Utility of relative and absolute measures of mammographic density vs clinical risk factors in evaluating breast cancer risk at time of screening mammography

作者：Abdolell Mohamed^*; Tsuruda Kaitlyn M; Lightfoot Christopher B; Payne Jennifer I; Caines Judy S; Iles Sian E

来源：British Journal of Radiology, 2016, 89(1059): 20150522.

DOI：10.1259/bjr.20150522

Objective: Various clinical risk factors, including high breast density, have been shown to be associated with breast cancer. The utility of using relative and absolute area-based breast density-related measures was evaluated as an alternative to clinical risk factors in cancer risk assessment at the time of screening mammography.
Methods: Contralateral mediolateral oblique digital mammography images from 392 females with unilateral breast cancer and 817 age-matched controls were analysed. Information on clinical risk factors was obtained from the provincial breast-imaging information system. Breast density-related measures were assessed using a fully automated breast density measurement software. Multivariable logistic regression was conducted, and area under the receiver-operating characteristic (AUROC) curve was used to evaluate the performance of three cancer risk models: the first using only clinical risk factors, the second using only density-related measures and the third using both clinical risk factors and density-related measures.
Results: The risk factor-based model generated an AUROC of 0.535, while the model including only breast density-related measures generated a significantly higher AUROC of 0.622 (p<0.001). The third combined model generated an AUROC of 0.632 and performed significantly better than the risk factor model (p< 0.001) but not the density-related measures model (p=0.097).
Conclusion: Density-related measures from screening mammograms at the time of screen may be superior predictors of cancer compared with clinical risk factors.
Advances in knowledge: Breast cancer risk models based on density-related measures alone can outperform risk models based on clinical factors. Such models may support the development of personalized breast-screening protocols.

• 出版日期2016