Researchers have developed a new model to help predict the development of breast cancer in women with atypical hyperplasia (AH) based on a breast biopsy.
With approximately 10% of benign breast biopsies demonstrating AH, there are about 100,000 women newly diagnosed with AH in the United States each year. “As a group, women with AH have an approximate four-fold increase in risk compared with the general population, which translates to an absolute risk of 1% to 2% per year,” wrote study authors led by Amy C. Degnim, MD, of the Mayo Clinic in Rochester, Minnesota. “Because clinical management decisions, such as screening and prevention therapies, are made for individual women, a tool that provides accurate individualized risk prediction in the AH setting is needed.”
The researchers created a risk prediction model specifically for women with AH; the women included were aged 18 to 85 years and had pathologically confirmed benign AH. The model-building cohort included 699 women with AH, 142 of whom developed breast cancer (median follow-up, 8.1 years), while a validation cohort included another 461 women with AH, of whom 114 later developed breast cancer (median follow-up, 11.4 years). The final model, which they termed the AH-BC model, included age at biopsy, age at biopsy squared, and the number of AH foci. The results of the analysis were published in the Journal of Clinical Oncology.
In the model-building set, at 10 years, the model demonstrated good discrimination (0.63 [95% CI, 0.57–0.70]) and calibration (0.87 [95% CI, 0.66–1.24]). The ratio of predicted cancers to observed cancers at 5, 10, and 20 years was 0.96, 0.87, and 0.92, respectively.
In the validation cohort, the researchers wrote that the model showed “acceptable” discrimination (0.59 [95% CI, 0.51–0.67]) and calibration (0.91 [95% CI, 0.65–1.42]). At 5 years, the AH-BC model over-predicted the number of cancers, with a ratio of 1.46, but it was better calibrated at 10 years (observed-to-predicted ratio of 0.91) and at 20 years (ratio of 1.02).
“Accurate risk prediction for women with AH is important because clinical management can be refined on the basis of risk level,” the authors wrote. “This model is an important step in improving risk prediction for women with AH.”