↓ Skip to main content

Invasive ductal breast cancer molecular subtype prediction by MRI radiomic and clinical features based on machine learning

Overview of attention for article published in Frontiers in oncology, September 2022
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Above-average Attention Score compared to outputs of the same age and source (53rd percentile)

Mentioned by

twitter
4 X users

Citations

dimensions_citation
10 Dimensions

Readers on

mendeley
19 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
Invasive ductal breast cancer molecular subtype prediction by MRI radiomic and clinical features based on machine learning
Published in
Frontiers in oncology, September 2022
DOI 10.3389/fonc.2022.964605
Pubmed ID
Authors

Weiyong Sheng, Shouli Xia, Yaru Wang, Lizhao Yan, Songqing Ke, Evelyn Mellisa, Fen Gong, Yun Zheng, Tiansheng Tang

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 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 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Other 1 5%
Lecturer 1 5%
Student > Bachelor 1 5%
Student > Ph. D. Student 1 5%
Student > Master 1 5%
Other 2 11%
Unknown 12 63%
Readers by discipline Count As %
Nursing and Health Professions 2 11%
Pharmacology, Toxicology and Pharmaceutical Science 2 11%
Arts and Humanities 1 5%
Computer Science 1 5%
Medicine and Dentistry 1 5%
Other 0 0%
Unknown 12 63%
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 29 September 2022.
All research outputs
#17,866,085
of 26,170,906 outputs
Outputs from Frontiers in oncology
#8,257
of 22,913 outputs
Outputs of similar age
#257,555
of 437,873 outputs
Outputs of similar age from Frontiers in oncology
#673
of 1,814 outputs
Altmetric has tracked 26,170,906 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 22,913 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 437,873 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,814 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 53% of its contemporaries.