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Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas

Overview of attention for article published in American Journal of Neuroradiology, May 2018
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#10 of 3,264)
  • High Attention Score compared to outputs of the same age (95th percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

news
4 news outlets
twitter
50 tweeters
facebook
1 Facebook page
video
1 video uploader

Citations

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16 Dimensions

Readers on

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46 Mendeley
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Title
Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas
Published in
American Journal of Neuroradiology, May 2018
DOI 10.3174/ajnr.a5667
Pubmed ID
Authors

P. Chang, J. Grinband, B.D. Weinberg, M. Bardis, M. Khy, G. Cadena, M.-Y. Su, S. Cha, C.G. Filippi, D. Bota, P. Baldi, L.M. Poisson, R. Jain, D. Chow

Abstract

The World Health Organization has recently placed new emphasis on the integration of genetic information for gliomas. While tissue sampling remains the criterion standard, noninvasive imaging techniques may provide complimentary insight into clinically relevant genetic mutations. Our aim was to train a convolutional neural network to independently predict underlying molecular genetic mutation status in gliomas with high accuracy and identify the most predictive imaging features for each mutation. MR imaging data and molecular information were retrospectively obtained from The Cancer Imaging Archives for 259 patients with either low- or high-grade gliomas. A convolutional neural network was trained to classify isocitrate dehydrogenase 1 (IDH1) mutation status, 1p/19q codeletion, and O6-methylguanine-DNA methyltransferase (MGMT) promotor methylation status. Principal component analysis of the final convolutional neural network layer was used to extract the key imaging features critical for successful classification. Classification had high accuracy: IDH1 mutation status, 94%; 1p/19q codeletion, 92%; and MGMT promotor methylation status, 83%. Each genetic category was also associated with distinctive imaging features such as definition of tumor margins, T1 and FLAIR suppression, extent of edema, extent of necrosis, and textural features. Our results indicate that for The Cancer Imaging Archives dataset, machine-learning approaches allow classification of individual genetic mutations of both low- and high-grade gliomas. We show that relevant MR imaging features acquired from an added dimensionality-reduction technique demonstrate that neural networks are capable of learning key imaging components without prior feature selection or human-directed training.

Twitter Demographics

The data shown below were collected from the profiles of 50 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 46 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 2 4%
Researcher 1 2%
Student > Master 1 2%
Unknown 42 91%
Readers by discipline Count As %
Unspecified 2 4%
Computer Science 1 2%
Medicine and Dentistry 1 2%
Unknown 42 91%

Attention Score in Context

This research output has an Altmetric Attention Score of 66. 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 May 2019.
All research outputs
#237,969
of 12,931,332 outputs
Outputs from American Journal of Neuroradiology
#10
of 3,264 outputs
Outputs of similar age
#11,076
of 269,813 outputs
Outputs of similar age from American Journal of Neuroradiology
#2
of 95 outputs
Altmetric has tracked 12,931,332 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,264 research outputs from this source. They receive a mean Attention Score of 4.4. This one has done particularly well, scoring higher than 99% 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 269,813 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 95 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.