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High-throughput mammographic-density measurement: a tool for risk prediction of breast cancer

Overview of attention for article published in Breast Cancer Research, July 2012
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (71st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

Mentioned by

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3 X users
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1 patent

Citations

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96 Dimensions

Readers on

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91 Mendeley
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1 CiteULike
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Title
High-throughput mammographic-density measurement: a tool for risk prediction of breast cancer
Published in
Breast Cancer Research, July 2012
DOI 10.1186/bcr3238
Pubmed ID
Authors

Jingmei Li, Laszlo Szekely, Louise Eriksson, Boel Heddson, Ann Sundbom, Kamila Czene, Per Hall, Keith Humphreys

Abstract

ABSTRACT: INTRODUCTION: Mammographic density (MD) is a strong, independent risk factor for breast cancer, but measuring MD is time consuming and reader dependent. Objective MD measurement in a high-throughput fashion would enable its wider use as a biomarker for breast cancer. We use a public domain image-processing software for the fully automated analysis of MD and penalized regression to construct a measure that mimics a well-established semiautomated measure (Cumulus). We also describe measures that incorporate additional features of mammographic images for improving the risk associations of MD and breast cancer risk. METHODS: We randomly partitioned our dataset into a training set for model building (733 cases, 748 controls) and a test set for model assessment (765 cases, 747 controls). The Pearson product-moment correlation coefficient (r) was used to compare the MD measurements by Cumulus and our automated measure, which mimics Cumulus. The likelihood ratio test was used to validate the performance of logistic regression models for breast cancer risk, which included our measure capturing additional information in mammographic images. RESULTS: We observed a high correlation between the Cumulus measure and our measure mimicking Cumulus (r = 0.884; 95% CI, 0.872 to 0.894) in an external test set. Adding a variable, which includes extra information to percentage density, significantly improved the fit of the logistic regression model of breast cancer risk (P = 0.0002). CONCLUSIONS: Our results demonstrate the potential to facilitate the integration of mammographic density measurements into large-scale research studies and subsequently into clinical practice.

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X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 91 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 1%
Germany 1 1%
Unknown 89 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 15%
Researcher 12 13%
Student > Bachelor 10 11%
Student > Master 9 10%
Other 6 7%
Other 19 21%
Unknown 21 23%
Readers by discipline Count As %
Medicine and Dentistry 29 32%
Computer Science 11 12%
Nursing and Health Professions 5 5%
Engineering 4 4%
Agricultural and Biological Sciences 3 3%
Other 12 13%
Unknown 27 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 01 August 2019.
All research outputs
#7,204,326
of 25,371,288 outputs
Outputs from Breast Cancer Research
#824
of 2,052 outputs
Outputs of similar age
#49,898
of 179,588 outputs
Outputs of similar age from Breast Cancer Research
#13
of 37 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 2,052 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.2. This one has gotten more attention than average, scoring higher than 59% 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 179,588 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 37 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 62% of its contemporaries.