Classification of solitary pulmonary nodules (SPNs) imaged on high-resolution CT using contrast enhancement and three-dimensional quantitative image features

摘要

Abstract: Spiral CT images were obtained of 21 SPN patients before and after the injection of an intravenous contrast agent. On pre- and post-injection images, nodules were isolated using a semi- automated contouring procedure; the resulting contours, as well as their internal pixels, were combined to form regions of interest (ROIs). These ROIs were then used to measure each nodule's CT attenuation, texture, volume and shape. Peak enhancement was calculated as the maximum difference between average gray levels in central areas of post-contrast and pre- contrast images for each nodule. Stepwise feature selection chose the best subset of discriminating measurements. A linear classifier was then trained and tested using chosen features. Using a commonly applied feature, peak enhancement, by itself, all malignant cases were classified correctly, but 6/12 benign cases were misclassified. Using peak contrast enhancement, a three-dimensional shape measure and two texture measures, 20/21 cases (95.2%) were classified correctly by resubstitution, and 17/21 (81.0%) by jackknifing. The combination of contrast enhancement, three dimensional shape features and texture features holds promise for accurate classification of solitary pulmonary nodules imaged on CT. !32