New model more accurately classifies breast cancer risk

the ONA take:

Every year in America, more than a million women have breast biopsies that turn out to have benign findings. A new model, tested by a group of researchers led by Amy Degnim, M.D., a surgeon at Mayo Clinic, uses histologic features of biopsied breast tissue from benign findings along with demographic data to more accurately determine future breast cancer risk.

To test their new model, the investigators examined a cohort of approximately ten thousand women who received benign breast biopsy results from the Mayo Clinic but later developed breast cancer. The resulting age-specific incidence data was combined with a relative risk model created from both matched controls and 377 patients that later developed breast cancer.

Risk predictions from the new model were compared to the current standard, the Breast Cancer Risk Assessment Tool (BCRAT). The concordance statistic (both in the model development series and the validation series) was found to be higher in the new model than from the BCRAT.

A more accurate model for early breast cancer prediction could prove extremely useful, given that women with benign breast disease stand a higher risk of eventually developing breast cancer. This research was published in the Journal of Clinical Oncology.

New model more accurately classifies breast cancer risk
Histologic features of biopsied breast tissue from benign findings along with demographic data to more accurately determine risk.
A new breast cancer risk prediction model combining histologic features of biopsied breast tissue from women with benign breast disease and individual patient demographic information more accurately classified breast cancer risk than the current screening standard. Results of a Mayo Clinic study comparing the new model to the current standard, the Breast Cancer Risk Assessment Tool (BCRAT), are published in the Journal of Clinical Oncology.
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