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|Title:||Characteristics of Mammographic Breast Density and Associated Factors for Chinese Women: Results from an Automated Measurement|
|Authors:||Li T; Tang L; Gandomkar Z; Heard R; Mello-Thoms C; Xiao Q; Gu Y; Di G; Nickson C; Shao Z; Brennan P|
|Categories:||Cancer Type - Breast Cancer|
|Journal Title:||Journal of Oncology|
|Abstract:||Background. Characteristics of mammographic density for Chinese women are understudied. This study aims to identify factors associated with mammographic density in China using a quantitative method. Methods. Mammographic density was measured for a total of 1071 (84 with and 987 without breast cancer) women using an automatic algorithm AutoDensity. Pearson tests examined relationships between density and continuous variables and t-tests compared differences of mean density values between groupings of categorical variables. Linear models were built using multiple regression. Results. Percentage density and dense area were positively associated with each other for cancer-free (r=0.487, p<0.001) and cancer groups (r=0.446, p<0.001), respectively. For women without breast cancer, weight and BMI (p<0.001) were found to be negatively associated (r=-0.237, r=-0.272) with percentage density whereas they were found to be positively associated (r=0.110, r=0.099) with dense area; age at mammography was found to be associated with percentage density (r=-0.202, p<0.001) and dense area (r=-0.086, p<0.001) but did not add any prediction within multivariate models; lower percentage density was found within women with secondary education background or below compared to women with tertiary education. For women with breast cancer, percentage density demonstrated similar relationships with that of cancer-free women whilst breast area was the only factor associated with dense area (r=0.739, p<0.001). Conclusion. This is the first time that mammographic density was measured by a quantitative method for women in China and identified associations should be useful to health policy makers who are responsible for introducing effective models of breast cancer prevention and diagnosis.|
|Division:||Cancer Research Division|
|Appears in Collections:||Research Articles|
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