↓ Skip to main content

Diagnostic Performance of 2D and 3D T2WI-Based Radiomics Features With Machine Learning Algorithms to Distinguish Solid Solitary Pulmonary Lesion

Overview of attention for article published in Frontiers in oncology, November 2021
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age and source (55th percentile)

Mentioned by

twitter
3 X users

Citations

dimensions_citation
17 Dimensions

Readers on

mendeley
7 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Diagnostic Performance of 2D and 3D T2WI-Based Radiomics Features With Machine Learning Algorithms to Distinguish Solid Solitary Pulmonary Lesion
Published in
Frontiers in oncology, November 2021
DOI 10.3389/fonc.2021.683587
Pubmed ID
Authors

Qi Wan, Jiaxuan Zhou, Xiaoying Xia, Jianfeng Hu, Peng Wang, Yu Peng, Tianjing Zhang, Jianqing Sun, Yang Song, Guang Yang, Xinchun Li

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 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 14%
Student > Bachelor 1 14%
Student > Doctoral Student 1 14%
Student > Master 1 14%
Unknown 3 43%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 1 14%
Unknown 6 86%
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 06 December 2021.
All research outputs
#20,580,732
of 26,163,973 outputs
Outputs from Frontiers in oncology
#9,608
of 22,913 outputs
Outputs of similar age
#379,752
of 528,343 outputs
Outputs of similar age from Frontiers in oncology
#509
of 1,352 outputs
Altmetric has tracked 26,163,973 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
So far Altmetric has tracked 22,913 research outputs from this source. They receive a mean Attention Score of 3.1. This one is in the 49th percentile – i.e., 49% 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 528,343 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,352 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 55% of its contemporaries.