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18F-FDG uptake in the stomach on screening PET/CT: value for predicting Helicobacter pylori infection and chronic atrophic gastritis

Overview of attention for article published in BMC Medical Imaging, October 2016
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Title
18F-FDG uptake in the stomach on screening PET/CT: value for predicting Helicobacter pylori infection and chronic atrophic gastritis
Published in
BMC Medical Imaging, October 2016
DOI 10.1186/s12880-016-0161-9
Pubmed ID
Authors

Shigeki Kobayashi, Mayumi Ogura, Naohisa Suzawa, Noriyuki Horiki, Masaki Katsurahara, Toru Ogura, Hajime Sakuma

Abstract

The aim of this study was to determine the value of (18)F-FDG uptake on screening PET/CT images for the prediction of Helicobacter pylori (H. pylori) infection and chronic atrophic gastritis. Among subjects who underwent (18)F-FDG PET/CT for cancer screening from April 2005 to November 2015, PET/CT images were analyzed in 88 subjects who had gastrointestinal fiberscopy within 6 months. The volumes of interest (VOIs) were placed in the fornix, corpus and antrum of the stomach to determine maximal standardized uptake value (SUVmax) and mean SUV (SUVmean). Receiver operating characteristic curve (ROC) analysis was performed to determine the diagnostic performance of SUV indicators in predicting H. pylori infection and chronic atrophic gastritis. SUV indicators of the stomach were significantly higher in subjects with H. pylori infection than those without (from P < 0.001 to P < 0.05). ROC analysis revealed that SUVmean had the highest performance in predicting H. pylori infection (AUC 0.807) and chronic atrophic gastritis (AUC 0.784). SUVmean exhibited the sensitivity of 86.5 % and the specificity of 70.6 % in predicting H. pylori infection, and the sensitivity of 75.0 % and 78.6 % in predicting chronic atrophic gastritis. Assessment of (18)F-FDG uptake in the stomach reflecting active inflammation is useful in predicting patients with H. pylori infection and subsequent chronic atrophic gastritis which is closely associated with the risk of gastric neoplasms.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 3 23%
Student > Bachelor 2 15%
Student > Master 2 15%
Lecturer 1 8%
Lecturer > Senior Lecturer 1 8%
Other 1 8%
Unknown 3 23%
Readers by discipline Count As %
Unspecified 3 23%
Medicine and Dentistry 3 23%
Nursing and Health Professions 1 8%
Pharmacology, Toxicology and Pharmaceutical Science 1 8%
Immunology and Microbiology 1 8%
Other 1 8%
Unknown 3 23%

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 19 October 2016.
All research outputs
#7,378,315
of 8,539,982 outputs
Outputs from BMC Medical Imaging
#194
of 225 outputs
Outputs of similar age
#203,632
of 249,349 outputs
Outputs of similar age from BMC Medical Imaging
#6
of 7 outputs
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So far Altmetric has tracked 225 research outputs from this source. They receive a mean Attention Score of 2.1. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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