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

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.