Literature DB >> 22327922

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

A R Larici1, M Amato, P Ordóñez, F Maggi, L Menchini, A Caulo, L Calandriello, G Vallati, S Giunta, M Crecco, L Bonomo.   

Abstract

PURPOSE: This study compared the sensitivity of two commercial computer-aided detection (CAD) systems in identifying noncalcified pulmonary nodules on low-dose multidetector computed tomography (MDCT) scans by using a double reference standard.
MATERIALS AND METHODS: Three chest low-dose MDCT scans of patients who had undergone lung cancer screening were retrospectively analysed using two distinct commercial CAD systems: LungCAD VC10A, Siemens Medical Solutions (CAD1) and LungVCAR, GE Healthcare (CAD2). The exact location of each finding suggested by each system was recorded by an independent reader according to spatial coordinates (x, y, z). Two panels of experienced thoracic radiologists from two different institutions independently established two reference standards (RS1, RS2) by identifying the true positive findings with spatial coordinates without using CAD. Sensitivity of the two CAD systems, defined by lesionlevel analysis, was tested and sensitivities compared.
RESULTS: RS1 identified 34 noncalcified pulmonary nodules, whereas RS2 identified 54. The total number of findings detected by the two CAD systems was 684. CAD1 correctly identified 13/34 nodules (sensitivity 38%) for RS1 and 17/54 (sensitivity 30%) for RS2, whereas CAD2 correctly identified 11/34 nodules (sensitivity 35%) for RS1 and 13/54 (sensitivity 23%) for RS2. Comparison between the two CAD systems did not show a statistically significant difference in terms of sensitivity (p<0.05) for both RS1 (p=0.42) and RS2 (p=0.33).
CONCLUSIONS: The two commercial CAD systems had similar sensitivity in detecting noncalcified pulmonary nodules on low-dose MDCT of the chest.

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Year:  2012        PMID: 22327922     DOI: 10.1007/s11547-012-0795-9

Source DB:  PubMed          Journal:  Radiol Med        ISSN: 0033-8362            Impact factor:   3.469


  29 in total

1.  Computer-assisted detection of pulmonary nodules: performance evaluation of an expert knowledge-based detection system in consensus reading with experienced and inexperienced chest radiologists.

Authors:  Katharina Marten; Tobias Seyfarth; Florian Auer; Edzard Wiener; Andreas Grillhösl; Silvia Obenauer; Ernst J Rummeny; Christoph Engelke
Journal:  Eur Radiol       Date:  2004-07-03       Impact factor: 5.315

2.  Observer studies involving detection and localization: modeling, analysis, and validation.

Authors:  Dev P Chakraborty; Kevin S Berbaum
Journal:  Med Phys       Date:  2004-08       Impact factor: 4.071

Review 3.  Current status and future potential of computer-aided diagnosis in medical imaging.

Authors:  K Doi
Journal:  Br J Radiol       Date:  2005       Impact factor: 3.039

4.  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.

Authors:  M Das; G Mühlenbruch; S Heinen; A H Mahnken; M Salganicoff; S Stanzel; R W Günther; J E Wildberger
Journal:  Br J Radiol       Date:  2008-11       Impact factor: 3.039

Review 5.  Small solitary pulmonary nodules.

Authors:  D F Yankelevitz; C I Henschke
Journal:  Radiol Clin North Am       Date:  2000-05       Impact factor: 2.303

6.  Early Lung Cancer Action Project: overall design and findings from baseline screening.

Authors:  C I Henschke; D I McCauley; D F Yankelevitz; D P Naidich; G McGuinness; O S Miettinen; D M Libby; M W Pasmantier; J Koizumi; N K Altorki; J P Smith
Journal:  Lancet       Date:  1999-07-10       Impact factor: 79.321

7.  Characteristics of small lung cancers invisible on conventional chest radiography and detected by population based screening using spiral CT.

Authors:  S Sone; F Li; Z G Yang; S Takashima; Y Maruyama; M Hasegawa; J C Wang; S Kawakami; T Honda
Journal:  Br J Radiol       Date:  2000-02       Impact factor: 3.039

8.  Lung cancer screening with CT: Mayo Clinic experience.

Authors:  Stephen J Swensen; James R Jett; Thomas E Hartman; David E Midthun; Jeff A Sloan; Anne-Marie Sykes; Gregory L Aughenbaugh; Medy A Clemens
Journal:  Radiology       Date:  2003-01-24       Impact factor: 11.105

Review 9.  Assessment methodologies and statistical issues for computer-aided diagnosis of lung nodules in computed tomography: contemporary research topics relevant to the lung image database consortium.

Authors:  Lori E Dodd; Robert F Wagner; Samuel G Armato; Michael F McNitt-Gray; Sergey Beiden; Heang-Ping Chan; David Gur; Geoffrey McLennan; Charles E Metz; Nicholas Petrick; Berkman Sahiner; Jim Sayre
Journal:  Acad Radiol       Date:  2004-04       Impact factor: 3.173

10.  Bias, underestimation of risk, and loss of statistical power in patient-level analyses of lesion detection.

Authors:  Nancy A Obuchowski; Peter J Mazzone; Abraham H Dachman
Journal:  Eur Radiol       Date:  2009-09-16       Impact factor: 5.315

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