How would you respond if a patient asked, "What is my risk of getting breast cancer"?
We may initially reference the national average: 1 in 8 (or about 13% of) people assigned female at birth (AFAB) develop breast cancer.1 However, we live in an age of personalized medicine; this statistic cannot be applied universally to every patient. Further, the American Cancer Society recommends that patients with a lifetime risk greater than 20% be offered supplemental breast MRI as part of a high-risk screening protocol, so determining each patient's individual risk is vital.
But how do we personalize a patient’s breast cancer risk assessment?
Providers and patients are becoming more aware than ever that family history can significantly influence the risk of cancer. However, other factors, such as age, physical and hormonal factors also affect the risk of breast cancer. On top of it, personal and family history changes year over year, further complicating the process to provide an accurate assessment of risk.
As part of The Ambry CARE ProgramTM (CARE), which stands for “Comprehensive Assessment Risk and Education,” patients AFAB have their breast cancer risk calculated based on the validated Tyrer-Cuzick risk model (version 8). Tyrer-Cuzick (TC) accounts for family history and other aspects of a patient's health history that influence the risk of developing breast cancer. Patients complete their initial assessment and provide annual updates, delivering a living score that evolves over the time of care with their healthcare provider. With this information, providers are informed of each patient's current breast cancer risk and can tailor their screening appropriately.
Ambry continues to enhance the CARE platform so healthcare teams can provide more patient-centric, proactive care.
Some of the most recent updates include optional features that more finely tune a patient's breast cancer risk assessment: competing mortality, breast density, and unaffected relatives.
• CARE sites now have the option to include competing mortality in their patient's breast cancer risk assessment. Competing mortality is the likelihood that a person will die from something other than a breast cancer diagnosis or recurrence (like heart disease, for example). Sites can customize their competing mortality setting to off, on, or show both values. Accounting for competing mortality more accurately reflects a patient’s breast cancer risk, especially over longer ranges of time.2
• Breast density by Volpara, BI-RADS, or Visual Analog Scale (VAS) can be calculated into the patient's assessment for a more refined personal risk score. CARE sites can opt to have a BI-RADS reference table included on the patient's clinical summary. They may also opt to manually input the breast density to update the patient's TC score. Including breast density information improves the predictive power of the TC score.3
• Lastly, CARE now enables providers to enter unaffected relatives AFAB in a patient's family history. By accounting for the number of these family members that have not developed breast cancer, and their degree of relationship, the patient's risk is even further stratified. Having relatives without breast cancer lowers the breast cancer risk score.
Including factors like competing mortality, breast density, and unaffected relatives provide the most equitable and personalized assessment of risk.
Through CARE, Ambry strives to enable healthcare providers to seamlessly provide personalized breast cancer risk assessment for all patients, while modifying their options to fit the needs of their patient population. Identifying patients at high risk for breast cancer empowers patients and their healthcare team to take action with a personalized breast cancer screening plan.
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Sources:
1. seer.cancer.gov
2. Brentnall, A. R., & Cuzick, J. (2020). Risk Models for Breast Cancer and Their Validation. Statistical science : a review journal of the Institute of Mathematical Statistics, 35(1), 14–30. https://doi.org/10.1214/19-STS729
3. Brentnall, A. R., Cohn, W. F., Knaus, W. A., Yaffe, M. J., Cuzick, J., & Harvey, J. A. (2019). A Case-Control Study to Add Volumetric or Clinical Mammographic Density into the Tyrer-Cuzick Breast Cancer Risk Model. Journal of breast imaging, 1(2), 99–106. https://doi.org/10.1093/jbi/wbz006
