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  4. Solid, part-solid, or non-solid?: Classification of pulmonary nodules in low-dose chest computed tomography by a computer-aided diagnosis system
 
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2015
Journal Article
Title

Solid, part-solid, or non-solid?: Classification of pulmonary nodules in low-dose chest computed tomography by a computer-aided diagnosis system

Abstract
Objectives: The purpose of this study was to develop and validate a computer-aided diagnosis (CAD) tool for automatic classification of pulmonary nodules seen on low-dose computed tomography into solid, part-solid, and non-solid. Materials and Methods: Study lesions were randomly selected from 2 sites participating in the Dutch-Belgian NELSON lung cancer screening trial. On the basis of the annotations made by the screening radiologists, 50 part-solid and 50 non-solid pulmonary nodules with a diameter between 5 and 30 mm were randomly selected from the 2 sites. For each unique nodule, 1 low-dose chest computed tomographic scan was randomly selected, in which the nodule was visible. In addition, 50 solid nodules in the same size range were randomly selected. A completely automatic 3-dimensional segmentation-based classification system was developed, which analyzes the pulmonary nodule, extracting intensity-, texture-, and segmentation-based features to perform a statistical classification. In addition to the nodule classification by the screening radiologists, an independent rating of all nodules by 3 experienced thoracic radiologists was performed. Performance of CAD was evaluated by comparing the agreement between CAD and human experts and among human experts using the Cohen k statistics. Results: Pairwise agreement for the differentiation between solid, part-solid, and non-solid nodules between CAD and each of the human experts had a k range between 0.54 and 0.72. The interobserver agreement among the human experts was in the same range (k range, 0.56-0.81). Conclusions: A novel automated classification tool for pulmonary nodules achieved good agreement with the human experts, yielding k values in the same range as the interobserver agreement. Computer-aided diagnosis may aid radiologists in selecting the appropriate workup for pulmonary nodules.
Author(s)
Jacobs, C.
Rikxoort, E.M. van
Scholten, E.T.
Jong, P.A. de
Prokop, M.
Schaefer-Prokop, C.
Ginneken, B. van
Journal
Investigative radiology  
Open Access
DOI
10.1097/RLI.0000000000000121
Additional full text version
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Language
English
Fraunhofer-Institut für Digitale Medizin MEVIS  
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