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Automatic delineation of functional lung volumes with 68Ga-ventilation/perfusion PET/CT

Overview of attention for article published in EJNMMI Research, October 2017
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2 tweeters

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4 Dimensions

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Title
Automatic delineation of functional lung volumes with 68Ga-ventilation/perfusion PET/CT
Published in
EJNMMI Research, October 2017
DOI 10.1186/s13550-017-0332-x
Pubmed ID
Authors

Pierre-Yves Le Roux, Shankar Siva, Jason Callahan, Yannis Claudic, David Bourhis, Daniel P. Steinfort, Rodney J. Hicks, Michael S. Hofman

Abstract

Functional volumes computed from (68)Ga-ventilation/perfusion (V/Q) PET/CT, which we have shown to correlate with pulmonary function test parameters (PFTs), have potential diagnostic utility in a variety of clinical applications, including radiotherapy planning. An automatic segmentation method would facilitate delineation of such volumes. The aim of this study was to develop an automated threshold-based approach to delineate functional volumes that best correlates with manual delineation. Thirty lung cancer patients undergoing both V/Q PET/CT and PFTs were analyzed. Images were acquired following inhalation of Galligas and, subsequently, intravenous administration of (68)Ga-macroaggreted-albumin (MAA). Using visually defined manual contours as the reference standard, various cutoff values, expressed as a percentage of the maximal pixel value, were applied. The average volume difference and Dice similarity coefficient (DSC) were calculated, measuring the similarity of the automatic segmentation and the reference standard. Pearson's correlation was also calculated to compare automated volumes with manual volumes, and automated volumes optimized to PFT indices. For ventilation volumes, mean volume difference was lowest (- 0.4%) using a 15%max threshold with Pearson's coefficient of 0.71. Applying this cutoff, median DSC was 0.93 (0.87-0.95). Nevertheless, limits of agreement in volume differences were large (- 31.0 and 30.2%) with differences ranging from - 40.4 to + 33.0%. For perfusion volumes, mean volume difference was lowest and Pearson's coefficient was highest using a 15%max threshold (3.3% and 0.81, respectively). Applying this cutoff, median DSC was 0.93 (0.88-0.93). Nevertheless, limits of agreement were again large (- 21.1 and 27.8%) with volume differences ranging from - 18.6 to + 35.5%. Using the 15%max threshold, moderate correlation was demonstrated with FEV1/FVC (r = 0.48 and r = 0.46 for ventilation and perfusion images, respectively). No correlation was found between other PFT indices. To automatically delineate functional volumes with (68)Ga-V/Q PET/CT, the most appropriate cutoff was 15%max for both ventilation and perfusion images. However, using this unique threshold systematically provided unacceptable variability compared to the reference volume and relatively poor correlation with PFT parameters. Accordingly, a visually adapted semi-automatic method is favored, enabling rapid and quantitative delineation of lung functional volumes with (68)Ga-V/Q PET/CT.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 5 33%
Student > Postgraduate 2 13%
Other 2 13%
Student > Ph. D. Student 2 13%
Librarian 1 7%
Other 3 20%
Readers by discipline Count As %
Medicine and Dentistry 10 67%
Nursing and Health Professions 2 13%
Agricultural and Biological Sciences 1 7%
Arts and Humanities 1 7%
Unknown 1 7%

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 12 October 2017.
All research outputs
#7,393,208
of 11,918,812 outputs
Outputs from EJNMMI Research
#80
of 227 outputs
Outputs of similar age
#154,085
of 274,039 outputs
Outputs of similar age from EJNMMI Research
#2
of 4 outputs
Altmetric has tracked 11,918,812 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 227 research outputs from this source. They receive a mean Attention Score of 1.8. This one has gotten more attention than average, scoring higher than 62% 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 274,039 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.