NCT01955018|CompletedPhase 4
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Sponsor study identifier
Secondary identifiers
interventional
90
1 country
1
Study Start
September 2012
Date type not reportedThis date has not been verified as actual or estimated.
Primary Completion
April 2013
Actual (reported)Study Completion
May 2013
Actual (reported)Asbestos fibers were intensively used throughout the 20th century and remain prevalent in developing countries. However, asbestos exposure induces a variety of benign and malignant pleural and lung diseases. The most common asbestos-induced neoplasm is lung cancer. Moreover, thin-section computed tomography (CT) is more sensitive than a chest x-ray for detecting early asbestos-related conditions. Increased exposure to radiation underpins the consequences of cancer induction. However, reducing CT doses increases image noise from the filtered back projection (FBP) reconstruction. Strategies to reduce radiation exposure include the use of iterative reconstruction algorithms. A new algorithm called VeoTM (General Electric Healthcare, Milwaukee, MI, USA) decreases the image noise up to 70% compared with the gold standard FBP model. Moreover, Veo improves spatial resolution with excellent detection of low and high contrast objects from a CT Dose Index (CTDIvol) equal to 0.3 mGy. The objective of the present study is to compare Veo with the gold standard FBP for detecting pulmonary asbestos-related conditions among workers previously exposed to asbestos. Comparisons included radiation delivered and image quality.
Registry-reported information. Elapsed time and enrollment totals alone do not measure study progress or recruitment pace.
May 2013
Actual (reported)Click on a node to explore related trials.
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Dates and qualifiers reported by the registry. Estimated dates do not confirm that a milestone has occurred.
September 2012
Date type not reportedThis date has not been verified as actual or estimated.
April 2013
Actual (reported)May 2013
Actual (reported)September 2, 2013
Actual (reported)October 7, 2013
Actual (reported)Pleural and parenchymal abnormalities
The kappa coefficient will be used to measure agreement for categorical parameters and Pearson's correlation coefficient and Lin concordance correlation coefficient for quantitative data.
at day 1
Radiation dose
at day 1
Quality images assessment
at day 1
A new algorithm called VeoTM (General Electric Healthcare, Milwaukee, MI, USA) decreases the image noise up to 70% compared with the gold standard FBP model. Moreover, Veo improves spatial resolution with excellent detection of low and high contrast objects from a CT Dose Index (CTDIvol) equal to 0.3 mGy
The objective of the present study is to compare Veo with the gold standard FBP for detecting pulmonary asbestos-related conditions among workers previously exposed to asbestos. Comparisons included radiation delivered and image quality
The objective of the present study is to compare Veo with the gold standard FBP for detecting pulmonary asbestos-related conditions among workers previously exposed to asbestos. Comparisons included radiation delivered and image quality.
Registry-reported names are shown in full. Names without a link have not yet been matched to a Syfrah profile.
CHU Clermont-Ferrand
Clermont-Ferrand, 63003, France
Tekath M, Dutheil F, Bellini R, Roche A, Pereira B, Naughton G, Chamoux A, Michel JL. Comparison of the ultra-low-dose Veo algorithm with the gold standard filtered back projection for detecting pulmonary asbestos-related conditions: a clinical observational study. BMJ Open. 2014 May 30;4(5):e004980. doi: 10.1136/bmjopen-2014-004980.
Frederic DUTHEIL
University Hospital, Clermont-Ferrand
90 participants (actual enrollment reported by the registry).
First Submitted
September 2, 2013
First Posted
October 7, 2013
Study Start
September 2012
Date type not reportedThis date has not been verified as actual or estimated.
Contact the study team to confirm eligibility.
Primary Completion
April 2013
Actual (reported)Study Completion
May 2013
Actual (reported)Last Updated
August 7, 2026
Record last verified: 2026-08
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Source: ClinicalTrials.gov.
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