Reproducibility of compartmental modelling of 18F-FDG PET/CT to evaluate lung inflammation

Abstract Introduction Compartmental modelling is an established method of quantifying 18F-FDG uptake; however, only recently has it been applied to evaluate pulmonary inflammation. Implementation of compartmental models remains challenging in the lung, partly due to the low signal-to-noise ratio com...

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Main Authors: Laurence D. Vass, Sarah Lee, Frederick J. Wilson, Marie Fisk, Joseph Cheriyan, Ian Wilkinson
Format: Article
Language:English
Published: SpringerOpen 2019-12-01
Series:EJNMMI Physics
Subjects:
Online Access:https://doi.org/10.1186/s40658-019-0265-8
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author Laurence D. Vass
Sarah Lee
Frederick J. Wilson
Marie Fisk
Joseph Cheriyan
Ian Wilkinson
author_facet Laurence D. Vass
Sarah Lee
Frederick J. Wilson
Marie Fisk
Joseph Cheriyan
Ian Wilkinson
author_sort Laurence D. Vass
collection DOAJ
description Abstract Introduction Compartmental modelling is an established method of quantifying 18F-FDG uptake; however, only recently has it been applied to evaluate pulmonary inflammation. Implementation of compartmental models remains challenging in the lung, partly due to the low signal-to-noise ratio compared to other organs and the lack of standardisation. Good reproducibility is a key requirement of an imaging biomarker which has yet to be demonstrated in pulmonary compartmental models of 18F-FDG; in this paper, we address this unmet need. Methods Retrospective subject data were obtained from the EVOLVE observational study: Ten COPD patients (age =66±9; 8M/2F), 10 α 1ATD patients (age =63±8; 7M/3F) and 10 healthy volunteers (age =68±8; 9M/1F) never smokers. PET and CT images were co-registered, and whole lung regions were extracted from CT using an automated algorithm; the descending aorta was defined using a manually drawn region. Subsequent stages of the compartmental analysis were performed by two independent operators using (i) a MIAKATTM based pipeline and (ii) an in-house developed pipeline. We evaluated the metabolic rate constant of 18F-FDG (K i m ) and the fractional blood volume (V b ); Bland-Altman plots were used to compare the results. Further, we adjusted the in-house pipeline to identify the salient features in the analysis which may help improve the standardisation of this technique in the lung. Results The initial agreement on a subject level was poor: Bland-Altman coefficients of reproducibility for K i m and V b were 0.0031 and 0.047 respectively. However, the effect size between the groups (i.e. COPD, α 1ATD and healthy subjects) was similar using either pipeline. We identified the key drivers of this difference using an incremental approach: ROI methodology, modelling of the IDIF and time delay estimation. Adjustment of these factors led to improved Bland-Altman coefficients of reproducibility of 0.0015 and 0.027 for K i m and V b respectively. Conclusions Despite similar methodology, differences in implementation can lead to disparate results in the outcome parameters. When reporting the outcomes of lung compartmental modelling, we recommend the inclusion of the details of ROI methodology, input function fitting and time delay estimation to improve reproducibility.
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spelling doaj.art-9948d2b68ec54e2b86b74d4eaa3f828a2022-12-21T23:30:23ZengSpringerOpenEJNMMI Physics2197-73642019-12-016111410.1186/s40658-019-0265-8Reproducibility of compartmental modelling of 18F-FDG PET/CT to evaluate lung inflammationLaurence D. Vass0Sarah Lee1Frederick J. Wilson2Marie Fisk3Joseph Cheriyan4Ian Wilkinson5Experimental Medicine and Immunotherapeutics, Department of MedicineAmallis Consulting LTDGSK R &DExperimental Medicine and Immunotherapeutics, Department of MedicineExperimental Medicine and Immunotherapeutics, Department of MedicineExperimental Medicine and Immunotherapeutics, Department of MedicineAbstract Introduction Compartmental modelling is an established method of quantifying 18F-FDG uptake; however, only recently has it been applied to evaluate pulmonary inflammation. Implementation of compartmental models remains challenging in the lung, partly due to the low signal-to-noise ratio compared to other organs and the lack of standardisation. Good reproducibility is a key requirement of an imaging biomarker which has yet to be demonstrated in pulmonary compartmental models of 18F-FDG; in this paper, we address this unmet need. Methods Retrospective subject data were obtained from the EVOLVE observational study: Ten COPD patients (age =66±9; 8M/2F), 10 α 1ATD patients (age =63±8; 7M/3F) and 10 healthy volunteers (age =68±8; 9M/1F) never smokers. PET and CT images were co-registered, and whole lung regions were extracted from CT using an automated algorithm; the descending aorta was defined using a manually drawn region. Subsequent stages of the compartmental analysis were performed by two independent operators using (i) a MIAKATTM based pipeline and (ii) an in-house developed pipeline. We evaluated the metabolic rate constant of 18F-FDG (K i m ) and the fractional blood volume (V b ); Bland-Altman plots were used to compare the results. Further, we adjusted the in-house pipeline to identify the salient features in the analysis which may help improve the standardisation of this technique in the lung. Results The initial agreement on a subject level was poor: Bland-Altman coefficients of reproducibility for K i m and V b were 0.0031 and 0.047 respectively. However, the effect size between the groups (i.e. COPD, α 1ATD and healthy subjects) was similar using either pipeline. We identified the key drivers of this difference using an incremental approach: ROI methodology, modelling of the IDIF and time delay estimation. Adjustment of these factors led to improved Bland-Altman coefficients of reproducibility of 0.0015 and 0.027 for K i m and V b respectively. Conclusions Despite similar methodology, differences in implementation can lead to disparate results in the outcome parameters. When reporting the outcomes of lung compartmental modelling, we recommend the inclusion of the details of ROI methodology, input function fitting and time delay estimation to improve reproducibility.https://doi.org/10.1186/s40658-019-0265-8Positron emission tomography computed tomographyKinetic modellingLung inflammationFluorodeoxyglucose F18Reproducibility of resultsPulmonary disease
spellingShingle Laurence D. Vass
Sarah Lee
Frederick J. Wilson
Marie Fisk
Joseph Cheriyan
Ian Wilkinson
Reproducibility of compartmental modelling of 18F-FDG PET/CT to evaluate lung inflammation
EJNMMI Physics
Positron emission tomography computed tomography
Kinetic modelling
Lung inflammation
Fluorodeoxyglucose F18
Reproducibility of results
Pulmonary disease
title Reproducibility of compartmental modelling of 18F-FDG PET/CT to evaluate lung inflammation
title_full Reproducibility of compartmental modelling of 18F-FDG PET/CT to evaluate lung inflammation
title_fullStr Reproducibility of compartmental modelling of 18F-FDG PET/CT to evaluate lung inflammation
title_full_unstemmed Reproducibility of compartmental modelling of 18F-FDG PET/CT to evaluate lung inflammation
title_short Reproducibility of compartmental modelling of 18F-FDG PET/CT to evaluate lung inflammation
title_sort reproducibility of compartmental modelling of 18f fdg pet ct to evaluate lung inflammation
topic Positron emission tomography computed tomography
Kinetic modelling
Lung inflammation
Fluorodeoxyglucose F18
Reproducibility of results
Pulmonary disease
url https://doi.org/10.1186/s40658-019-0265-8
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