or extremly dense,

The recommendations by National guidelines for breast cancer screening [27],

Estimates may be different from sum of each cells due to rounding, estimated for data based on 2017 Korean Census counts.