Usefulness of computerized method for lung nodule detection on digital chest radiographs using similar subtraction images from different patients

Abstract Purpose The purpose of this study is to evaluate the usefulness of a novel computerized method to select automatically the similar chest radiograph for image subtraction in the patients who have no previous chest radiographs and to assist the radiologists’ interpretation by presenting the “...

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Veröffentlicht in:European journal of radiology 2012-05, Vol.81 (5), p.1062-1067
Hauptverfasser: Aoki, Takatoshi, Oda, Nobuhiro, Yamashita, Yoshiko, Yamamoto, Keiji, Korogi, Yukunori
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container_end_page 1067
container_issue 5
container_start_page 1062
container_title European journal of radiology
container_volume 81
creator Aoki, Takatoshi
Oda, Nobuhiro
Yamashita, Yoshiko
Yamamoto, Keiji
Korogi, Yukunori
description Abstract Purpose The purpose of this study is to evaluate the usefulness of a novel computerized method to select automatically the similar chest radiograph for image subtraction in the patients who have no previous chest radiographs and to assist the radiologists’ interpretation by presenting the “similar subtraction image” from different patients. Materials and methods Institutional review board approval was obtained, and the requirement for informed patient consent was waived. A large database of approximately 15,000 normal chest radiographs was used for searching similar images of different patients. One hundred images of candidates were selected according to two clinical parameters and similarity of the lung field in the target image. We used the correlation value of chest region in the 100 images for searching the most similar image. The similar subtraction images were obtained by subtracting the similar image selected from the target image. Thirty cases with lung nodules and 30 cases without lung nodules were used for an observer performance test. Four attending radiologists and four radiology residents participated in this observer performance test. Results The AUC for all radiologists increased significantly from 0.925 to 0.974 with the CAD ( P = .004). When the computer output images were available, the average AUC for the residents was more improved (0.960 vs. 0.890) than for the attending radiologists (0.987 vs. 0.960). Conclusion The novel computerized method for lung nodule detection using similar subtraction images from different patients would be useful to detect lung nodules on digital chest radiographs, especially for less experienced readers.
doi_str_mv 10.1016/j.ejrad.2011.02.010
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Materials and methods Institutional review board approval was obtained, and the requirement for informed patient consent was waived. A large database of approximately 15,000 normal chest radiographs was used for searching similar images of different patients. One hundred images of candidates were selected according to two clinical parameters and similarity of the lung field in the target image. We used the correlation value of chest region in the 100 images for searching the most similar image. The similar subtraction images were obtained by subtracting the similar image selected from the target image. Thirty cases with lung nodules and 30 cases without lung nodules were used for an observer performance test. Four attending radiologists and four radiology residents participated in this observer performance test. Results The AUC for all radiologists increased significantly from 0.925 to 0.974 with the CAD ( P = .004). When the computer output images were available, the average AUC for the residents was more improved (0.960 vs. 0.890) than for the attending radiologists (0.987 vs. 0.960). Conclusion The novel computerized method for lung nodule detection using similar subtraction images from different patients would be useful to detect lung nodules on digital chest radiographs, especially for less experienced readers.</description><identifier>ISSN: 0720-048X</identifier><identifier>EISSN: 1872-7727</identifier><identifier>DOI: 10.1016/j.ejrad.2011.02.010</identifier><identifier>PMID: 21382681</identifier><language>eng</language><publisher>Ireland: Elsevier Ireland Ltd</publisher><subject>Chest radiograph ; Computer-aided diagnosis (CAD) ; Digital imaging ; Female ; Humans ; Japan - epidemiology ; Lung cancer ; Lung nodule ; Male ; Pattern Recognition, Automated - methods ; Prevalence ; Radiographic Image Enhancement - methods ; Radiography, Thoracic - statistics &amp; numerical data ; Radiology ; Reproducibility of Results ; Sensitivity and Specificity ; Solitary Pulmonary Nodule - diagnostic imaging ; Solitary Pulmonary Nodule - epidemiology ; Subtraction Technique - statistics &amp; numerical data</subject><ispartof>European journal of radiology, 2012-05, Vol.81 (5), p.1062-1067</ispartof><rights>Elsevier Ireland Ltd</rights><rights>2011 Elsevier Ireland Ltd</rights><rights>Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c480t-a1e852b2c2a6f126168163ed47b9f8427a052536ae48a31fa95d577605cbaaeb3</citedby><cites>FETCH-LOGICAL-c480t-a1e852b2c2a6f126168163ed47b9f8427a052536ae48a31fa95d577605cbaaeb3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.ejrad.2011.02.010$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/21382681$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Aoki, Takatoshi</creatorcontrib><creatorcontrib>Oda, Nobuhiro</creatorcontrib><creatorcontrib>Yamashita, Yoshiko</creatorcontrib><creatorcontrib>Yamamoto, Keiji</creatorcontrib><creatorcontrib>Korogi, Yukunori</creatorcontrib><title>Usefulness of computerized method for lung nodule detection on digital chest radiographs using similar subtraction images from different patients</title><title>European journal of radiology</title><addtitle>Eur J Radiol</addtitle><description>Abstract Purpose The purpose of this study is to evaluate