Quantifying the extent of emphysema: factors associated with radiologists' estimations and quantitative indices of emphysema severity using the ECLIPSE cohort
This study investigated what factors radiologists take into account when estimating emphysema severity and assessed quantitative computed tomography (CT) measurements of low attenuation areas. CT scans and spirometry were obtained on 1519 chronic obstructive pulmonary disease (COPD) subjects, 269 sm...
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| Veröffentlicht in: | Academic radiology 2011-06, Vol.18 (6), p.661-671 |
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| creator | Gietema, Hester A Müller, Nestor L Fauerbach, Paola V Nasute Sharma, Sanjay Edwards, Lisa D Camp, Pat G Coxson, Harvey O |
| description | This study investigated what factors radiologists take into account when estimating emphysema severity and assessed quantitative computed tomography (CT) measurements of low attenuation areas.
CT scans and spirometry were obtained on 1519 chronic obstructive pulmonary disease (COPD) subjects, 269 smoker controls, and 184 nonsmoker controls from the Evaluation of COPD Longitudinally to Indentify Surrogate Endpoints (ECLIPSE) study. CT scans were analyzed using the threshold technique (% |
| doi_str_mv | 10.1016/j.acra.2011.01.011 |
| format | Article |
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CT scans and spirometry were obtained on 1519 chronic obstructive pulmonary disease (COPD) subjects, 269 smoker controls, and 184 nonsmoker controls from the Evaluation of COPD Longitudinally to Indentify Surrogate Endpoints (ECLIPSE) study. CT scans were analyzed using the threshold technique (%<-950HU) and a low attenuation cluster analysis. Two radiologists scored emphysema severity (0 to 5 scale), described the predominant type and distribution of emphysema, and the presence of suspected small airways disease.
The percent low attenuation area (%LAA) and visual scores of emphysema severity correlated well (r = 0.77, P < .001). %LAA, low attenuation cluster analysis, and absence of radiologist described gas trapping, distribution, and predominant type of emphysema were predictors of visual scores of emphysema severity (all P < .001). CT scans scored as showing regions of gas trapping had smaller lesions for a similar %LAA than those without (P < .001).
Visual estimates of emphysema are not only determined by the extent of LAA, but also by lesion size, predominant type, and distribution of emphysema and presence/absence of areas of small airways disease. A computer analysis of low attenuation cluster size helps quantitative algorithms discriminate low attenuation areas from gas trapping, image noise, and emphysema.</description><identifier>EISSN: 1878-4046</identifier><identifier>DOI: 10.1016/j.acra.2011.01.011</identifier><identifier>PMID: 21393027</identifier><language>eng</language><publisher>United States</publisher><subject>Adult ; Aged ; Algorithms ; Analysis of Variance ; Biomarkers ; Case-Control Studies ; Cluster Analysis ; Disease Progression ; Female ; Forced Expiratory Volume ; Humans ; Longitudinal Studies ; Male ; Middle Aged ; Pulmonary Emphysema - diagnostic imaging ; Pulmonary Emphysema - physiopathology ; Radiographic Image Interpretation, Computer-Assisted ; Regression Analysis ; Severity of Illness Index ; Spirometry ; Tomography, X-Ray Computed</subject><ispartof>Academic radiology, 2011-06, Vol.18 (6), p.661-671</ispartof><rights>Copyright © 2011 AUR. Published by Elsevier Inc. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/21393027$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Gietema, Hester A</creatorcontrib><creatorcontrib>Müller, Nestor L</creatorcontrib><creatorcontrib>Fauerbach, Paola V Nasute</creatorcontrib><creatorcontrib>Sharma, Sanjay</creatorcontrib><creatorcontrib>Edwards, Lisa D</creatorcontrib><creatorcontrib>Camp, Pat G</creatorcontrib><creatorcontrib>Coxson, Harvey O</creatorcontrib><creatorcontrib>Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE) investigators</creatorcontrib><title>Quantifying the extent of emphysema: factors associated with radiologists' estimations and quantitative indices of emphysema severity using the ECLIPSE cohort</title><title>Academic radiology</title><addtitle>Acad Radiol</addtitle><description>This study investigated what factors radiologists take into account when estimating emphysema severity and assessed quantitative computed tomography (CT) measurements of low attenuation areas.
