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A novel and fully automated mammographic texture analysis for risk prediction: results from two case-control studies

Overview of attention for article published in Breast Cancer Research, October 2017
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (59th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

twitter
3 tweeters

Citations

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25 Dimensions

Readers on

mendeley
36 Mendeley
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1 CiteULike
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Title
A novel and fully automated mammographic texture analysis for risk prediction: results from two case-control studies
Published in
Breast Cancer Research, October 2017
DOI 10.1186/s13058-017-0906-6
Pubmed ID
Authors

Chao Wang, Adam R. Brentnall, Jack Cuzick, Elaine F. Harkness, D. Gareth Evans, Susan Astley

Abstract

The percentage of mammographic dense tissue (PD) is an important risk factor for breast cancer, and there is some evidence that texture features may further improve predictive ability. However, relatively little work has assessed or validated textural feature algorithms using raw full field digital mammograms (FFDM). A case-control study nested within a screening cohort (age 46-73 years) from Manchester UK was used to develop a texture feature risk score (264 cases diagnosed at the same time as mammogram of the contralateral breast, 787 controls) using the least absolute shrinkage and selection operator (LASSO) method for 112 features, and validated in a second case-control study from the same cohort but with cases diagnosed after the index mammogram (317 cases, 931 controls). Predictive ability was assessed using deviance and matched concordance index (mC). The ability to improve risk estimation beyond percent volumetric density (Volpara) was evaluated using conditional logistic regression. The strongest features identified in the training set were "sum average" based on the grey-level co-occurrence matrix at low image resolutions (original resolution 10.628 pixels per mm; downsized by factors of 16, 32 and 64), which had a better deviance and mC than volumetric PD. In the validation study, the risk score combining the three sum average features achieved a better deviance than volumetric PD (Δχ(2) = 10.55 or 6.95 if logarithm PD) and a similar mC to volumetric PD (0.58 and 0.57, respectively). The risk score added independent information to volumetric PD (Δχ(2) = 14.38, p = 0.0008). Textural features based on digital mammograms improve risk assessment beyond volumetric percentage density. The features and risk score developed need further investigation in other settings.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 36 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 22%
Student > Bachelor 5 14%
Student > Ph. D. Student 5 14%
Other 3 8%
Student > Doctoral Student 2 6%
Other 1 3%
Unknown 12 33%
Readers by discipline Count As %
Medicine and Dentistry 8 22%
Engineering 5 14%
Computer Science 4 11%
Nursing and Health Professions 2 6%
Agricultural and Biological Sciences 1 3%
Other 3 8%
Unknown 13 36%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 27 October 2017.
All research outputs
#6,325,877
of 12,059,719 outputs
Outputs from Breast Cancer Research
#776
of 1,371 outputs
Outputs of similar age
#112,031
of 284,614 outputs
Outputs of similar age from Breast Cancer Research
#16
of 48 outputs
Altmetric has tracked 12,059,719 research outputs across all sources so far. This one is in the 47th percentile – i.e., 47% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,371 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 10.0. This one is in the 42nd percentile – i.e., 42% of its peers scored the same or lower than it.
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 284,614 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 59% of its contemporaries.
We're also able to compare this research output to 48 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 66% of its contemporaries.