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Comparison and Fusion of Deep Learning and Radiomics Features of Ground-Glass Nodules to Predict the Invasiveness Risk of Stage-I Lung Adenocarcinomas in CT Scan

Overview of attention for article published in Frontiers in oncology, March 2020
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age and source (58th percentile)

Mentioned by

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3 X users

Citations

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53 Dimensions

Readers on

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50 Mendeley
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Title
Comparison and Fusion of Deep Learning and Radiomics Features of Ground-Glass Nodules to Predict the Invasiveness Risk of Stage-I Lung Adenocarcinomas in CT Scan
Published in
Frontiers in oncology, March 2020
DOI 10.3389/fonc.2020.00418
Pubmed ID
Authors

Xianwu Xia, Jing Gong, Wen Hao, Ting Yang, Yeqing Lin, Shengping Wang, Weijun Peng

X Demographics

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.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 50 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 14%
Student > Master 7 14%
Student > Bachelor 3 6%
Student > Postgraduate 3 6%
Student > Doctoral Student 2 4%
Other 6 12%
Unknown 22 44%
Readers by discipline Count As %
Medicine and Dentistry 9 18%
Computer Science 5 10%
Nursing and Health Professions 3 6%
Engineering 3 6%
Business, Management and Accounting 1 2%
Other 5 10%
Unknown 24 48%
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 03 May 2020.
All research outputs
#17,604,528
of 25,806,763 outputs
Outputs from Frontiers in oncology
#8,179
of 22,805 outputs
Outputs of similar age
#256,133
of 398,045 outputs
Outputs of similar age from Frontiers in oncology
#180
of 482 outputs
Altmetric has tracked 25,806,763 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 22,805 research outputs from this source. They receive a mean Attention Score of 3.1. This one has gotten more attention than average, scoring higher than 58% 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 398,045 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 482 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 58% of its contemporaries.