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Incorporation of pre‐therapy 18F‐FDG uptake data with CT texture features into a radiomics model for radiation pneumonitis diagnosis

Overview of attention for article published in Medical Physics, May 2017
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (61st percentile)
  • Good Attention Score compared to outputs of the same age and source (77th percentile)

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Title
Incorporation of pre‐therapy 18F‐FDG uptake data with CT texture features into a radiomics model for radiation pneumonitis diagnosis
Published in
Medical Physics, May 2017
DOI 10.1002/mp.12282
Pubmed ID
Authors

Gregory J. Anthony, Alexandra Cunliffe, Richard Castillo, Ngoc Pham, Thomas Guerrero, Samuel G. Armato, Hania A. Al‐Hallaq

Abstract

To determine whether the addition of standardized uptake value (SUV) from PET scans to CT lung texture features could improve a radiomics-based model of radiation pneumonitis (RP) diagnosis in patients undergoing radiotherapy. Anonymized data from 96 esophageal cancer patients (18 RP-positive cases of Grade ≥ 2) were collected including pre-therapy PET/CT scans, pre-/post-therapy diagnostic CT scans and RP status. Twenty texture features (first-order, fractal, Laws' filter and gray-level co-occurrence matrix) were calculated from diagnostic CT scans and compared in anatomically matched regions of the lung. Classifier performance (texture, SUV, or combination) was assessed by calculating the area under the receiver operating characteristic curve (AUC). For each texture feature, logistic regression classifiers consisting of the average change in texture feature value and the pre-therapy SUV standard deviation (SUVSD ) were created and compared with the texture feature as a lone classifier using ANOVA with correction for multiple comparisons (p < 0.0025). While clinical parameters (mean lung dose, smoking history, tumor location) were not significantly different among patients with and without symptomatic RP, SUV and texture parameters were significantly associated with RP status. AUC for single-texture-feature classifiers alone ranged from 0.58-0.81 and 0.53-0.71 in high-dose (≥ 30 Gy) and low-dose (< 10 Gy) regions of the lungs, respectively. AUC for SUVSD alone was 0.69 (95% confidence interval: 0.54-0.83). Adding SUVSD into a logistic regression model significantly increased the mean AUC across 11-18 texture features by 0.08, 0.06, 0.04 in the low-, medium-, and high-dose regions, respectively. Addition of SUVSD to a single texture feature improves classifier performance on average, but the improvement is smaller in magnitude when SUVSD is added to an already effective classifier using texture alone. These findings demonstrate the potential for more accurate assessment of RP using information from multiple imaging modalities. This article is protected by copyright. All rights reserved.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 67 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 67 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 24%
Researcher 15 22%
Student > Master 10 15%
Student > Doctoral Student 4 6%
Other 4 6%
Other 8 12%
Unknown 10 15%
Readers by discipline Count As %
Medicine and Dentistry 24 36%
Physics and Astronomy 9 13%
Computer Science 5 7%
Biochemistry, Genetics and Molecular Biology 2 3%
Psychology 1 1%
Other 5 7%
Unknown 21 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 15 July 2017.
All research outputs
#8,264,793
of 25,382,440 outputs
Outputs from Medical Physics
#2,041
of 7,985 outputs
Outputs of similar age
#122,144
of 327,324 outputs
Outputs of similar age from Medical Physics
#32
of 145 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one has received more attention than most of these and is in the 66th percentile.
So far Altmetric has tracked 7,985 research outputs from this source. They receive a mean Attention Score of 3.5. This one has gotten more attention than average, scoring higher than 73% 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 327,324 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.
We're also able to compare this research output to 145 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.