Literature DB >> 16632719

Computer-aided detection in screening CT for pulmonary nodules.

Ren Yuan1, Patrick M Vos, Peter L Cooperberg.   

Abstract

OBJECTIVE: Our objective was to evaluate the performance of a computer-aided detection (CAD) system for pulmonary nodule detection using low-dose screening CT images.
MATERIALS AND METHODS: One hundred fifty consecutive low-dose screening CT examinations were independently evaluated by a radiologist and a CAD pulmonary nodule detection system (R2 Technology) designed to identify nodules larger than 4 mm in maximum long-axis diameter. All discrepancies between the two techniques were reviewed by one of another two radiologists working in consensus with the initial interpreting radiologist, and a "true" nodule count was determined. Detected nodules were classified by size, density, and location. The performance of the initial radiologist and the CAD system were compared.
RESULTS: The radiologist detected 518 nodules and the CAD system, 934 nodules. Of the 1,106 separate nodules detected using the two techniques, 628 were classified as true nodules on consensus review. Of the true nodules present, the radiologist detected 518 (82%) of 628 nodules and the CAD, 456 (73%) of 628 nodules. All 518 radiologist-detected nodules were true nodules, and 456 (49%) of 934 of CAD-detected nodules were true nodules. The radiologist missed 110 true nodules that were only detected by CAD. In six patients, these were the only nodules detected in the examination, changing the imaging follow-up protocol. CAD identified 478 lesions that on consensus review were false-positive nodules, a rate of 3.19 (478/150) per patient.
CONCLUSION: CAD detected 72.6% of true nodules and detected nodules in six (4%) patients not identified by radiologists, changing the imaging follow-up protocol of these subjects. In this study, the combined review of low-dose CT scans by both the radiologist and CAD was necessary to identify all nodules.

Entities:  

Mesh:

Year:  2006        PMID: 16632719     DOI: 10.2214/AJR.04.1969

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  30 in total

1.  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 2.  Recent progress in computer-aided diagnosis of lung nodules on thin-section CT.

Authors:  Qiang Li
Journal:  Comput Med Imaging Graph       Date:  2007-03-21       Impact factor: 4.790

3.  Pulmonary nodule detection on MDCT images: evaluation of diagnostic performance using thin axial images, maximum intensity projections, and computer-assisted detection.

Authors:  A Jankowski; T Martinelli; J F Timsit; C Brambilla; F Thony; M Coulomb; G Ferretti
Journal:  Eur Radiol       Date:  2007-09-01       Impact factor: 5.315

Review 4.  Screening for lung cancer with low-dose computed tomography: a review of current status.

Authors:  Henry M Marshall; Rayleen V Bowman; Ian A Yang; Kwun M Fong; Christine D Berg
Journal:  J Thorac Dis       Date:  2013-10       Impact factor: 2.895

5.  Multi slice computed tomography in the study of pulmonary metastases.

Authors:  G Angelelli; V Grimaldi; F Spinelli; A Scardapane; A Sardaro
Journal:  Radiol Med       Date:  2008-09-08       Impact factor: 3.469

Review 6.  Computer-aided diagnosis of lung cancer and pulmonary embolism in computed tomography-a review.

Authors:  Heang-Ping Chan; Lubomir Hadjiiski; Chuan Zhou; Berkman Sahiner
Journal:  Acad Radiol       Date:  2008-05       Impact factor: 3.173

7.  Machine Learning in Computer-aided Diagnosis of the Thorax and Colon in CT: A Survey.

Authors:  Kenji Suzuki
Journal:  IEICE Trans Inf Syst       Date:  2013-04-01

8.  Computer-aided diagnosis systems for lung cancer: challenges and methodologies.

Authors:  Ayman El-Baz; Garth M Beache; Georgy Gimel'farb; Kenji Suzuki; Kazunori Okada; Ahmed Elnakib; Ahmed Soliman; Behnoush Abdollahi
Journal:  Int J Biomed Imaging       Date:  2013-01-29

Review 9.  Missed lung cancer: when, where, and why?

Authors:  Annemilia Del Ciello; Paola Franchi; Andrea Contegiacomo; Giuseppe Cicchetti; Lorenzo Bonomo; Anna Rita Larici
Journal:  Diagn Interv Radiol       Date:  2017 Mar-Apr       Impact factor: 2.630

10.  A review of computer-aided diagnosis in thoracic and colonic imaging.

Authors:  Kenji Suzuki
Journal:  Quant Imaging Med Surg       Date:  2012-09
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