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. 2023 Sep 1;16(9):531-537.
doi: 10.1158/1940-6207.CAPR-22-0499.

Association and Prediction Utilizing Craniocaudal and Mediolateral Oblique View Digital Mammography and Long-Term Breast Cancer Risk

Simin Chen  1 , Rulla M Tamimi  2 , Graham A Colditz  1   3 , Shu Jiang  1   3
Affiliations

Association and Prediction Utilizing Craniocaudal and Mediolateral Oblique View Digital Mammography and Long-Term Breast Cancer Risk

Simin Chen et al. Cancer Prev Res (Phila). .

Abstract

Mammographic percentage of volumetric density is an important risk factor for breast cancer. Epidemiology studies historically used film images often limited to craniocaudal (CC) views to estimate area-based breast density. More recent studies using digital mammography images typically use the averaged density between craniocaudal (CC) and mediolateral oblique (MLO) view mammography for 5- and 10-year risk prediction. The performance in using either and both mammogram views has not been well-investigated. We use 3,804 full-field digital mammograms from the Joanne Knight Breast Health Cohort (294 incident cases and 657 controls), to quantity the association between volumetric percentage of density extracted from either and both mammography views and to assess the 5 and 10-year breast cancer risk prediction performance. Our results show that the association between percent volumetric density from CC, MLO, and the average between the two, retain essentially the same association with breast cancer risk. The 5- and 10-year risk prediction also shows similar prediction accuracy. Thus, one view is sufficient to assess association and predict future risk of breast cancer over a 5 or 10-year interval.

Prevention relevance: Expanding use of digital mammography and repeated screening provides opportunities for risk assessment. To use these images for risk estimates and guide risk management in real time requires efficient processing. Evaluating the contribution of different views to prediction performance can guide future applications for risk management in routine care.

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Figures

Figure 1. Linear correlation and scatter plots; all variables are square root transformed. A, MLO-VPD and CC-VPD. B, CC-VPD and Full-VPD. C, MLO-VPD and Full-VPD.
Figure 1.
Linear correlation and scatter plots; all variables are square root transformed. A, MLO-VPD and CC-VPD. B, CC-VPD and Full-VPD. C, MLO-VPD and Full-VPD.
Figure 2. 5 and 10-year breast cancer–risk prediction for three different models. The reported AUCs are based on Uno's cumulative 5 and 10-year AUC estimated by averaging over a 10-fold cross-validation.
Figure 2.
5 and 10-year breast cancer–risk prediction for three different models. The reported AUCs are based on Uno's cumulative 5 and 10-year AUC estimated by averaging over a 10-fold cross-validation.
See this image and copyright information in PMC

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References

    1. Boyd NF, Rommens JM, Vogt K, Lee V, Hopper JL, Yaffe MJ, et al. . Mammographic breast density as an intermediate phenotype for brea cancer. Lancet Oncol 2005;6:798–808. - PubMed
    1. Eriksson M, Eklund M, Borgquist S, Hellgren R, Margolin S, Thoren L, et al. . Low-dose tamoxifen for mammographic density reduction: a randomized controlled trial. J Clin Oncol 2021;39:1899–908. - PMC - PubMed
    1. Kleinstern G, Scott CG, Tamimi RM, Jensen MR, Pankratz VS, Bertrand KA, et al. . Association of mammographic density measures and breast cancer "intrinsic" molecular subtypes. Breast Cancer Res Treat 2021;187:215–24. - PMC - PubMed
    1. Burton A, Byrnes G, Stone J, Tamimi RM, Heine J, Vachon C, et al. . Mammographic density assessed on paired raw and processed digital images and on paired screen-film and digital images across three mammography systems. Breast Cancer Res 2016;18:130. - PMC - PubMed
    1. Warner ET, Rice MS, Zeleznik OA, Fowler EE, Murthy D, Vachon CM, et al. . Automated percent mammographic density, mammographic texture variation, and risk of breast cancer: a nested case–control study. NPJ Breast Cancer 2021;7:68. - PMC - PubMed

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