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Computer-Aided Classification of Visual Ventilation Patterns in Patients with Chronic Obstructive Pulmonary Disease at Two-Phase Xenon-Enhanced CT

OBJECTIVE: To evaluate the technical feasibility, performance, and interobserver agreement of a computer-aided classification (CAC) system for regional ventilation at two-phase xenon-enhanced CT in patients with chronic obstructive pulmonary disease (COPD). MATERIALS AND METHODS: Thirty-eight patien...

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Autores principales: Yoon, Soon Ho, Goo, Jin Mo, Jung, Julip, Hong, Helen, Park, Eun Ah, Lee, Chang Hyun, Lee, Youkyung, Jin, Kwang Nam, Choo, Ji Yung, Lee, Nyoung Keun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Korean Society of Radiology 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4023059/
https://www.ncbi.nlm.nih.gov/pubmed/24843245
http://dx.doi.org/10.3348/kjr.2014.15.3.386
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author Yoon, Soon Ho
Goo, Jin Mo
Jung, Julip
Hong, Helen
Park, Eun Ah
Lee, Chang Hyun
Lee, Youkyung
Jin, Kwang Nam
Choo, Ji Yung
Lee, Nyoung Keun
author_facet Yoon, Soon Ho
Goo, Jin Mo
Jung, Julip
Hong, Helen
Park, Eun Ah
Lee, Chang Hyun
Lee, Youkyung
Jin, Kwang Nam
Choo, Ji Yung
Lee, Nyoung Keun
author_sort Yoon, Soon Ho
collection PubMed
description OBJECTIVE: To evaluate the technical feasibility, performance, and interobserver agreement of a computer-aided classification (CAC) system for regional ventilation at two-phase xenon-enhanced CT in patients with chronic obstructive pulmonary disease (COPD). MATERIALS AND METHODS: Thirty-eight patients with COPD underwent two-phase xenon ventilation CT with resulting wash-in (WI) and wash-out (WO) xenon images. The regional ventilation in structural abnormalities was visually categorized into four patterns by consensus of two experienced radiologists who compared the xenon attenuation of structural abnormalities with that of adjacent normal parenchyma in the WI and WO images, and it served as the reference. Two series of image datasets of structural abnormalities were randomly extracted for optimization and validation. The proportion of agreement on a per-lesion basis and receiver operating characteristics on a per-pixel basis between CAC and reference were analyzed for optimization. Thereafter, six readers independently categorized the regional ventilation in structural abnormalities in the validation set without and with a CAC map. Interobserver agreement was also compared between assessments without and with CAC maps using multirater κ statistics. RESULTS: Computer-aided classification maps were successfully generated in 31 patients (81.5%). The proportion of agreement and the average area under the curve of optimized CAC maps were 94% (75/80) and 0.994, respectively. Multirater κ value was improved from moderate (κ = 0.59; 95% confidence interval [CI], 0.56-0.62) at the initial assessment to excellent (κ = 0.82; 95% CI, 0.79-0.85) with the CAC map. CONCLUSION: Our proposed CAC system demonstrated the potential for regional ventilation pattern analysis and enhanced interobserver agreement on visual classification of regional ventilation.
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spelling pubmed-40230592014-05-19 Computer-Aided Classification of Visual Ventilation Patterns in Patients with Chronic Obstructive Pulmonary Disease at Two-Phase Xenon-Enhanced CT Yoon, Soon Ho Goo, Jin Mo Jung, Julip Hong, Helen Park, Eun Ah Lee, Chang Hyun Lee, Youkyung Jin, Kwang Nam Choo, Ji Yung Lee, Nyoung Keun Korean J Radiol Thoracic Imaging OBJECTIVE: To evaluate the technical feasibility, performance, and interobserver agreement of a computer-aided classification (CAC) system for regional ventilation at two-phase xenon-enhanced CT in patients with chronic obstructive pulmonary disease (COPD). MATERIALS AND METHODS: Thirty-eight patients with COPD underwent two-phase xenon ventilation CT with resulting wash-in (WI) and wash-out (WO) xenon images. The regional ventilation in structural abnormalities was visually categorized into four patterns by consensus of two experienced radiologists who compared the xenon attenuation of structural abnormalities with that of adjacent normal parenchyma in the WI and WO images, and it served as the reference. Two series of image datasets of structural abnormalities were randomly extracted for optimization and validation. The proportion of agreement on a per-lesion basis and receiver operating characteristics on a per-pixel basis between CAC and reference were analyzed for optimization. Thereafter, six readers independently categorized the regional ventilation in structural abnormalities in the validation set without and with a CAC map. Interobserver agreement was also compared between assessments without and with CAC maps using multirater κ statistics. RESULTS: Computer-aided classification maps were successfully generated in 31 patients (81.5%). The proportion of agreement and the average area under the curve of optimized CAC maps were 94% (75/80) and 0.994, respectively. Multirater κ value was improved from moderate (κ = 0.59; 95% confidence interval [CI], 0.56-0.62) at the initial assessment to excellent (κ = 0.82; 95% CI, 0.79-0.85) with the CAC map. CONCLUSION: Our proposed CAC system demonstrated the potential for regional ventilation pattern analysis and enhanced interobserver agreement on visual classification of regional ventilation. The Korean Society of Radiology 2014 2014-04-29 /pmc/articles/PMC4023059/ /pubmed/24843245 http://dx.doi.org/10.3348/kjr.2014.15.3.386 Text en Copyright © 2014 The Korean Society of Radiology http://creativecommons.org/licenses/by-nc/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Thoracic Imaging
Yoon, Soon Ho
Goo, Jin Mo
Jung, Julip
Hong, Helen
Park, Eun Ah
Lee, Chang Hyun
Lee, Youkyung
Jin, Kwang Nam
Choo, Ji Yung
Lee, Nyoung Keun
Computer-Aided Classification of Visual Ventilation Patterns in Patients with Chronic Obstructive Pulmonary Disease at Two-Phase Xenon-Enhanced CT
title Computer-Aided Classification of Visual Ventilation Patterns in Patients with Chronic Obstructive Pulmonary Disease at Two-Phase Xenon-Enhanced CT
title_full Computer-Aided Classification of Visual Ventilation Patterns in Patients with Chronic Obstructive Pulmonary Disease at Two-Phase Xenon-Enhanced CT
title_fullStr Computer-Aided Classification of Visual Ventilation Patterns in Patients with Chronic Obstructive Pulmonary Disease at Two-Phase Xenon-Enhanced CT
title_full_unstemmed Computer-Aided Classification of Visual Ventilation Patterns in Patients with Chronic Obstructive Pulmonary Disease at Two-Phase Xenon-Enhanced CT
title_short Computer-Aided Classification of Visual Ventilation Patterns in Patients with Chronic Obstructive Pulmonary Disease at Two-Phase Xenon-Enhanced CT
title_sort computer-aided classification of visual ventilation patterns in patients with chronic obstructive pulmonary disease at two-phase xenon-enhanced ct
topic Thoracic Imaging
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4023059/
https://www.ncbi.nlm.nih.gov/pubmed/24843245
http://dx.doi.org/10.3348/kjr.2014.15.3.386
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