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Functional MRI for the prediction of treatment response in head and neck squamous cell carcinoma: potential and limitations

Overview of attention for article published in Cancer Imaging, August 2016
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Title
Functional MRI for the prediction of treatment response in head and neck squamous cell carcinoma: potential and limitations
Published in
Cancer Imaging, August 2016
DOI 10.1186/s40644-016-0080-6
Pubmed ID
Authors

Ann D. King, Harriet C. Thoeny

Abstract

Pre-treatment or early intra-treatment prediction of patients with head and neck squamous cell carcinomas (HNSCC) who are likely to have tumours that are resistant to chemoradiotherapy (CRT) would enable treatment regimens to be changed at an early time point, or allow patients at risk of residual disease to be targeted for more intensive post-treatment investigation. Research into the potential advantages of using functional-based magnetic resonance imaging (MRI) sequences before or during cancer treatments to predict treatment response has been ongoing for several years. In regard to HNSCC, the reported results from functional MRI research are promising but they have yet to be transferred to the clinical domain. This article will review the functional MRI literature in HNSCC to determine the current status of the research and try to identify areas that are close to application in clinical practice. This review will focus on diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI (DCE-MRI) and briefly include proton magnetic resonance spectroscopy ((1)H-MRS)and blood oxygen level dependent (BOLD) MRI.

X Demographics

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The data shown below were collected from the profile of 1 X user 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 86 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Switzerland 1 1%
Unknown 85 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 19%
Student > Ph. D. Student 10 12%
Student > Master 10 12%
Student > Bachelor 9 10%
Other 8 9%
Other 13 15%
Unknown 20 23%
Readers by discipline Count As %
Medicine and Dentistry 32 37%
Physics and Astronomy 7 8%
Nursing and Health Professions 4 5%
Mathematics 2 2%
Agricultural and Biological Sciences 2 2%
Other 8 9%
Unknown 31 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 09 September 2016.
All research outputs
#17,285,668
of 25,373,627 outputs
Outputs from Cancer Imaging
#319
of 674 outputs
Outputs of similar age
#232,148
of 355,119 outputs
Outputs of similar age from Cancer Imaging
#6
of 15 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 674 research outputs from this source. They receive a mean Attention Score of 2.4. This one is in the 41st percentile – i.e., 41% 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 355,119 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 15 others from the same source and published within six weeks on either side of this one. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.