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2D and 3D texture analysis to differentiate brain metastases on MR images: proceed with caution

Overview of attention for article published in Magnetic Resonance Materials in Physics, Biology and Medicine, September 2017
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Title
2D and 3D texture analysis to differentiate brain metastases on MR images: proceed with caution
Published in
Magnetic Resonance Materials in Physics, Biology and Medicine, September 2017
DOI 10.1007/s10334-017-0653-9
Pubmed ID
Authors

Monika Béresová, Andrés Larroza, Estanislao Arana, József Varga, László Balkay, David Moratal

Abstract

To find structural differences between brain metastases of lung and breast cancer, computing their heterogeneity parameters by means of both 2D and 3D texture analysis (TA). Patients with 58 brain metastases from breast (26) and lung cancer (32) were examined by MR imaging. Brain lesions were manually delineated by 2D ROIs on the slices of contrast-enhanced T1-weighted (CET1) images, and local binary patterns (LBP) maps were created from each region. Histogram-based (minimum, maximum, mean, standard deviation, and variance), and co-occurrence matrix-based (contrast, correlation, energy, entropy, and homogeneity) 2D, weighted average of the 2D slices, and true 3D TA were obtained on the CET1 images and LBP maps. For LBP maps and 2D TA contrast, correlation, energy, and homogeneity were identified as statistically different heterogeneity parameters (SDHPs) between lung and breast metastasis. The weighted 3D TA identified entropy as an additional SDHP. Only two texture indexes (TI) were significantly different with true 3D TA: entropy and energy. All these TIs discriminated between the two tumor types significantly by ROC analysis. For the CET1 images there was no SDHP at all by 3D TA. Our results indicate that the used textural analysis methods may help with discriminating between brain metastases of different primary tumors.

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Geographical breakdown

Country Count As %
Unknown 41 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 17%
Researcher 7 17%
Student > Master 6 15%
Professor > Associate Professor 5 12%
Student > Postgraduate 3 7%
Other 8 20%
Unknown 5 12%
Readers by discipline Count As %
Medicine and Dentistry 17 41%
Computer Science 5 12%
Engineering 4 10%
Physics and Astronomy 4 10%
Nursing and Health Professions 2 5%
Other 4 10%
Unknown 5 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 20 April 2018.
All research outputs
#14,906,966
of 23,849,058 outputs
Outputs from Magnetic Resonance Materials in Physics, Biology and Medicine
#323
of 492 outputs
Outputs of similar age
#179,586
of 320,433 outputs
Outputs of similar age from Magnetic Resonance Materials in Physics, Biology and Medicine
#5
of 9 outputs
Altmetric has tracked 23,849,058 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 492 research outputs from this source. They receive a mean Attention Score of 3.2. This one is in the 32nd percentile – i.e., 32% 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 320,433 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.