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Consistency and duplicability in Computed Tomography (CT) output is essentialto quantitative imaging for lung cancer detection and monitoring. This study ofCT-detected lung nodules investigated the reproducibility of volume-, density-,and texture-based features (outcome variables) over routine ranges ofradiation-dose, reconstruction kernel, and slice thickness. CT raw data of 23nodules were reconstructed using 320 acquisition/reconstruction conditions(combinations of 4 doses, 10 kernels, and 8 thicknesses). Scans at 12.5%, 25%,and 50% of protocol dose were simulated; reduced-dose and full-dose data werereconstructed using conventional filtered back-projection anditerative-reconstruction kernels at a range of thicknesses (0.6-5.0 mm).Full-dose/B50f kernel reconstructions underwent expert segmentation forreference Region-Of-Interest (ROI) and nodule volume per thickness; each ROIwas applied to 40 corresponding images (combinations of 4 doses and 10kernels). Typical texture analysis metrics (including 5 histogram features, 13Gray Level Co-occurrence Matrix, 5 Run Length Matrix, 2 Neighboring Gray-LevelDependence Matrix, and 2 Neighborhood Gray-Tone Difference Matrix) werecomputed per ROI. Reconstruction conditions resulting in no significant changein volume, density, or texture metrics were identified as "compatible pairs"for a given outcome variable. Our results indicate that as thickness increases,volumetric reproducibility decreases, while reproducibility of histogram- andtexture-based features across different acquisition and reconstructionparameters improves. In order to achieve concomitant reproducibility ofvolumetric and radiomic results across studies, balanced standardization of theimaging acquisition parameters is required.

Are Quantitative Features of Lung Nodules Reproducible at Different CT Acquisition and Reconstruction Parameters?