Computed tomography radiomic texture features dependence upon imaging parameters

Introduction and Aim: Few studies have been carried out to determine the influence of Computed Tomography (CT) acquisition parameters (slice thickness, tube potential difference (kVp), and tube current time product (mAs)) on the quantitative image features in radiomics studies. There is little evidence in the published literature, of studies that use mathematics to establish radiomic texture features that are independent of the CT scan technique parameters. The stability of radiomic texture features may have a great impact on the diagnosis and treatment of cancers. Robust texture features can be used to track radiotherapy treatment response. In this study radiomic texture features were investigated to identify features that did not depend on the CT technique parameters. Methodology: The credence cartridge radiomics (CCR) phantom was imaged at four CT units at the Universitas Academic and the National District hospitals. The tube current-time product (mAs) was varied from 75 to 400 mAs in steps of 25mAs while the kilovoltage peak and slice thickness were kept set at 120kVp and 5 mm respectively. The CT tube potential was investigated at 80, 100, 120 and 135 kVp whilst mAs and slice thickness was kept set at 300 mAs and 5 mm respectively. The slice thickness was varied from 1 mm to 5 mm whilst the mAs and kVp was kept constant at 300 mAs and 120kVp respectively. The acquisition field of view (FOV) and pitch were kept constant. The images obtained were processed using PyRadiomics software platform of 3D Slicer and the Matlab 2017a package. PyRadiomics was used to segment and extract a total of 105 radiomics texture features for each region of interest (ROI) delineated on an image. The 105 radiomic features included 13 shape features, 18 first order statistics features, 23 grey-level co-occurrence matrix, 14 grey level difference matrix, 16 grey-level run length matrix, 16 grey level size zone matrix and 5 neighbourhood grey tone difference matrix features. For each 10