Literature DB >> 18941043

Performance evaluation of a computer-aided detection algorithm for solid pulmonary nodules in low-dose and standard-dose MDCT chest examinations and its influence on radiologists.

M Das1, G Mühlenbruch, S Heinen, A H Mahnken, M Salganicoff, S Stanzel, R W Günther, J E Wildberger.   

Abstract

The aim of the study was to evaluate the performance of a computer-aided detection (CAD) algorithm in low-dose and full-dose multidetector-row CT (MDCT) of the thorax and its impact on radiologists' performance. Chest CT examinations of 77 patients were evaluated retrospectively for pulmonary nodules. All patients had undergone a 16-slice MDCT chest examination with a standard acquisition protocol. Artificial image noise was added to the raw data to simulate image acquisition at 10 mAs(eff.) The data were transferred to dedicated lung analysis software (LungCare) with a prototype CAD algorithm (LungCAD). CAD was applied to both dose settings. Images were read by a radiologist and a first-year resident with and without the software at both dose settings. All images were reviewed in consensus by the two radiologists to set the reference standard. Sensitivity results with respect to the reference standard were compared. No statistically significant differences in the detection rate for all pulmonary nodules could be found between low-dose and full-dose settings for the CAD software alone (p = 0.0065). Both radiologists displayed a statistically significant increase in sensitivity with the use of CAD (p<0.0001). In conclusion, CAD is beneficial in both low-dose and standard-dose settings. This may be beneficial in reducing false-negative diagnosis in lung cancer screening, standard chest examinations and the search for metastases.

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Year:  2008        PMID: 18941043     DOI: 10.1259/bjr/50635688

Source DB:  PubMed          Journal:  Br J Radiol        ISSN: 0007-1285            Impact factor:   3.039


  17 in total

1.  Detection of noncalcified pulmonary nodules on low-dose MDCT: comparison of the sensitivity of two CAD systems by using a double reference standard.

Authors:  A R Larici; M Amato; P Ordóñez; F Maggi; L Menchini; A Caulo; L Calandriello; G Vallati; S Giunta; M Crecco; L Bonomo
Journal:  Radiol Med       Date:  2012-02-10       Impact factor: 3.469

2.  Impact of a computer-aided detection (CAD) system integrated into a picture archiving and communication system (PACS) on reader sensitivity and efficiency for the detection of lung nodules in thoracic CT exams.

Authors:  Luca Bogoni; Jane P Ko; Jeffrey Alpert; Vikram Anand; John Fantauzzi; Charles H Florin; Chi Wan Koo; Derek Mason; William Rom; Maria Shiau; Marcos Salganicoff; David P Naidich
Journal:  J Digit Imaging       Date:  2012-12       Impact factor: 4.056

3.  Evaluation of a method of computer-aided detection (CAD) of pulmonary nodules with computed tomography.

Authors:  G Foti; N Faccioli; M D'Onofrio; A Contro; T Milazzo; R Pozzi Mucelli
Journal:  Radiol Med       Date:  2010-06-23       Impact factor: 3.469

Review 4.  CAD (computed-aided detection) and CADx (computer aided diagnosis) systems in identifying and characterising lung nodules on chest CT: overview of research, developments and new prospects.

Authors:  F Fraioli; G Serra; R Passariello
Journal:  Radiol Med       Date:  2010-01-15       Impact factor: 3.469

5.  Integration of fully automated computer-aided pulmonary nodule detection into CT pulmonary angiography studies in the emergency department: effect on workflow and diagnostic accuracy.

Authors:  Amirhossein Mozaffary; Tugce Agirlar Trabzonlu; Pamela Lombardi; Adeel R Seyal; Rishi Agrawal; Vahid Yaghmai
Journal:  Emerg Radiol       Date:  2019-07-27

6.  A cloud-based computer-aided detection system improves identification of lung nodules on computed tomography scans of patients with extra-thoracic malignancies.

Authors:  Lorenzo Vassallo; Alberto Traverso; Michelangelo Agnello; Christian Bracco; Delia Campanella; Gabriele Chiara; Maria Evelina Fantacci; Ernesto Lopez Torres; Antonio Manca; Marco Saletta; Valentina Giannini; Simone Mazzetti; Michele Stasi; Piergiorgio Cerello; Daniele Regge
Journal:  Eur Radiol       Date:  2018-06-15       Impact factor: 5.315

7.  The effect of radiation dose reduction on computer-aided detection (CAD) performance in a low-dose lung cancer screening population.

Authors:  Stefano Young; Pechin Lo; Grace Kim; Matthew Brown; John Hoffman; William Hsu; Wasil Wahi-Anwar; Carlos Flores; Grace Lee; Frederic Noo; Jonathan Goldin; Michael McNitt-Gray
Journal:  Med Phys       Date:  2017-03-14       Impact factor: 4.071

8.  Seamless Insertion of Pulmonary Nodules in Chest CT Images.

Authors:  Aria Pezeshk; Berkman Sahiner; Rongping Zeng; Adam Wunderlich; Weijie Chen; Nicholas Petrick
Journal:  IEEE Trans Biomed Eng       Date:  2015-06-12       Impact factor: 4.538

9.  Computer-aided detection (CAD) of lung nodules in CT scans: radiologist performance and reading time with incremental CAD assistance.

Authors:  Justus E Roos; David Paik; David Olsen; Emily G Liu; Lawrence C Chow; Ann N Leung; Robert Mindelzun; Kingshuk R Choudhury; David P Naidich; Sandy Napel; Geoffrey D Rubin
Journal:  Eur Radiol       Date:  2009-09-16       Impact factor: 5.315

Review 10.  Lung Cancer Screening, Version 3.2018, NCCN Clinical Practice Guidelines in Oncology.

Authors:  Douglas E Wood; Ella A Kazerooni; Scott L Baum; George A Eapen; David S Ettinger; Lifang Hou; David M Jackman; Donald Klippenstein; Rohit Kumar; Rudy P Lackner; Lorriana E Leard; Inga T Lennes; Ann N C Leung; Samir S Makani; Pierre P Massion; Peter Mazzone; Robert E Merritt; Bryan F Meyers; David E Midthun; Sudhakar Pipavath; Christie Pratt; Chakravarthy Reddy; Mary E Reid; Arnold J Rotter; Peter B Sachs; Matthew B Schabath; Mark L Schiebler; Betty C Tong; William D Travis; Benjamin Wei; Stephen C Yang; Kristina M Gregory; Miranda Hughes
Journal:  J Natl Compr Canc Netw       Date:  2018-04       Impact factor: 11.908

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