Exploring the prediction performance for breast cancer risk based on volumetric mammographic density at different thresholds
Author:
Funder
Cancer Research UK
Programme Grants for Applied Research
Genesis Prevention Appeal
Publisher
Springer Science and Business Media LLC
Link
http://link.springer.com/content/pdf/10.1186/s13058-018-0979-x.pdf
Reference22 articles.
1. Assi V, Warwick J, Cuzick J, Duffy SW. Clinical and epidemiological issues in mammographic density. Nat Rev Clin Oncol. 2012;9(1):33–40.
2. Hopper JL. Odds per adjusted standard deviation: comparing strengths of associations for risk factors measured on different scales and across diseases and populations. Am J Epidemiol. 2015;182(10):863–7.
3. Byng JW, Boyd NF, Fishell E, Jong RA, Yaffe MJ. The quantitative-analysis of mammographic densities. Phys Med Biol. 1994;39(10):1629–38.
4. Nguyen TL, Aung YK, Evans CF, Yoon-Ho C, Jenkins MA, Sung J, Hopper JL, Song Y-M. Mammographic density defined by higher than conventional brightness threshold better predicts breast cancer risk for full-field digital mammograms. Breast Cancer Res. 2015;17:1-9.
5. Nguyen TL, Choi Y-H, Aung YK, Evans CF, Trinh NH, Li S, Dite GS, Kim MS, Brennan PC, Jenkins MA, et al. Breast cancer risk associations with digital mammographic Density by pixel brightness threshold and mammographic system. Radiology. 2018;286(2):433–42.
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