Mohamed Abdolell1,2, Kaitlyn M Tsuruda2, Christopher B Lightfoot1,2, Jennifer I Payne1,3,4, Judy S Caines1,2,3, Sian E Iles1,2. 1. 1 Department of Diagnostic Radiology, Dalhousie University, Halifax, NS, Canada. 2. 2 Department of Diagnostic Imaging, Nova Scotia Health Authority, Halifax, NS, Canada. 3. 3 Nova Scotia Breast Screening Program, Halifax, NS, Canada. 4. 4 Department of Community Health and Epidemiology, Dalhousie University, Halifax, NS, Canada.
Abstract
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.
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.
Authors: Karla Kerlikowske; Weiwei Zhu; Anna N A Tosteson; Brian L Sprague; Jeffrey A Tice; Constance D Lehman; Diana L Miglioretti Journal: Ann Intern Med Date: 2015-05-19 Impact factor: 25.391
Authors: Celine M Vachon; Carla H van Gils; Thomas A Sellers; Karthik Ghosh; Sandhya Pruthi; Kathleen R Brandt; V Shane Pankratz Journal: Breast Cancer Res Date: 2007 Impact factor: 6.466