The relationship between breast density, age, and mammographic lesion type among Chinese breast cancer patients from a large clinical dataset

基于大型临床数据集的中国乳腺癌患者乳腺密度、年龄和乳腺X线病变类型之间的关系

阅读:1

Abstract

BACKGROUND: The purpose of this study was to investigate the relationship between breast density, age, and mammographic lesion type among Chinese breast cancer patients included in a large clinical dataset. METHODS: A review of mammographic images acquired between July 2014 and June 2017 from a total of 9716 retrospectively registered breast cancer patients was conducted. Mammographic breast density was defined according to the American College of Radiology Breast Imaging Reporting and Data System (ACR BI-RADS) 4-class density rating. Mammographic lesion types were defined according to the ACR BI-RADS, including mass, mass with calcifications, calcifications, architectural distortion/asymmetries, and architectural distortion/asymmetries with calcifications. Three experienced breast radiologists interpreted all mammograms. The chi-square (χ(2)) test and Pearson correlation analyses were performed to assess the relationship between breast density, age, and mammographic lesion type. RESULTS: A significant inverse relationship was observed between the BI-RADS breast density rating given by radiologists and patient age (r = - 0.521, p < 0.01). The breast density distribution in breast cancer patients from China reversed at the age of 55 years, and exhibited one age peak in the age 55-59 year group. The percentage of lesions with calcifications decreased with increasing age (p < 0.01), and increased with increasing breast density (p < 0.01). CONCLUSIONS: In general, we identified a relationship between patient breast density, age, and mammographic lesion type. This finding may provide a basis for clinical diagnoses and support development of breast cancer screening programs in China.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。