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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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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The Korean Society of Radiology
2014
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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. |
format | Online Article Text |
id | pubmed-4023059 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | The Korean Society of Radiology |
record_format | MEDLINE/PubMed |
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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