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.
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 κ statistics.
Pairwise agreement for the differentiation between solid, part-solid, and non-solid nodules between CAD and each of the human experts had a κ range between 0.54 and 0.72. The interobserver agreement among the human experts was in the same range (κ range, 0.56–0.81).
A novel automated classification tool for pulmonary nodules achieved good agreement with the human experts, yielding κ values in the same range as the interobserver agreement. Computer-aided diagnosis may aid radiologists in selecting the appropriate workup for pulmonary nodules.
From the *Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands; †Fraunhofer MEVIS, Bremen, Germany; ‡Department of Radiology, Kennemer Gasthuis, Haarlem; §Department of Radiology, University Medical Center Utrecht, Utrecht; and ∥Department of Radiology, Meander Medical Center, Amersfoort, the Netherlands.
Received for publication May 28, 2014; and accepted for publication, after revision, October 15, 2014.
Conflicts of interest and sources of funding: Supported by a research grant from MeVis Medical Solutions AG, Bremen, Germany.
The authors report no conflicts of interest.
Reprints: Colin Jacobs, MSc, Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein-Zuid 10, 6525 GA, Nijmegen, the Netherlands. E-mail: colin.jacobs@radboudumc.nl.
