Breast cancer screening is rapidly moving into an era of personalized care, where prevention, early detection, and treatment are guided by risk assessment.
Currently, the most comprehensive breast cancer risk assessment model available is the Tyrer-Cuzick risk model version 8 (TCv8).1 It is recognized globally as a valuable tool for determining appropriate pathways for therapeutic intervention and/or supplementary screening.2,3,4 However, there is little information about how to use TCv8 clinically to obtain the most accurate lifetime risk estimates.
In our work helping to implement programs for women at high risk of breast cancer, we have seen the following best practices make risk assessment and triage more efficient and precise.
Breast density as a risk factor
There is overwhelming evidence that breast density is an independent risk factor for the development of breast cancer.5,6 In May of 2019, the first validation paper was published showing the value breast density added to the Tyrer-Cuzick risk model.7 The results showed that inclusion of breast density, with other classical risk factors, increased the number of women accurately identified at higher and lower risk of breast cancer.
Two clinically practical breast density measurements validated for use in TCv8 are the following:
- Volpara volumetric breast density percentage (VBD%): The validated density input for the use of VBD% is the average of the left and right breast VBD% as measured by VolparaDensity.
- BI-RADS ATLAS density: The validated density input for the use of BI-RADS ATLAS density is the visual assessment using BI-RADS fourth edition guidelines (1/2/3/4). The assessment should then be mapped to the BI-RADS fifth edition equivalent (1 → a, 2 → b, 3 → c, 4 → d) before entering the density grade into the model.
Takeaway: Using a validated measure of breast density adds significant value to the TCv8 risk assessment model.
Different measurement methods impact risk scores
There are key differences in the two methods of breast density measurement validated for use in the Tyrer-Cuzick risk model:
To see these differences in action, imagine three women walk into a breast screening clinic with exactly the same risk factors and a visual breast density of BI-RADS 3, which we would enter as "C." What happens when their unique volumetric breast density percentage (VBD%) is entered into TCv8 as opposed to the BI-RADS score?
Assuming these consistent risk factors (let's say, age: 55 years; height: 5'4"; weight: 130 lb; children: no; menopause: 52 years; mother BC: 60 years) and their unique VBD%, the lifetime risk score calculated by TCv8 changes significantly.
Professor Jack Cuzick states that compared to BI-RADS, "volumetric density has some practical advantages because it is fully automated with excellent agreement with 3D magnetic resonance imaging."
Takeaway: Volumetric breast density can give a more accurate reflection of risk because of the continuous nature of both the risk model and density calculation.
Attention to competing mortality
A breast cancer risk assessment provides a realistic estimate of a woman's risk of developing breast cancer over a period of time. However, there is also a risk that a woman may die from other competing causes of mortality before breast cancer can manifest -- this is absolute risk. If competing mortality is not included in the risk calculation, a woman's lifetime risk score may be inflated and less representative of her actual risk.11,12
For example, a woman with risk factors of age: 47 years; height: 5'4"; weight: 130 lb; and grandmother BC: 68 years will have a different lifetime risk score depending on whether her breast density is reported using visual BI-RADS or VBD%.
Note that competing mortality is not turned on by default in the Tyrer-Cuzick risk assessment model.13
Takeaway: Confirm that competing mortality is turned on to avoid erroneous inflation of your patient risk scores.
The Tyrer-Cuzick breast cancer risk assessment model is one of our strongest tools to identify women who may be at a higher risk of developing breast cancer over time -- but it requires diligent use of clinical best practices to be effective.
Interested in learning more? Download Volpara Health's TCv8 five-step best practice guide for more considerations for your practice.
- Terry, M.B. et al. 10-year performance of four models of breast cancer risk: a validation study. Lancet Oncol 20, 504–517 (2019).
- The American Society of Breast Surgeons. Position Statement on Screening Mammography. 2019.
- Radiology, A.C.o. ACR Position Statement 2012 [cited]. Available from: http://www.acr.org/About-Us/Media-Center/Position-Statements/Position-Statements-Folder/Statement-on-Reporting-Breast-Density-in-Mammography-Reports-and-Patient-Summaries.
- National Comprehensive Cancer Network. NCCN Clinical Practice Guidelines in Oncology Breast Cancer Screening and Diagnosis. 2019.
- Boyd, N.F. et al. Breast tissue composition and susceptibility to breast cancer. J Natl Cancer Inst 102, 1224–1237 (2010).
- Assi, V., Warwick, J., Cuzick, J. & Duffy, S.W. Clinical and epidemiological issues in mammographic density. Nature Reviews Clinical Oncology 9, 33–40 (2012).
- Brentnall, A.R. et al. A Case-Control Study to Add Volumetric or Clinical Mammographic Density into the Tyrer-Cuzick Breast Cancer Risk Model. Journal of Breast Imaging (2019).
- Sartor, H. et al. Measuring mammographic density: comparing a fully automated volumetric assessment versus European radiologists' qualitative classification. Eur Radiol 26, 4354–4360 (2016).
- Eom, H.-J. et al. Comparison of variability in breast density assessment by BI-RADS category according to the level of experience. Acta Radiologica 59, 527–532 (2018).
- Mainiero, M.B. et al. ACR Appropriateness Criteria((R)) Breast Cancer Screening. J Am Coll Radiol 14, S383-s390 (2017).
- Gail, M.H. Twenty-five Years of Breast Cancer Risk Models and Their Applications. JNCI: Journal of the National Cancer Institute 107 (2015).
- Himes, D.O., Root, A.E., Gammon, A. & Luthy, K.E. Breast Cancer Risk Assessment: Calculating Lifetime Risk Using the Tyrer-Cuzick Model. The Journal for Nurse Practitioners 12, 581–592 (2016).
- Cuzick, J. IBIS Breast Cancer Risk Evaluation Tool. 2017 [cited 2019 November 12]. Available from: http://www.ems-trials.org/riskevaluator/.