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Breast Cancer Risk Prediction Using Clinical Models and 77 Independent Risk-Associated SNPs for Women Aged Under 50 Years: Australian Breast Cancer Family Registry

Overview of attention for article published in Cancer Epidemiology, Biomarkers & Prevention, December 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (94th percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

Mentioned by

news
3 news outlets
twitter
5 tweeters
patent
3 patents

Citations

dimensions_citation
67 Dimensions

Readers on

mendeley
57 Mendeley
citeulike
1 CiteULike
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Title
Breast Cancer Risk Prediction Using Clinical Models and 77 Independent Risk-Associated SNPs for Women Aged Under 50 Years: Australian Breast Cancer Family Registry
Published in
Cancer Epidemiology, Biomarkers & Prevention, December 2015
DOI 10.1158/1055-9965.epi-15-0838
Pubmed ID
Authors

Gillian S. Dite, Robert J. MacInnis, Adrian Bickerstaffe, James G. Dowty, Richard Allman, Carmel Apicella, Roger L. Milne, Helen Tsimiklis, Kelly-Anne Phillips, Graham G. Giles, Mary Beth Terry, Melissa C. Southey, John L. Hopper

Abstract

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. 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. 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. By combining a 77 SNP-based score with clinical models, the AUC for predicting breast cancer before age 50 years improved by >20%. Our estimates of the increased performance of clinical risk prediction models from including genetic information could be used to inform targeted screening and prevention.

Twitter Demographics

The data shown below were collected from the profiles of 5 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 1 2%
Unknown 56 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 26%
Researcher 10 18%
Student > Master 6 11%
Other 6 11%
Professor 4 7%
Other 5 9%
Unknown 11 19%
Readers by discipline Count As %
Medicine and Dentistry 14 25%
Agricultural and Biological Sciences 10 18%
Biochemistry, Genetics and Molecular Biology 9 16%
Mathematics 3 5%
Computer Science 2 4%
Other 6 11%
Unknown 13 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 27. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 February 2021.
All research outputs
#878,101
of 17,360,236 outputs
Outputs from Cancer Epidemiology, Biomarkers & Prevention
#338
of 3,936 outputs
Outputs of similar age
#21,987
of 372,635 outputs
Outputs of similar age from Cancer Epidemiology, Biomarkers & Prevention
#11
of 67 outputs
Altmetric has tracked 17,360,236 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,936 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.1. This one has done particularly well, scoring higher than 91% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 372,635 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 94% of its contemporaries.
We're also able to compare this research output to 67 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.