the usefulness of a novel computerized method to select automatically the similar chest radiograph for image subtraction in the patients who have no previous chest radiographs and to assist the radiologists’ interpretation by presenting the “similar subtraction image” from different patients. Materials and methods Institutional review board approval was obtained, and the requirement for informed patient consent was waived. A large database of approximately 15,000 normal chest radiographs was used for searching similar images of different patients. One hundred images of candidates were selected according to two clinical parameters and similarity of the lung field in the target image. We used the correlation value of chest region in the 100 images for searching the most similar image. The similar subtraction images were obtained by subtracting the similar image selected from the target image. Thirty cases with lung nodules and 30 cases without lung nodules were used for an observer performance test. Four attending radiologists and four radiology residents participated in this observer performance test. Results The AUC for all radiologists increased significantly from 0.925 to 0.974 with the CAD ( P = .004). When the computer output images were available, the average AUC for the residents was more improved (0.960 vs. 0.890) than for the attending radiologists (0.987 vs. 0.960). 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Oda, Nobuhiro ; Yamashita, Yoshiko ; Yamamoto, Keiji ; Korogi, Yukunori</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c480t-a1e852b2c2a6f126168163ed47b9f8427a052536ae48a31fa95d577605cbaaeb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Chest radiograph</topic><topic>Computer-aided diagnosis (CAD)</topic><topic>Digital imaging</topic><topic>Female</topic><topic>Humans</topic><topic>Japan - epidemiology</topic><topic>Lung cancer</topic><topic>Lung nodule</topic><topic>Male</topic><topic>Pattern Recognition, Automated - methods</topic><topic>Prevalence</topic><topic>Radiographic Image Enhancement - methods</topic><topic>Radiography, Thoracic - statistics &amp; numerical data</topic><topic>Radiology</topic><topic>Reproducibility of Results</topic><topic>Sensitivity and Specificity</topic><topic>Solitary Pulmonary Nodule - diagnostic imaging</topic><topic>Solitary Pulmonary Nodule - epidemiology</topic><topic>Subtraction Technique - statistics &amp; numerical data</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Aoki, Takatoshi</creatorcontrib><creatorcontrib>Oda, Nobuhiro</creatorcontrib><creatorcontrib>Yamashita, Yoshiko</creatorcontrib><creatorcontrib>Yamamoto, Keiji</creatorcontrib><creatorcontrib>Korogi, Yukunori</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>European journal of radiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Aoki, Takatoshi</au><au>Oda, Nobuhiro</au><au>Yamashita, Yoshiko</au><au>Yamamoto, Keiji</au><au>Korogi, Yukunori</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Usefulness of computerized method for lung nodule detection on digital chest radiographs using similar subtraction images from different patients</atitle><jtitle>European journal of radiology</jtitle><addtitle>Eur J Radiol</addtitle><date>2012-05-01</date><risdate>2012</risdate><volume>81</volume><issue>5</issue><spage>1062</spage><epage>1067</epage><pages>1062-1067</pages><issn>0720-048X</issn><eissn>1872-7727</eissn><abstract>Abstract Purpose The purpose of this study is to evaluate the usefulness of a novel computerized method to select automatically the similar chest radiograph for image subtraction in the patients who have no previous chest radiographs and to assist the radiologists’ interpretation by presenting the “similar subtraction image” from different patients. Materials and methods Institutional review board approval was obtained, and the requirement for informed patient consent was waived. A large database of approximately 15,000 normal chest radiographs was used for searching similar images of different patients. One hundred images of candidates were selected according to two clinical parameters and similarity of the lung field in the target image. We used the correlation value of chest region in the 100 images for searching the most similar image. The similar subtraction images were obtained by subtracting the similar image selected from the target image. Thirty cases with lung nodules and 30 cases without lung nodules were used for an observer performance test. Four attending radiologists and four radiology residents participated in this observer performance test. Results The AUC for all radiologists increased significantly from 0.925 to 0.974 with the CAD ( P = .004). When the computer output images were available, the average AUC for the residents was more improved (0.960 vs. 0.890) than for the attending radiologists (0.987 vs. 0.960). Conclusion The novel computerized method for lung nodule detection using similar subtraction images from different patients would be useful to detect lung nodules on digital chest radiographs, especially for less experienced readers.</abstract><cop>Ireland</cop><pub>Elsevier Ireland Ltd</pub><pmid>21382681</pmid><doi>10.1016/j.ejrad.2011.02.010</doi><tpages>6</tpages></addata></record>
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subjects Chest radiograph
Computer-aided diagnosis (CAD)
Digital imaging
Female
Humans
Japan - epidemiology
Lung cancer
Lung nodule
Male
Pattern Recognition, Automated - methods
Prevalence
Radiographic Image Enhancement - methods
Radiography, Thoracic - statistics & numerical data
Radiology
Reproducibility of Results
Sensitivity and Specificity
Solitary Pulmonary Nodule - diagnostic imaging
Solitary Pulmonary Nodule - epidemiology
Subtraction Technique - statistics & numerical data
title Usefulness of computerized method for lung nodule detection on digital chest radiographs using similar subtraction images from different patients
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