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Development of an interpretable machine learning model for Ki-67 prediction in breast cancer using intratumoral and peritumoral ultrasound radiomics features

Overview of attention for article published in Frontiers in oncology, November 2023
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Mentioned by

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1 X user

Citations

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1 Dimensions

Readers on

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4 Mendeley
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Title
Development of an interpretable machine learning model for Ki-67 prediction in breast cancer using intratumoral and peritumoral ultrasound radiomics features
Published in
Frontiers in oncology, November 2023
DOI 10.3389/fonc.2023.1290313
Pubmed ID
Authors

Jing Wang, Weiwei Gao, Min Lu, Xiaohua Yao, Debin Yang

X Demographics

X Demographics

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

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 1 25%
Unknown 3 75%
Readers by discipline Count As %
Unspecified 1 25%
Unknown 3 75%
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 17 November 2023.
All research outputs
#23,208,433
of 25,864,668 outputs
Outputs from Frontiers in oncology
#16,270
of 22,819 outputs
Outputs of similar age
#308,406
of 372,404 outputs
Outputs of similar age from Frontiers in oncology
#728
of 805 outputs
Altmetric has tracked 25,864,668 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 22,819 research outputs from this source. They receive a mean Attention Score of 3.1. This one is in the 1st percentile – i.e., 1% 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 372,404 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 805 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.