Objective To assess the cancer risk in children and adolescents following exposure to low dose ionising radiation from diagnostic computed tomography (CT) scans.

Background The extent to which clinical breast cancer risk prediction models can be improved by including information on known susceptibility single nucleotide polymorphisms (SNPs) is not known. Methods Using 750 cases and 405 controls from the population-based Australian Breast Cancer Family Registry who were younger than 50 years at diagnosis and recruitment, respectively, Caucasian and not BRCA1 or BRCA2 mutation carriers, we derived absolute 5-year risks of breast cancer using the BOADICEA, BRCAPRO, BCRAT, and IBIS risk prediction models and combined these with a risk score based on 77 independent risk-associated SNPs. We used logistic regression to estimate the odds ratio per adjusted standard deviation for log-transformed age-adjusted 5-year risks. Discrimination was assessed by the area under the receiver operating characteristic curve (AUC). Calibration was assessed using the Hosmer–Lemeshow goodness-of-fit test. We also constructed reclassification tables and calculated the net reclassification improvement. Results The odds ratios for BOADICEA, BRCAPRO, BCRAT, and IBIS were 1.80, 1.75, 1.67, and 1.30, respectively. When combined with the SNP-based score, the corresponding odds ratios were 1.96, 1.89, 1.80, and 1.52. The corresponding AUCs were 0.66, 0.65, 0.64, and 0.57 for the risk prediction models, and 0.70, 0.69, 0.66, and 0.63 when combined with the SNP-based score. Conclusions By combining a 77 SNP-based score with clinical models, the AUC for predicting breast cancer before age 50 years improved by >20%. Impact Our estimates of the increased performance of clinical risk prediction models from including genetic information could be used to inform targeted screening and prevention.

Background: Mammographic density defined by the conventional pixel brightness threshold, and adjusted for age and body mass index (BMI), is a well-established risk factor for breast cancer. We asked if higher thresholds better separate women with and without breast cancer. Methods: We studied Australian women, 354 with breast cancer over-sampled for early-onset and family history, and 944 unaffected controls frequency-matched for age at mammogram. We measured mammographic dense area and percent density using the CUMULUS software at the conventional threshold, which we call Cumulus, and at two increasingly higher thresholds, which we call Altocumulus and Cirrocumulus, respectively. All measures were Box–Cox transformed and adjusted for age and BMI. We estimated the odds per adjusted standard deviation (OPERA) using logistic regression and the area under the receiver operating characteristic curve (AUC). Results: Altocumulus and Cirrocumulus were correlated with Cumulus (r ∼ 0.8 and 0.6, respectively). For dense area, the OPERA was 1.62, 1.74 and 1.73 for Cumulus, Altocumulus and Cirrocumulus, respectively (all P < 0.001). After adjusting for Altocumulus and Cirrocumulus, Cumulus was not significant (P > 0.6). The OPERAs for percent density were less but gave similar findings. The mean of the standardized adjusted Altocumulus and Cirrocumulus dense area measures was the best predictor; OPERA = 1.87 [95% confidence interval (CI): 1.64–2.14] and AUC = 0.68 (0.65–0.71). Conclusions: The areas of higher mammographically dense regions are associated with almost 30% stronger breast cancer risk gradient, explain the risk association of the conventional measure and might be more aetiologically important. This has substantial implications for clinical translation and molecular, genetic and epidemiological research.

Decision support tools for the assessment and management of breast cancer risk may improve uptake of prevention strategies. End-user input in the design of such tools is critical to increase clinical use. Before developing such a computerized tool, we examined clinicians' practice and future needs. Twelve breast surgeons, 12 primary care physicians and 5 practice nurses participated in 4 focus groups. These were recorded, coded, and analyzed to identify key themes. Participants identified difficulties assessing risk, including a lack of available tools to standardize practice. Most expressed confidence identifying women at potentially high risk, but not moderate risk. Participants felt a tool could especially reassure young women at average risk. Desirable features included:evidence-based, accessible (e.g. web-based), and displaying absolute (not relative) risks in multiple formats. The potential to create anxiety was a concern. Development of future tools should address these issues to optimize translation of knowledge into clinical practice.

It has been shown that, for women aged 50 years or older, the discriminatory accuracy of the Breast Cancer Risk Prediction Tool (BCRAT) can be modestly improved by the inclusion of information on common single nucleotide polymorphisms (SNPs) that are associated with increased breast cancer risk. We aimed to determine whether a similar improvement is seen for earlier onset disease. We used the Australian Breast Cancer Family Registry to study a population-based sample of 962 cases aged 35 to 59 years and 463 controls frequency matched for age and for whom genotyping data was available. Overall, the inclusion of data on seven SNPs improved the area under the receiver operating characteristic curve (AUC) from 0.58 (95% confidence interval [CI]=0.55–0.61) for BCRAT alone to 0.61 (95% CI=0.58–0.64) for BCRAT and SNP data combined (p<0.001). For women aged 35 to 39 years at interview, the corresponding improvement in AUC was from 0.61 (95% CI=0.56–0.66) to 0.65 (95% CI=0.60–0.70; p=0.03), while for women aged 40 to 49 years at diagnosis, the AUC improved from 0.61 (95% CI=0.55–0.66) to 0.63 (95% CI=0.57–0.69; p=0.04). Using previously used classifications of low, intermediate and high risk, 2.1% of cases and none of the controls aged 35 to 39 years, and 10.9% of cases and 4.0% of controls aged 40 to 49 years were classified into a higher risk group. Including information on seven SNPs associated with breast cancer risk improves the discriminatory accuracy of BCRAT for women aged 35 to 39 years and 40 to 49 years. Given the low absolute risk for women in these age groups, only a small proportion are reclassified into a higher category for predicted 5-year risk of breast cancer.