Mayo Clinic researchers have developed a breast cancer risk prediction model that they believe more accurately classifies breast cancer risk than the current screening standard, according to a new study published in the Journal of Clinical Oncology.
Dr. Amy Degnim and colleagues at the Mayo Clinic Cancer Center in Rochester, MN, created a model that combined histologic features of biopsied breast tissue from women with benign breast disease and individual patient demographic information, and compared it with the current standard, the Breast Cancer Risk Assessment Tool (BCRAT).
The researchers studied 10,000 women who had benign breast biopsies at the Mayo Clinic and who had long-term follow-up for a later breast cancer occurrence. They examined the cohort's age-specific incidence of breast cancer and death, and they used the data in a relative risk model derived from 377 patients who later developed breast cancer and 734 matched controls.
They then validated the model using another set of women from the Mayo cohort (378 patients with a later breast cancer and 728 matched controls) and compared the risk predictions from the new model with those from BCRAT (J Clin Oncol, January 26, 2015).
The new model's concordance rate was 0.665 in the model development series and 0.629 in the validation series -- higher than rates from BCRAT, which were 0.567 and 0.472, respectively.
"Ideally, women at increased risk for breast cancer should be identified so that we can offer appropriate surveillance and prevention strategies," Degnim said in a statement released by the Mayo Clinic. "Unfortunately, the BCRAT risk prediction model does not provide accurate estimates of risk for these women at the individual level."
