Mammographic breast density, body mass index and risk of breast cancer in Korean women aged 75 years and older

Tran, Thi Xuan Mai; Kim, Soyeoun; Song, Huiyeon; Park, Boyoung

doi:10.1002/ijc.34038

Cited by 6 publications

(4 citation statements)

References 36 publications

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“…The self-reported questionnaire provided information on known risk factors for breast cancer [ 25 ], including first and second-degree family history of breast cancer [ 27 , 28 ], body mass index (BMI) [ 29 ], exercise and physical activity [ 30 , 31 ], alcohol and smoking [ 32 , 33 ], age at menarche [ 34 ], pregnancy [ 35 ], use of hormone therapy (HT), oral contraception, and hormone spiral [ 36 , 37 , 38 ]. First-degree family history was defined as a mother, sister, or daughter with a breast cancer diagnosis, and second-degree family history as a grandmother, aunt, or niece and no first-degree.…”

Section: Methodsmentioning

confidence: 99%

Personalized Breast Cancer Screening: A Risk Prediction Model Based on Women Attending BreastScreen Norway

Louro,

Román,

Moshina

et al. 2023

Cancers

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Background: We aimed to develop and validate a model predicting breast cancer risk for women targeted by breast cancer screening. Method: This retrospective cohort study included 57,411 women screened at least once in BreastScreen Norway during the period from 2007 to 2019. The prediction model included information about age, mammographic density, family history of breast cancer, body mass index, age at menarche, alcohol consumption, exercise, pregnancy, hormone replacement therapy, and benign breast disease. We calculated a 4-year absolute breast cancer risk estimates for women and in risk groups by quartiles. The Bootstrap resampling method was used for internal validation of the model (E/O ratio). The area under the curve (AUC) was estimated with a 95% confidence interval (CI). Results: The 4-year predicted risk of breast cancer ranged from 0.22–7.33%, while 95% of the population had a risk of 0.55–2.31%. The thresholds for the quartiles of the risk groups, with 25% of the population in each group, were 0.82%, 1.10%, and 1.47%. Overall, the model slightly overestimated the risk with an E/O ratio of 1.10 (95% CI: 1.09–1.11) and the AUC was 62.6% (95% CI: 60.5–65.0%). Conclusions: This 4-year risk prediction model showed differences in the risk of breast cancer, supporting personalized screening for breast cancer in women aged 50–69 years.

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Section: Methodsmentioning

confidence: 99%

Personalized Breast Cancer Screening: A Risk Prediction Model Based on Women Attending BreastScreen Norway

Louro,

Román,

Moshina

et al. 2023

Cancers

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show abstract

“…Women with a family history of breast cancer (FHBC) in first-degree relatives have an approximately 2-fold increased risk of breast cancer, which varies depending on the number of family members affected by breast cancer. 1 Mammographic breast density is a strong risk factor for the development of breast cancer, [2][3][4][5][6] with a 3-fold increase in the risk of breast cancer among women with dense breast tissues compared with women with fatty breasts. 7 Mammographic breast density is affected by both genetic and environmental factors, and familial correlations of breast density have been observed between twins and family members.…”

Section: Introductionmentioning

confidence: 99%

Association of Breast Cancer Family History With Breast Density Over Time in Korean Women

