Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA.
JNCI Cancer Spectr. 2023 Jul 3;7(4). doi: 10.1093/jncics/pkad041.
Mammographic density is a strong predictor of breast cancer but only slightly increased the discriminatory ability of existing risk prediction models in previous studies with limited racial diversity. We assessed discrimination and calibration of models consisting of the Breast Cancer Risk Assessment Tool (BCRAT), Breast Imaging-Reporting and Data System density and quantitative density measures. Patients were followed up from the date of first screening mammogram until invasive breast cancer diagnosis or 5-year follow-up. Areas under the curve for White women stayed consistently around 0.59 for all models, whereas the area under the curve increased slightly from 0.60 to 0.62 when adding dense area and area percent density to the BCRAT model for Black women. All women saw underprediction in all models, with Black women having less underprediction. Adding quantitative density to the BCRAT did not statistically significantly improve prediction for White or Black women. Future studies should evaluate whether volumetric breast density improves risk prediction.
乳腺密度是乳腺癌的一个强有力的预测指标，但在以前的研究中，由于种族多样性有限，它只是略微提高了现有风险预测模型的区分能力。我们评估了由乳腺癌风险评估工具（BCRAT）、乳腺成像报告和数据系统密度以及定量密度测量组成的模型的区分度和校准度。患者从首次筛查乳房 X 光检查的日期开始随访，直到浸润性乳腺癌诊断或 5 年随访。对于白人女性，所有模型的曲线下面积始终保持在 0.59 左右，而对于黑人女性，当将致密区域和面积百分比密度添加到 BCRAT 模型中时，曲线下面积从 0.60 略微增加到 0.62。所有女性在所有模型中都存在低估，黑人女性的低估程度较小。向 BCRAT 添加定量密度并没有在统计学上显著改善白人或黑人女性的预测。未来的研究应评估体积乳腺密度是否能改善风险预测。
