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Micro-anatomical quantitative optical imaging: toward automated assessment of breast tissues

Overview of attention for article published in Breast Cancer Research, August 2015
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#47 of 1,900)
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

news
7 news outlets
blogs
1 blog
twitter
11 tweeters

Citations

dimensions_citation
12 Dimensions

Readers on

mendeley
32 Mendeley
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Title
Micro-anatomical quantitative optical imaging: toward automated assessment of breast tissues
Published in
Breast Cancer Research, August 2015
DOI 10.1186/s13058-015-0617-9
Pubmed ID
Authors

Jessica L. Dobbs, Jenna L. Mueller, Savitri Krishnamurthy, Dongsuk Shin, Henry Kuerer, Wei Yang, Nirmala Ramanujam, Rebecca Richards-Kortum

Abstract

Pathologists currently diagnose breast lesions through histologic assessment, which requires fixation and tissue preparation. The diagnostic criteria used to classify breast lesions are qualitative and subjective, and inter-observer discordance has been shown to be a significant challenge in the diagnosis of selected breast lesions, particularly for borderline proliferative lesions. Thus, there is an opportunity to develop tools to rapidly visualize and quantitatively interpret breast tissue morphology for a variety of clinical applications. Toward this end, we acquired images of freshly excised breast tissue specimens from a total of 34 patients using confocal fluorescence microscopy and proflavine as a topical stain. We developed computerized algorithms to segment and quantify nuclear and ductal parameters that characterize breast architectural features. A total of 33 parameters were evaluated and used as input to develop a decision tree model to classify benign and malignant breast tissue. Benign features were classified in tissue specimens acquired from 30 patients and malignant features were classified in specimens from 22 patients. The decision tree model that achieved the highest accuracy for distinguishing between benign and malignant breast features used the following parameters: standard deviation of inter-nuclear distance and number of duct lumens. The model achieved 81 % sensitivity and 93 % specificity, corresponding to an area under the curve of 0.93 and an overall accuracy of 90 %. The model classified IDC and DCIS with 92 % and 96 % accuracy, respectively. The cross-validated model achieved 75 % sensitivity and 93 % specificity and an overall accuracy of 88 %. These results suggest that proflavine staining and confocal fluorescence microscopy combined with image analysis strategies to segment morphological features could potentially be used to quantitatively diagnose freshly obtained breast tissue at the point of care without the need for tissue preparation.

Twitter Demographics

The data shown below were collected from the profiles of 11 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 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Poland 1 3%
Canada 1 3%
Unknown 30 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 28%
Student > Ph. D. Student 6 19%
Other 3 9%
Student > Master 3 9%
Professor > Associate Professor 2 6%
Other 3 9%
Unknown 6 19%
Readers by discipline Count As %
Medicine and Dentistry 9 28%
Engineering 4 13%
Physics and Astronomy 4 13%
Agricultural and Biological Sciences 2 6%
Nursing and Health Professions 1 3%
Other 4 13%
Unknown 8 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 67. 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 22 April 2016.
All research outputs
#536,108
of 22,824,164 outputs
Outputs from Breast Cancer Research
#47
of 1,900 outputs
Outputs of similar age
#7,421
of 265,957 outputs
Outputs of similar age from Breast Cancer Research
#1
of 33 outputs
Altmetric has tracked 22,824,164 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,900 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.9. This one has done particularly well, scoring higher than 97% 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 265,957 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 97% of its contemporaries.
We're also able to compare this research output to 33 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 96% of its contemporaries.