et al. 2023

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ImportanceEvidence suggests that women with a family history of breast cancer (FHBC) in first-degree relatives have a higher level of breast density; however, studies of premenopausal women remain limited.ObjectiveTo investigate the association between FHBC and mammographic breast density and breast density changes among premenopausal women.Design, Setting, and ParticipantsThis retrospective cohort study used population-based data obtained from the National Health Insurance Service–National Health Information Database of Korea. We included premenopausal women aged 40 to 55 years who underwent mammography for breast cancer screening once between January 1, 2015, and December 31, 2016 (n = 1 174 214), and women who underwent mammography twice (first in 2015-2016 and again between January 1, 2017 and December 31, 2018) (n = 838 855).ExposuresFamily history of breast cancer was assessed using a self-reported questionnaire, which included information on FHBC in the mother and/or sister.Main Outcomes and MeasuresBreast density, based on the Breast Imaging Reporting and Data System, was categorized as dense (heterogeneously or extremely dense) and nondense (almost entirely fat or scattered fibroglandular areas). Multivariate logistic regression was used to assess the association among FHBC, breast density, and changes in breast density from the first to second screening. Data analysis was performed from June 1 to September 31, 2022.ResultsOf the 1 174 214 premenopausal women, 34 003 (2.4%; mean [SD] age, 46.3 [3.2] years) reported having FHBC among their first-degree relatives, and 1 140 211 (97.1%; mean [SD] age, 46.3 [3.2] years) reported no FHBC. Odds of having dense breasts was 22% higher (adjusted odds ratio [aOR], 1.22; 95% CI, 1.19-1.26) in women with FHBC than in women without FHBC, and the association varied by affected relatives: mother alone (aOR, 1.15; 95% CI, 1.10-1.21), sister alone (aOR, 1.26; 95% CI, 1.22-1.31), and both mother and sister (aOR, 1.64; 95% CI, 1.20-2.25). Among women with fatty breasts at baseline, the odds of developing dense breasts was higher in women with FHBC than in those without FHBC (aOR, 1.19; 95% CI, 1.11-1.26), whereas among women with dense breasts, higher odds of having persistently dense breasts were observed in women with FHBC (aOR, 1.11; 95% CI, 1.05-1.16) than in those without FHBC.Conclusions and RelevanceIn this cohort study of premenopausal Korean women, FHBC was positively associated with an increased incidence of having increased or persistently dense breasts over time. These findings suggest the need for a tailored breast cancer risk assessment for women with FHBC.

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“…A dense breast, which includes the higher density types (c) and (d) [8], has a high probability of decreased sensitivity and makes lesion detection difficult. It has also been reported that a dense breast is associated with an increased risk of breast cancer [9][10][11][12][13][14][15][16][17][18][19]. In March 2023, the U.S. Food and Drug Administration mandated notification of breast types to patients [20], so it is increasing demand and vital to evaluate breast types in medical screening and daily practice.…”

Section: Introductionmentioning

confidence: 99%

Automated Estimation of Mammary Gland Content Ratio Using Regression Deep Convolutional Neural Network and the Effectiveness in Clinical Practice as Explainable Artificial Intelligence

Kai

Ishizuka

Otsuka

et al. 2023

Cancers

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Recently, breast types were categorized into four types based on the Breast Imaging Reporting and Data System (BI-RADS) atlas, and evaluating them is vital in clinical practice. A Japanese guideline, called breast composition, was developed for the breast types based on BI-RADS. The guideline is characterized using a continuous value called the mammary gland content ratio calculated to determine the breast composition, therefore allowing a more objective and visual evaluation. Although a discriminative deep convolutional neural network (DCNN) has been developed conventionally to classify the breast composition, it could encounter two-step errors or more. Hence, we propose an alternative regression DCNN based on mammary gland content ratio. We used 1476 images, evaluated by an expert physician. Our regression DCNN contained four convolution layers and three fully connected layers. Consequently, we obtained a high correlation of 0.93 (p < 0.01). Furthermore, to scrutinize the effectiveness of the regression DCNN, we categorized breast composition using the estimated ratio obtained by the regression DCNN. The agreement rates are high at 84.8%, suggesting that the breast composition can be calculated using regression DCNN with high accuracy. Moreover, the occurrence of two-step errors or more is unlikely, and the proposed method can intuitively understand the estimated results.

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Mammographic breast density, body mass index and risk of breast cancer in Korean women aged 75 years and older

Cited by 6 publications

References 36 publications

Personalized Breast Cancer Screening: A Risk Prediction Model Based on Women Attending BreastScreen Norway

Personalized Breast Cancer Screening: A Risk Prediction Model Based on Women Attending BreastScreen Norway

Association of Breast Cancer Family History With Breast Density Over Time in Korean Women

Automated Estimation of Mammary Gland Content Ratio Using Regression Deep Convolutional Neural Network and the Effectiveness in Clinical Practice as Explainable Artificial Intelligence

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