IntroductionMammography screening results in a significant number of false-positives. The use of pre-test breast cancer risk factors to guide follow-up of abnormal mammograms could improve the positive predictive value of screening. We evaluated the use of the Gail model, body mass index (BMI), and genetic markers to predict cancer diagnosis among women with abnormal mammograms. We also examined the extent to which pre-test risk factors could reclassify women without cancer below the biopsy threshold.MethodsWe recruited a prospective cohort of women referred to biopsy with abnormal (BI-RADS 4) mammograms. Breast cancer risk factors were assessed prior to biopsy. A validated panel of 12 single nucleotide polymorphisms (SNPs) associated with breast cancer were measured. Logistic regression was used to assess the association of Gail risk factors, BMI and SNPs with cancer diagnosis (invasive or DCIS). Model discrimination was assessed using area under the receiver operating curve and calibration was assessed using the Hosmer-Lemeshow goodness of fit test. Finally, the distribution of predicted probabilities of cancer diagnosis were compared for women with and without breast cancer.ResultsIn the multivariate model, age (OR¿=¿1.05, 95% CI 1.03 to 1.08 P <0.001), SNP panel relative risk (OR¿=¿2.30, 95% CI 1.06 to 4.99, P¿=¿0.035), and BMI (¿30 kg/m2 versus <25 kg/m2, OR¿=¿2.20, 95% CI 1.05 to 4.58, P¿=¿0.036) were significantly associated with breast cancer diagnosis. Older women were more likely to be diagnosed with breast cancer. The SNP Panel RR remained strongly associated with breast cancer diagnosis after multivariable adjustment. Higher BMI was also strongly associated with increased odds of breast cancer diagnosis. Obese women (OR¿=¿2.20, 95% CI 1.05 to 4.58, P¿=¿0.036) had more than twice the odds of cancer diagnosis compared to women with BMI <25 kg/m2. The SNP Panel appeared to have predictive ability among both white and black women.ConclusionsBreast cancer risk factors, including BMI and genetic markers are predictive of cancer diagnosis among women with BI-RADS 4 mammograms. Using pre-test risk factors to guide follow-up of abnormal mammograms could reduce the burden of false-positive mammograms.