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Improving cancer detection through combinations of cancer and immune biomarkers: a modelling approach

Overview of attention for article published in Journal of Translational Medicine, March 2018
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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 (80th percentile)

Mentioned by

news
1 news outlet
twitter
2 tweeters

Citations

dimensions_citation
14 Dimensions

Readers on

mendeley
28 Mendeley
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Title
Improving cancer detection through combinations of cancer and immune biomarkers: a modelling approach
Published in
Journal of Translational Medicine, March 2018
DOI 10.1186/s12967-018-1432-8
Pubmed ID
Authors

Raluca Eftimie, Esraa Hassanein

Abstract

Early cancer diagnosis is one of the most important challenges of cancer research, since in many cancers it can lead to cure for patients with early stage diseases. For epithelial ovarian cancer (which is the leading cause of death among gynaecologic malignancies) the classical detection approach is based on measurements of CA-125 biomarker. However, the poor sensitivity and specificity of this biomarker impacts the detection of early-stage cancers. Here we use a computational approach to investigate the effect of combining multiple biomarkers for ovarian cancer (e.g., CA-125 and IL-7), to improve early cancer detection. We show that this combined biomarkers approach could lead indeed to earlier cancer detection. However, the immune response (which influences the level of secreted IL-7 biomarker) plays an important role in improving and/or delaying cancer detection. Moreover, the detection level of IL-7 immune biomarker could be in a range that would not allow to distinguish between a healthy state and a cancerous state. In this case, the construction of solution diagrams in the space generated by the IL-7 and CA-125 biomarkers could allow us predict the long-term evolution of cancer biomarkers, thus allowing us to make predictions on cancer detection times. Combining cancer and immune biomarkers could improve cancer detection times, and any predictions that could be made (at least through the use of CA-125/IL-7 biomarkers) are patient specific.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 28 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 25%
Student > Master 4 14%
Student > Ph. D. Student 4 14%
Student > Bachelor 3 11%
Professor > Associate Professor 2 7%
Other 1 4%
Unknown 7 25%
Readers by discipline Count As %
Medicine and Dentistry 3 11%
Agricultural and Biological Sciences 3 11%
Engineering 3 11%
Nursing and Health Professions 2 7%
Mathematics 2 7%
Other 6 21%
Unknown 9 32%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 03 December 2021.
All research outputs
#2,522,024
of 20,406,949 outputs
Outputs from Journal of Translational Medicine
#394
of 3,561 outputs
Outputs of similar age
#56,324
of 296,504 outputs
Outputs of similar age from Journal of Translational Medicine
#1
of 1 outputs
Altmetric has tracked 20,406,949 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,561 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.8. This one has done well, scoring higher than 88% 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 296,504 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 80% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them