Computer-aided diagnosis for distinguishing benign perifissural opacities from other pulmonary nodules in computed tomography chest scans

B. van Ginneken, M. Klik, E. van Rikxoort, H. Gietema, J. Peters and M. Prokop

Annual Meeting of the Radiological Society of North America 2006:598.

Purpose: Around a third of pulmonary nodules detected with CT lung cancer screening programs are perifissural opacities (PFOs). These nodules are flattened and attached to a pulmonary fissure and represent benign findings. A computer-aided diagnosis system is presented that automatically determines if a nodule is a PFO. Method: Data was obtained from a lung cancer screening program with low dose multidetector CT (Philips Mx8000IDT, 16 x 0.75 mm collimation, 30 mAs). Only nodules with a volume between 50 and 500 mm3 were considered. Scans from 221 patients containing at least one such nodule were randomly selected, resulting in a set of 284 nodules. A radiologist determined which of these findings represented PFOs. Around each nodule a volume of interest (VOI) of 60 x 60 x 60 mm was extracted. An automatic algorithm segmented all nodules. Another automatic algorithm detected voxels belonging to fissures in the VOI, using a plate detector based on density values and the directions of principal curvature and a grouping algorithm to remove false isolated responses. Hough transforms were applied to the fissure voxels and the nodule boundary voxels to determine if the nodule had a flattened side that coincided with an attached fissure. From this analysis a number of numerical features were extracted. In addition, a number of features describing the shape of the nodule were computed. A Parzen density classifier was used to infer the probability that a nodule was a PFO from these features. Cross validation was used to train and test the system. Results: From the 284 nodules, 99 (35%) were identified as PFOs by the radiologist. The system obtained an area under the ROC curve of 0.80. It could detect 40% of all PFOs without any false positive finding and 65% at 95% specificity. PFOs that were not well detected were usually attached to fissures that were barely visible in the low dose data. Conclusion: Computer-aided diagnosis can be used to identify a large amount of benign nodules from chest CT data. This can be used in the work-up of patients with pulmonary nodules and may prevent unnecessary repeat examinations.