The effect of computer-aided detection on radiologist performance in the detection of lung cancers previously missed on a chest radiograph.
Purpose: The purpose of the study was to determine whether computer-aided detection (CAD) can improve a radiologist's ability to detect lung cancers previously missed on a chest radiograph (CXR). Materials and Methods: Eighty-one cases of lung cancer previously missed on CXR were collected, along wi...
| Publicado en: | Journal of Thoracic Imaging Vol. 28; no. 4; pp. 244 - 253 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
2013 Jul
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=107906231&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 107906231 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08835993 N1M jtl: Journal of Thoracic Imaging issn: 08835993 maglogo: N pubinfo: dt: 2013 Jul vid: 28 iid: 4 pid: 433 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 107906231 107906231 NLM23059738 2012165947 10.1097/RTI.0b013e31826c29ec NLM23059738 107906231 ppf: 244 ppct: 9 formats: tig: atl: The effect of computer-aided detection on radiologist performance in the detection of lung cancers previously missed on a chest radiograph. aug: au: Kligerman, Seth Cai, Ling White, Charles S affil: Departments of *Diagnostic Radiology and Nuclear Medicine tBiostatistics, University of Maryland School of Medicine, Baltimore, MD. sug: subj: Clinical Competence Lung Neoplasms Radiography Radiographic Image Interpretation, Computer-Assisted Adult Aged Aged, 80 and Over Diagnostic Errors Female Image Processing, Computer Assisted Male Middle Age Adult: 19-44 years Aged: 65+ years Aged, 80 & over Middle Aged: 45-64 years Female Male ab: Purpose: The purpose of the study was to determine whether computer-aided detection (CAD) can improve a radiologist's ability to detect lung cancers previously missed on a chest radiograph (CXR). Materials and Methods: Eighty-one cases of lung cancer previously missed on CXR were collected, along with the CXRs of 215 age-matched and emphysema-matched controls without lung cancer. Tumor subtlety was scored from 1 (very subtle) to 5 (very obvious) by expert thoracic radiologists. All 297 CXRs were processed using a CAD system (OnGuard, Version 5.1 Riverain Medical, Miamisburg, OH) to create a set of 2 images for each patient, 1 with and 1 without CAD. Eleven general radiologists took part in a reader study. Each radiologist viewed the CXR without CAD and then the one with CAD for each patient sequentially. Areas of concern, if present, were marked. The degree of confidence in diagnosis was scored on a scale of 0 (no cancer) to 100 (definite) in succession for CXRs without CAD and then for those with CAD. Localization receiver operating characteristic analysis was used for evaluation of the observers' performance. Results: Of the 81 cancer cases, OnGuard correctly detected 40 tumors with a sensitivity of 49.4%. In the reader study, there was a significant increase in the area under the localization receiver operating characteristic curve with the aid of OnGuard, which increased from 0.38 to 0.43. Aggregate reader sensitivity improved significantly from 0.44 to 0.5 with the use of OnGuard. Conclusion: The use of OnGuard improves reader accuracy and sensitivity for the detection of lung cancers previously missed on CXR. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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