Auer, Florian Johann

[["dc.bibliographiccitation.firstpage","137"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Clinical Radiology"],["dc.bibliographiccitation.lastpage","144"],["dc.bibliographiccitation.volume","65"],["dc.contributor.author","Engelke, C."],["dc.contributor.author","Schmidt, S."],["dc.contributor.author","Auer, F."],["dc.contributor.author","Rummeny, Ernst J."],["dc.contributor.author","Marten, Katharina"],["dc.date.accessioned","2018-11-07T08:46:07Z"],["dc.date.available","2018-11-07T08:46:07Z"],["dc.date.issued","2010"],["dc.description.abstract","AIM: To prospectively assess the value of computer-aided detection (CAD) for the Computed tomography (CT) severity assessment of acute Pulmonary embolism (PE). MATERIALS AND METHODS: CT angiographic scans of 58 PE-positive patients (34-89 years, mean 66 years) were analysed by four observers for PE severity using the Mastora index, and by CAD. Patients were stratified to three PE risk groups and results Compared to an independent reference standard. Interobserver agreement was tested by Bland and Altman and extended kappa (Ke) statistics. Mastora index changes after CAD data review were tested by Wilcoxon signed ranks. RESULTS: CAD detected 343 out of 1118 emboli within given arterial segments and a total of 155 out of 218 polysegmental emboli (segmental vessel-based sensitivity = 30.7%, embolus-based sensitivity = 71.2% false-positive rate = 4.1/scan). Interobserver agreement on PE severity 195% limits of agreement (LOA) = -19.7-7.5% and-5.5-3% for reader pairs I versus 2 and 3 versus 4, respectively was enhanced by consensus with CAD data (LOA = -6.5-5.4% and -3.7-2% for reader pairs I versus 2 and 3 versus 4, respectively). Simultaneously, the percentual scoring errors (PSE) were significantly decreased (PSE = 35.4 +/- 31.8% and 5.1 +/- 8.9% for readers 1/2 and 2/3, respectively, and PSE = 27.6 +/- 31 % and 3.8 +/- 6.2%, respectively, after CAD consensus; p <= 0.005). Misclassifications to PE risk groups occurred in 27.6, 24.1, 5.2, and 5.2% of patients for readers 1-4, respectively, (Ke = 0.74) and were corrected by CAD consensus in 56.3, 36, 33.3, and 33.3% of misclassified patients, respectively (Ke = 0.83; p < 0.05). CONCLUSION: Radiologists may benefit from consensus with CAD data that improve PE severity scores and stratification to PE risk groups. (C) 2009 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved."],["dc.identifier.doi","10.1016/j.crad.2009.10.007"],["dc.identifier.isi","000275091700006"],["dc.identifier.pmid","20103436"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/20611"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","W B Saunders Co Ltd"],["dc.relation.issn","0009-9260"],["dc.title","Does computer-assisted detection of pulmonary emboli enhance severity assessment and risk stratification in acute pulmonary embolism?"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1930"],["dc.bibliographiccitation.issue","10"],["dc.bibliographiccitation.journal","European Radiology"],["dc.bibliographiccitation.lastpage","1938"],["dc.bibliographiccitation.volume","14"],["dc.contributor.author","Marten, Katharina"],["dc.contributor.author","Seyfarth, T."],["dc.contributor.author","Auer, F."],["dc.contributor.author","Wiener, E."],["dc.contributor.author","Grillhosl, A."],["dc.contributor.author","Obenauer, Silvia"],["dc.contributor.author","Rummeny, Ernst J."],["dc.contributor.author","Engelke, C."],["dc.date.accessioned","2018-11-07T10:45:09Z"],["dc.date.available","2018-11-07T10:45:09Z"],["dc.date.issued","2004"],["dc.description.abstract","To evaluate the performance of experienced versus inexperienced radiologists in comparison and in consensus with an interactive computer-aided detection (CAD) system for detection of pulmonary nodules. Eighteen consecutive patients (mean age: 62.2 years; range 29-83 years) prospectively underwent routine 16-row multislice computed tomography (MSCT). Four blinded radiologists (experienced: readers 1, 2; inexperienced: readers 3, 4) assessed image data against CAD for pulmonary nodules. Thereafter, consensus readings of readers 1+3, reader 1+CAD and reader 3+CAD were performed. Data were compared against an independent gold standard. Statistical tests used to calculate interobserver agreement, reader performance and nodule size were Kappa, ROC and Mann-Whitney U. CAD and experienced readers outperformed inexperienced readers (Az=0.72, 0.71, 0.73, 0.49 and 0.50 for CAD, readers 1-4, respectively; P<0.05). Performance of reader 1+CAD was superior to single reader and reader 1+3 performances (Az=0.93, 0.72 for reader 1+CAD and reader 1+3 consensus, respectively, P<0.05). Reader 3+CAD did not perform superiorly to experienced readers or CAD (Az=0.79 for reader 3+CAD; P>0.05). Consensus of reader 1+CAD significantly outperformed all other readings, demonstrating a benefit in using CAD as an inexperienced reader replacement. It is questionable whether inexperienced readers can be regarded as adequate for interpretation of pulmonary nodules in consensus with CAD, replacing an experienced radiologist."],["dc.identifier.doi","10.1007/s00330-004-2389-y"],["dc.identifier.isi","000226576500028"],["dc.identifier.pmid","15235812"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/47434"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Springer"],["dc.relation.issn","0938-7994"],["dc.title","Computer-assisted detection of pulmonary nodules: performance evaluation of an expert knowledge-based detection system in consensus reading with experienced and inexperienced chest radiologists"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","716"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Bioinformatics"],["dc.bibliographiccitation.lastpage","717"],["dc.bibliographiccitation.volume","34"],["dc.contributor.author","Auer, Florian"],["dc.contributor.author","Hammoud, Zaynab"],["dc.contributor.author","Ishkin, Alexandr"],["dc.contributor.author","Pratt, Dexter"],["dc.contributor.author","Ideker, Trey"],["dc.contributor.author","Kramer, Frank"],["dc.contributor.editor","Kelso, Janet"],["dc.date.accessioned","2020-12-10T18:17:34Z"],["dc.date.available","2020-12-10T18:17:34Z"],["dc.date.issued","2017"],["dc.identifier.doi","10.1093/bioinformatics/btx683"],["dc.identifier.eissn","1460-2059"],["dc.identifier.issn","1367-4803"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/75078"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","ndexr—an R package to interface with the network data exchange"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]