CT scans and spirometry were obtained on 1519 chronic obstructive pulmonary disease (COPD) subjects, 269 smoker controls, and 184 nonsmoker controls from the Evaluation of COPD Longitudinally to Indentify Surrogate Endpoints (ECLIPSE) study. CT scans were analyzed using the threshold technique (%<-950HU) and a low attenuation cluster analysis. Two radiologists scored emphysema severity (0 to 5 scale), described the predominant type and distribution of emphysema, and the presence of suspected small airways disease.
The percent low attenuation area (%LAA) and visual scores of emphysema severity correlated well (r = 0.77, P < .001). %LAA, low attenuation cluster analysis, and absence of radiologist described gas trapping, distribution, and predominant type of emphysema were predictors of visual scores of emphysema severity (all P < .001). CT scans scored as showing regions of gas trapping had smaller lesions for a similar %LAA than those without (P < .001).
Visual estimates of emphysema are not only determined by the extent of LAA, but also by lesion size, predominant type, and distribution of emphysema and presence/absence of areas of small airways disease. A computer analysis of low attenuation cluster size helps quantitative algorithms discriminate low attenuation areas from gas trapping, image noise, and emphysema.</description><subject>Adult</subject><subject>Aged</subject><subject>Algorithms</subject><subject>Analysis of Variance</subject><subject>Biomarkers</subject><subject>Case-Control Studies</subject><subject>Cluster Analysis</subject><subject>Disease Progression</subject><subject>Female</subject><subject>Forced Expiratory Volume</subject><subject>Humans</subject><subject>Longitudinal Studies</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Pulmonary Emphysema - diagnostic imaging</subject><subject>Pulmonary Emphysema - physiopathology</subject><subject>Radiographic Image Interpretation, Computer-Assisted</subject><subject>Regression Analysis</subject><subject>Severity of Illness Index</subject><subject>Spirometry</subject><subject>Tomography, X-Ray Computed</subject><issn>1878-4046</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpVkE1Lw0AQhhdBbK3-AQ-yt54Sd_Mdb1KqFgoq6jlMsrPNliSbZjfV_Bl_q1HbgzAwMLw8w_MScsWZyxmPbrYuFB24HuPcZT_DT8iUJ3HiBCyIJuTcmC1jPIwS_4xMPO6nPvPiKfl66aGxSg6q2VBbIsVPi42lWlKs23IwWMMtlVBY3RkKxuhCgUVBP5QtaQdC6UpvlLFmTtFYVYNVuhmTjaC7X7QdL3ukqhGqQPMPTA3usVN2oL05_l8u1qvn1yUtdKk7e0FOJVQGLw97Rt7vl2-LR2f99LBa3K2d1uPMOhAkUZDnbJSSKYs5JAhhHuZBJAoR88iPc08mEBa5F4NMJUoBHDCHNBIslujPyPyP23Z6148iWa1MgVUFDereZEnsB2nge2xMXh-SfV6jyNpudO6G7Fip_w0GT3z_</recordid><startdate>201106</startdate><enddate>201106</enddate><creator>Gietema, Hester A</creator><creator>Müller, Nestor L</creator><creator>Fauerbach, Paola V Nasute</creator><creator>Sharma, Sanjay</creator><creator>Edwards, Lisa D</creator><creator>Camp, Pat G</creator><creator>Coxson, Harvey O</creator><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>7X8</scope></search><sort><creationdate>201106</creationdate><title>Quantifying the extent of emphysema: factors associated with radiologists' estimations and quantitative indices of emphysema severity using the ECLIPSE cohort</title><author>Gietema, Hester A ; Müller, Nestor L ; Fauerbach, Paola V Nasute ; Sharma, Sanjay ; Edwards, Lisa D ; Camp, Pat G ; Coxson, Harvey O</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p210t-a4864bb0930f9071a8ea5b5b46dcd71637b2f8a5cb27af9fefda1aeba96d07fe3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Algorithms</topic><topic>Analysis of Variance</topic><topic>Biomarkers</topic><topic>Case-Control Studies</topic><topic>Cluster Analysis</topic><topic>Disease Progression</topic><topic>Female</topic><topic>Forced Expiratory Volume</topic><topic>Humans</topic><topic>Longitudinal Studies</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Pulmonary Emphysema - diagnostic imaging</topic><topic>Pulmonary