Computer-aided detection of lung cancer on chest radiographs: effect on observer performance.
Purpose: To assess how computer-aided detection (CAD) affects reader performance in detecting early lung cancer on chest radiographs. Materials and Methods: In this ethics committee-approved study, 46 individuals with 49 computed tomographically (CT)-detected and histologically proved lung cancers a...
| Publicado en: | Radiology Vol. 257; no. 2; pp. 532 - 541 |
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| Autores principales: | , , , , , , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Radiological Society of North America
2010 Nov
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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=104932304&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104932304 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00338419 0IQ jtl: Radiology issn: 00338419 maglogo: N pubinfo: dt: 2010 Nov vid: 257 iid: 2 pid: 2091 pub: Radiological Society of North America place: Oak Brook, Illinois artinfo: ui: 104932304 104932304 NLM20807851 2010835765 10.1148/radiol.10092437 NLM20807851 104932304 ppf: 532 ppct: 9 formats: tig: atl: Computer-aided detection of lung cancer on chest radiographs: effect on observer performance. aug: au: de Hoop B De Boo DW Gietema HA van Hoorn F Mearadji B Schijf L van Ginneken B Prokop M Schaefer-Prokop C de Hoop, Bartjan De Boo, Diederik W Gietema, Hester A van Hoorn, Frans Mearadji, Banafsche Schijf, Laura van Ginneken, Bram Prokop, Mathias Schaefer-Prokop, Cornelia affil: Department of Radiology and Image Sciences Institute, University Medical Center, Heidelberglaan 100, 3584 CX, Utrecht, the Netherlands sug: subj: Lung Neoplasms Radiography Radiographic Image Interpretation, Computer-Assisted Radiography, Thoracic Methods Tomography, X-Ray Computed Female Human Male Middle Age Observer Bias ROC Curve Retrospective Design Sensitivity and Specificity Software Middle Aged: 45-64 years Female Male ab: Purpose: To assess how computer-aided detection (CAD) affects reader performance in detecting early lung cancer on chest radiographs. Materials and Methods: In this ethics committee-approved study, 46 individuals with 49 computed tomographically (CT)-detected and histologically proved lung cancers and 65 patients without nodules at CT were retrospectively included. All subjects participated in a lung cancer screening trial. Chest radiographs were obtained within 2 months after screening CT. Four radiology residents and two experienced radiologists were asked to identify and localize potential cancers on the chest radiographs, first without and subsequently with the use of CAD software. A figure of merit was calculated by using free-response receiver operating characteristic analysis. Results: Tumor diameter ranged from 5.1 to 50.7 mm (median, 11.8 mm). Fifty-one percent (22 of 49) of lesions were subtle and detected by two or fewer readers. Stand-alone CAD sensitivity was 61%, with an average of 2.4 false-positive annotations per chest radiograph. Average sensitivity was 63% for radiologists at 0.23 false-positive annotations per chest radiograph and 49% for residents at 0.45 false-positive annotations per chest radiograph. Figure of merit did not change significantly for any of the observers after using CAD. CAD marked between five and 16 cancers that were initially missed by the readers. These correctly CAD-depicted lesions were rejected by radiologists in 92% of cases and by residents in 77% of cases. Conclusion: The sensitivity of CAD in identifying lung cancers depicted with CT screening was similar to that of experienced radiologists. However, CAD did not improve cancer detection because, especially for subtle lesions, observers were unable to sufficiently differentiate true-positive from false-positive annotations. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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