Breast density improves prediction of long-term breast cancer risk
A risk model that combined classical risk factors with breast density information stratified women at high risk for breast cancer up to 19 years after assessment, according to findings published in JAMA Oncology.
“These results lend support to the use of such models to guide long-term, systematic, risk-adapted screening and prevention strategies to minimize both intervention-associated harms and the public health burden of breast cancer,” Adam R. Brentnall, PhD, senior statistician at Wolfson Institute of Preventive Medicine of Queen Mary University of London, told HemOnc Today.
Risk-based breast cancer screening has not been formally adopted in the United States and elsewhere but may improve preventative efforts for patients at risk for breast cancer. However, to implement risk-adapted screening, accurate risk assessment is needed.
Several risk models have determined the residual lifetime risk for a woman by year; however, validation studies have been limited.
“U.S. guidelines recommend lifetime breast cancer risk assessment to determine who should be offered supplemental screening by breast MRI, but most of the data reported on risk model accuracy have been to 5 or 10 years from risk assessment,” Brentnall told HemOnc Today.
Further, studies have shown the Tyrer-Cuzick risk model — which incorporates classic breast cancer risk factors, including information on affected second- and third-degree relatives, BMI, menopause and hormone therapy — identifies few women in the general population as high risk. Also, the performance of the Tyrer-Cuzick model has not been directly assessed in a cohort study from a screening population in the U.S.
Brentnall and colleagues sought to determine performance of the model at different follow-up times and how accuracy of the model improved by adding the Breast Imaging and Reporting Data System measure of breast density.
Researchers evaluated data from 132,139 women in the Kaiser Permanente Washington Breast Cancer Surveillance Consortium registry who underwent mammography screening from 1996 through 2013.
All women were aged 40 to 73 years at time of entry (median age, 50 years; interquartile range [IQR], 44-58), attended at least one screening visit with Breast Imaging and Reporting Data System density recorded, and did not have prior diagnosis of invasive breast cancer or ductal carcinoma in situ.
The time from 6 months after the cohort entry questionnaire to diagnosis of invasive breast cancer or censoring at age 75 years served as the primary outcome.
During median follow-up of 5.2 years (IQR, 2.4-11.1) — with a maximum follow-up of 19 years — researchers observed 2,699 invasive breast cancer cases, for an annual incidence rate of 1.9 cases per 1,000 women.
Researchers calculated observed divided by expected (O/E) numbers of diagnoses, which they compared using exact Poisson 95% CIs.
Results showed good calibration of absolute risk for both the Tyrer-Cuzick model (O/E = 1.02; 95% CI, 0.98-1.06) and Tyrer-Cuzick model with density (O/E = 0.98; 95% CI, 0.94-1.02).
The Tyrer-Cuzick model estimated 2,554 women were high risk (1.8%), defined as a 10-year risk greater than 8%.
Of these, 147 women developed invasive breast cancer (O/E = 0.79; 95% CI, 0.67-0.93), for an incidence rate of 8.7 cases per 1,000 women.
The Tyrer-Cuzick model with density estimated 4,645 women were high risk (3.5%). Of these, 273 cases of invasive breast cancer developed (O/E = 0.78; 95% CI, 0.69-0.88), for an incidence rate of 9.2 cases per 1,000 women. Researchers noted that risk was overestimated in this group and risk was underestimated in women with 10-year risk of less than 2%.
Further, overestimation of the highest risk decile compared with the middle 80% was probable. The HR for top decile was 2.22 (95% CI, 2.02-2.45) for the Tyrer-Cuzick model and 2.55 (95% CI, 2.33-2.8) for the Tyrer-Cuzick model with density.
The HR for the bottom decile was 0.5 (95% CI, 0.42-0.61) for the Tyrer-Cuzick model compared with 0.36 (95% CI, 0.29-0.45) for the Tyrer-Cuzick model with density.
“Incorporating density in the model provided a greater range of observed risk between the top and bottom deciles,” the researchers wrote. – by Melinda Stevens
Disclosures: Brentnall and another author report royalties through Cancer Research UL for commercial use of the Tyrer-Cuzick algorithm. The other authors report no relevant financial disclosures.