Breast cancer was the second most commonly diagnosed cancer in women, after skin cancer – accounting for 31% of all new female cancer diagnoses in 2022. Yet, determining who is most at risk of breast cancer is still a challenge for the medical community. Physicians use risk assessment models to determine when to start screening, the frequency of screening and need for prevention.

A leading model is the Breast Cancer Surveillance Consortium (BCSC) risk assessment calculator, which uses information about a woman’s age, race and ethnicity, first-degree family history of breast cancer, breast density and benign breast biopsy results to estimate a woman’s five- and 10-year absolute risk of developing invasive breast cancer. BCSC is a nationwide research network with community-based data collection from geographically and socio-demographically diverse settings that evaluates the benefits and harms of different screening approaches.

In a study publishing Nov. 17, 2023 in the Journal of Clinical Oncology, UC San Francisco researchers analyzed data from over 5 million screening and diagnostic mammograms to develop an updated BCSC model that adds new risk factors, including body mass index (BMI), second degree relatives with a family history of breast cancer and age at first live birth to improve model prediction.

“The new BCSC model (v3) updates an already well calibrated and validated breast cancer risk assessment tool to include additional important risk factors,” said Jeffrey Tice, MD, UCSF professor of Medicine, specializing in breast cancer risk assessment. “The inclusion of BMI was associated with the largest improvement in estimated risk for individual women.”

The researchers analyzed data from 1,455,493 women ages 35 to 79 without a history of breast cancer. During an average follow-up period of 7.3 years, 30,266 women were diagnosed with invasive breast cancer, breast cancer that has spread into surrounding breast tissue. The newly adopted BCSC model (v3) improved prediction of the five-year risk compared with the BCSC (v2) model. The new model showed the most improvement among Asians, Whites and Blacks. Among obese women with a BMI of 30 to 34.9 kg/m2, the true-positive rate in women with an estimated five-year risk of 3% or higher increased from 10% (v2) to 19.8% (v3), and the improvement was even more among obese women with a BMI ≥35 kg/m2 – from 7.6% to 19.8%.

First to use body mass index

In addition, current guidelines for the use of medicines to lower a woman’s risk for breast cancer are based on a women’s risk for invasive cancer in the next five years. The BCSC model (v3) is particularly useful to help guide decision-making in this context.

The BCSC risk model is recommended by the United States Preventive Services Task Force to identify women eligible for primary prevention with tamoxifen or an aromatase inhibitor.  “Incorporating body mass index into the v3 model more accurately identifies overweight and obese women eligible for taking medication to reduce their risk of breast cancer,” said senior author Karla Kerlikowske, MD, UCSF professor in the Departments of Medicine and Epidemiology and Biostatistics and co-PI of the BCSC.

The BCSC model was the first to incorporate clinical mammographic breast density. Where breast tissue is dense, a mammogram of the tissue appears cloudy or opaque, making it more challenging to accurately detect cancer at its earliest stages and increasing breast cancer risk. The BCSC model provides risk information specific to the woman’s breast density assessment as well as other risk factors. Patients’ five- and 10-year risk for invasive cancer is estimated and the risk for an average woman of her age and race/ethnicity is provided for comparison and context.

“The updated BCSC model (v3) can help provide context for discussions between patients and their providers when a woman learns that she has dense breasts on her mammogram,” said Tice.

The research team will continue to use the BCSC database to help improve screening and surveillance. Tice hopes the findings will contribute to public health efforts to promote a more efficient risk-based screening approach to reduce breast cancer disparities.

This story was published by the University of California San Francisco on November 20, 2023. It is republished with permission.