Breast cancer is the most common invasive cancer for women all around the world. It is thus important to be able to predict who will more likely succumb to the disease, in order to provide these people with increased screening opportunities and disease prevention strategies. This is what breast cancer risk prediction models set out to do. These are mathematical models that incorporate information about given risk factors of the disease for a given woman. However, these models tend to only enjoy a moderate performance [1]. One potential reason is that while a lot of risk models incorporate factors that put women at high risk (such as family history, genetic factors or proliferative disease), such high risk factors are only present in a minority of the population [2-4].
The Tyrer-Cuzick risk prediction model has shown superior risk prediction to other models [5] and enjoys widespread acceptance by advisory bodies that make recommendations for screening and breast cancer prevention (American Cancer Society (ACS) USA, The National Institute for Health and Care Excellence (NICE) UK, US Preventative Services Task Force (USPSTF)). It calculates a woman’s absolute risk of the disease and her risk of carrying a high-risk BRCA1 or 2 mutation.
An exciting new development is imminent with the upcoming release of the 8th version of Tyrer-Cuzick—the incorporation of breast density as a risk factor. This is an important risk factor for breast cancer due to its relative risk [6] and common prevalence in the population [7]. There are three inputs of breast density included—the ACR 4th edition BI-RADS, the visual analogue scale (VAS) and Volpara Density. BI-RADS and VAS are both visual methods where the observer estimates the percentage of the breast that looks dense. However, they both rely on human judgement and suffer from considerable subjectivity and lack of agreement between observers [8, 9]. On the other hand, Volpara Density is an objective computer-based measure of breast density that avoids such issues [10].
Breast density is likely to play an important role in the Tyrer-Cuzick v8 model as evidenced by the risk it confers. A 50-year-old woman with no family history of breast cancer, proliferative disease and who does not use hormone replacement therapy (with all other risk factors remaining at the level of the general population) would have a lifetime disease risk of 9.9%—below the level of the general population. However, if the same woman had very dense breasts (BI-RADS category 4), her lifetime risk would jump to 19%. This would put her in the “moderate risk” category as dictated by ACS and would make her eligible for annual mammographic screening [11].
The incorporation of breast density into the Tyrer-Cuzick risk model will mean that this important risk factor is taken into consideration for official recommendations on supplementary screening and risk minimization strategies. Thus it could bring help to at-risk women who would not otherwise qualify for breast cancer prevention measures.
1. Meads, C., I. Ahmed, and R.D. Riley, A systematic review of breast cancer incidence risk prediction models with meta-analysis of their performance. Breast Cancer Res Treat, 2012. 132(2): p. 365-77.
2. Tice, J.A., et al., Breast Density and Benign Breast Disease: Risk Assessment to Identify Women at High Risk of Breast Cancer. Journal of Clinical Oncology, 2015. 33(28): p. 3137-3143.
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9. Sprague, B.L., et al., Variation in Mammographic Breast Density Assessments Among Radiologists in Clinical PracticeA Multicenter Observational StudyVariation in Mammographic Breast Density Assessments Among Radiologists. Annals of Internal Medicine, 2016. N/A(N/A): p. N/A-N/A.
10. Highnam, R., et al., Robust breast composition measurement–VolparaTM, in Lecture Notes in Computer Science, J. Marti and A. Oliver, Editors. 2010, Springer Berlin Heidelberg: Berlin, Heidelberg. p. 342-349.
11. Kim-Sing, C., L. Weir, and U. Kuusk, Breast cancer risk management for moderate-risk and high-risk women. BCMJ, 2004. 46(8): p. 397-401.
