Evaluation of a breast cancer risk prediction model expanded to include category of prior benign breast disease lesion

Abstract

Background: Benign breast diseases (BBD) encompass several histologic subtypes with various risks of subsequent breast cancer. Information on previous benign breast disease biopsies has been incorporated into breast cancer risk prediction models; however, the type of histologic lesion has not been taken into account. Given the substantial heterogeneity in breast cancer risk dependent on the type of benign lesion, the authors evaluated whether incorporating this level of detail would improve the discriminatory power of risk classification models.

Methods: By using data from the Nurses' Health Study, a breast cancer nested case-control study (240 cases; 1036 controls), the authors determined predictors of categories of BBD lesions and developed imputation models. The type of BBD, imputed for each cohort member who reported a diagnosis, was added to a modified version of the Rosner-Colditz breast cancer risk prediction model.

Results: Compared with the model that included only previous BBD (yes/no), the model that included categories of BBD was significantly improved (P<.0001). Overall, including the category of BBD increased the concordance statistic from 0.628 to 0.635. By using risk reclassification, inclusion of the type of BBD resulted in a 17% increase in incidence per increase of 1 risk decile, holding the model without BBD type risk decile constant.

Conclusions: Although the current data suggested that the inclusion of BBD category may improve breast cancer risk classification, the clinical utility of such a model will depend on the consistency of histologic classification of benign breast disease lesions.