Emphysema - physiopathology</topic><topic>Radiographic Image Interpretation, Computer-Assisted</topic><topic>Regression Analysis</topic><topic>Severity of Illness Index</topic><topic>Spirometry</topic><topic>Tomography, X-Ray Computed</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gietema, Hester A</creatorcontrib><creatorcontrib>Müller, Nestor L</creatorcontrib><creatorcontrib>Fauerbach, Paola V Nasute</creatorcontrib><creatorcontrib>Sharma, Sanjay</creatorcontrib><creatorcontrib>Edwards, Lisa D</creatorcontrib><creatorcontrib>Camp, Pat G</creatorcontrib><creatorcontrib>Coxson, Harvey O</creatorcontrib><creatorcontrib>Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE) investigators</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>MEDLINE - Academic</collection><jtitle>Academic radiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gietema, Hester A</au><au>Müller, Nestor L</au><au>Fauerbach, Paola V Nasute</au><au>Sharma, Sanjay</au><au>Edwards, Lisa D</au><au>Camp, Pat G</au><au>Coxson, Harvey O</au><aucorp>Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE) investigators</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Quantifying the extent of emphysema: factors associated with radiologists' estimations and quantitative indices of emphysema severity using the ECLIPSE cohort</atitle><jtitle>Academic radiology</jtitle><addtitle>Acad Radiol</addtitle><date>2011-06</date><risdate>2011</risdate><volume>18</volume><issue>6</issue><spage>661</spage><epage>671</epage><pages>661-671</pages><eissn>1878-4046</eissn><abstract>This study investigated what factors radiologists take into account when estimating emphysema severity and assessed quantitative computed tomography (CT) measurements of low attenuation areas.
CT scans and spirometry were obtained on 1519 chronic obstructive pulmonary disease (COPD) subjects, 269 smoker controls, and 184 nonsmoker controls from the Evaluation of COPD Longitudinally to Indentify Surrogate Endpoints (ECLIPSE) study. CT scans were analyzed using the threshold technique (%<-950HU) and a low attenuation cluster analysis. Two radiologists scored emphysema severity (0 to 5 scale), described the predominant type and distribution of emphysema, and the presence of suspected small airways disease.
The percent low attenuation area (%LAA) and visual scores of emphysema severity correlated well (r = 0.77, P < .001). %LAA, low attenuation cluster analysis, and absence of radiologist described gas trapping, distribution, and predominant type of emphysema were predictors of visual scores of emphysema severity (all P < .001). CT scans scored as showing regions of gas trapping had smaller lesions for a similar %LAA than those without (P < .001).
Visual estimates of emphysema are not only determined by the extent of LAA, but also by lesion size, predominant type, and distribution of emphysema and presence/absence of areas of small airways disease. A computer analysis of low attenuation cluster size helps quantitative algorithms discriminate low attenuation areas from gas trapping, image noise, and emphysema.</abstract><cop>United States</cop><pmid>21393027</pmid><doi>10.1016/j.acra.2011.01.011</doi><tpages>11</tpages></addata></record> |
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| subjects | Adult Aged Algorithms Analysis of Variance Biomarkers Case-Control Studies Cluster Analysis Disease Progression Female Forced Expiratory Volume Humans Longitudinal Studies Male Middle Aged Pulmonary Emphysema - diagnostic imaging Pulmonary Emphysema - physiopathology Radiographic Image Interpretation, Computer-Assisted Regression Analysis Severity of Illness Index Spirometry Tomography, X-Ray Computed |
| title | Quantifying the extent of emphysema: factors associated with radiologists' estimations and quantitative indices of emphysema severity using the ECLIPSE cohort |
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