More accurate breast cancer risk prediction model

Volume 4, Issue 2, 2015

Summary

New model is based on biopsied breast tissue and patient demographic data.

Photograph of Amy C. Degnim, M.D.

Amy C. Degnim, M.D.

A new breast cancer risk prediction model developed at the Mayo Clinic Cancer Center may more accurately classify breast cancer risk than the current screening standard, researchers say.

Results of a Mayo Clinic study comparing the new model to the current standard — the Breast Cancer Risk Assessment Tool (BCRAT) — were published in the March 10, 2015, issue of the Journal of Clinical Oncology.

"Physicians routinely perform biopsies to evaluate concerning findings in the breast, either felt on exam or seen on mammogram, for the presence of a breast cancer," said Amy C. Degnim, M.D., a surgeon at the Mayo Clinic Cancer Center and a senior author of the study. "However, about three-quarters of these biopsies prove to be benign and are referred to as benign breast disease."

More than a million American women each year have a biopsy with a benign finding, and they may be left wondering if they will later develop breast cancer.

Dr. Degnim and her colleagues hypothesized that even benign breast tissue findings can help predict which women are at increased risk of developing breast cancer later.

To test the new model, Dr. Degnim and her colleagues studied a cohort of about 10,000 women who had benign breast biopsies at Mayo Clinic and who received long-term follow-up for a later breast cancer occurrence.

Using this cohort, researchers determined the age-specific incidence of breast cancer and death. They combined these estimates with a relative risk model derived from 377 patients who later developed breast cancer and 734 matched controls sampled from the Mayo Clinic benign breast disease cohort.

The researchers validated the new model using an independent set of women from the benign breast disease cohort (378 patients with a later breast cancer and 728 matched controls) and compared the risk predictions from the new model to those from the BCRAT.

The concordance statistic from the new model was 0.665 in the model development series and 0.629 in the validation series; these values were higher than those from the BCRAT (0.567 and 0.472, respectively).

The BCRAT significantly underpredicted breast cancer risk after benign biopsy (P = 0.004), whereas predictions derived from the new Mayo Clinic model were appropriately calibrated to observed cancers (P = 0.247), researchers concluded.

"Since women with benign breast disease are at higher risk of breast cancer, optimal early detection is extremely important," Dr. Degnim said. "Ideally, women at increased risk of breast cancer should be identified so that we can offer appropriate surveillance and prevention strategies. Unfortunately, the BCRAT risk prediction model does not provide accurate estimates of risk for these women at the individual level."