Breast cancer screening in the era of density notification legislation: summary of 2014 Massachusetts experience and suggestion of an evidence-based management algorithm by multi-disciplinary expert panel

Purpose: Stemming from breast density notification legislation in Massachusetts effective 2015, we sought to develop a collaborative evidence-based approach to density notification that could be used by practitioners across the state. Our goal was to develop an evidence-based consensus management algorithm to help patients and health care providers follow best practices to implement a coordinated, evidence-based, cost-effective, sustainable practice and to standardize care in recommendations for supplemental screening. Methods: We formed the Massachusetts Breast Risk Education and Assessment Task Force (MA-BREAST) a multi-institutional, multi-disciplinary panel of expert radiologists, surgeons, primary care physicians, and oncologists to develop a collaborative approach to density notification legislation. Using evidence-based data from the Institute for Clinical and Economic Review (ICER), the Cochrane review, National Comprehensive Cancer Network (NCCN) guidelines, American Cancer Society (ACS) recommendations, and American College of Radiology (ACR) appropriateness criteria, the group collaboratively developed an evidence-based best-practices algorithm. Results: The expert consensus algorithm uses breast density as one element in the risk stratification to determine the need for supplemental screening. Women with dense breasts and otherwise low risk (20% lifetime) should consider supplemental screening MRI in addition to routine mammography regardless of breast density. Conclusion: We report the development of the multi-disciplinary collaborative approach to density notification. We propose a risk stratification algorithm to assess personal level of risk to determine the need for supplemental screening for an individual woman

Costochondral Ossification and Aging in Five Populations

Age changes in extent of costochondral ossification of the first rib and of the lower ribs were evaluated separately from chest roentgenograms in five populations: European Americans, Lebanese, Solomon Islanders (the Lau and the Baegu), and a special veterans group. Increase in the ossification was closely associated with age in all groups. The shapes of the age curves were similar in all populations within each measure and within sexes. However, the Solomon Islanders showed less ossification than the Caucasians, and the Baegu showed less ossification than the Lau. These findings may be explained by the dietary differences in the populations. With respect to sex differences, for the first rib, males showed greater ossification than females regardless of age in each of the groups. For the lower ribs, males generally showed most age changes before age 45 and females after age 45. The sex differences may be related to endocrine factors. Ossification in the first rib cartilage was related to chest circumference in all three male groups investigated (the veterans, the Lau and the Baegu) but not in the females (the Lau and the Baegu). Ossification in the lower rib cartilages was related to chest expansion in the male veterans, the only group where such data were available. These latter findings supported the hypothesis that biomechanical factors influence costochondral ossification

The impact of patient age on breast cancer risk prediction models

BackgroundThe impact of age on breast cancer risk model calculations at the population level has not been well documented

Combining Breast Cancer Risk Prediction Models

Accurate risk stratification is key to reducing cancer morbidity through targeted screening and preventative interventions. Multiple breast cancer risk prediction models are used in clinical practice, and often provide a range of different predictions for the same patient. Integrating information from different models may improve the accuracy of predictions, which would be valuable for both clinicians and patients. BRCAPRO is a widely used model that predicts breast cancer risk based on detailed family history information. A major limitation of this model is that it does not consider non-genetic risk factors. To address this limitation, we expand BRCAPRO by combining it with another popular existing model, BCRAT (i.e., Gail), which uses a largely complementary set of risk factors, most of them non-genetic. We consider two approaches for combining BRCAPRO and BCRAT: (1) modifying the penetrance (age-specific probability of developing cancer given genotype) functions in BRCAPRO using relative hazard estimates from BCRAT, and (2) training an ensemble model that takes BRCAPRO and BCRAT predictions as input. Using both simulated data and data from Newton-Wellesley Hospital and the Cancer Genetics Network, we show that the combination models are able to achieve performance gains over both BRCAPRO and BCRAT. In the Cancer Genetics Network cohort, we show that the proposed BRCAPRO + BCRAT penetrance modification model performs comparably to IBIS, an existing model that combines detailed family history with non-genetic risk factors

Combining Breast Cancer Risk Prediction Models

Accurate risk stratification is key to reducing cancer morbidity through targeted screening and preventative interventions. Multiple breast cancer risk prediction models are used in clinical practice, and often provide a range of different predictions for the same patient. Integrating information from different models may improve the accuracy of predictions, which would be valuable for both clinicians and patients. BRCAPRO is a widely used model that predicts breast cancer risk based on detailed family history information. A major limitation of this model is that it does not consider non-genetic risk factors. To address this limitation, we expand BRCAPRO by combining it with another popular existing model, BCRAT (i.e., Gail), which uses a largely complementary set of risk factors, most of them non-genetic. We consider two approaches for combining BRCAPRO and BCRAT: (1) modifying the penetrance (age-specific probability of developing cancer given genotype) functions in BRCAPRO using relative hazard estimates from BCRAT, and (2) training an ensemble model that takes BRCAPRO and BCRAT predictions as input. Using both simulated data and data from Newton-Wellesley Hospital and the Cancer Genetics Network, we show that the combination models are able to achieve performance gains over both BRCAPRO and BCRAT. In the Cancer Genetics Network cohort, we show that the proposed BRCAPRO + BCRAT penetrance modification model performs comparably to IBIS, an existing model that combines detailed family history with non-genetic risk factors

Performance of Breast Cancer Risk-Assessment Models in a Large Mammography Cohort

Background: Several breast cancer risk-assessment models exist. Few studies have evaluated predictive accuracy of multiple models in large screening populations