Measuring Computed Tomography Scanner Variability of Radiomics Features. Issue 11 (November 2015)

• Record Type: Journal Article
• Authors: Mackin, Dennis; Fave, Xenia; Zhang, Lifei; Fried, David; Yang, Jinzhong; Taylor, Brian; Rodriguez-Rivera, Edgardo; Dodge, Cristina; Jones, Aaron Kyle; Court, Laurence
• Is Part Of: Investigative radiology. Volume 50:Issue 11(2015:Nov.)
• Publication Date: 2015-11
• Subjects: radiomics -- image texture -- image features -- CT -- computed tomography -- phantom; Diagnosis, Radioscopic -- Periodicals; Radiology, Medical -- Periodicals; 616.0757
• DOI: 10.1097/RLI.0000000000000180
• Languages: English
• ISSNs: 0020-9996

Abstract

Abstract : Objectives: The purpose of this study was to determine the significance of interscanner variability in CT image radiomics studies. Materials and Methods: We compared the radiomics features calculated for non–small cell lung cancer (NSCLC) tumors from 20 patients with those calculated for 17 scans of a specially designed radiomics phantom. The phantom comprised 10 cartridges, each filled with different materials to produce a wide range of radiomics feature values. The scans were acquired using General Electric, Philips, Siemens, and Toshiba scanners from 4 medical centers using their routine thoracic imaging protocol. The radiomics feature studied included the mean and standard deviations of the CT numbers as well as textures derived from the neighborhood gray-tone difference matrix. To quantify the significance of the interscanner variability, we introduced the metric feature noise. To look for patterns in the scans, we performed hierarchical clustering for each cartridge. Results: The mean CT numbers for the 17 CT scans of the phantom cartridges spanned from −864 to 652 Hounsfield units compared with a span of −186 to 35 Hounsfield units for the CT scans of the NSCLC tumors, showing that the phantom's dynamic range includes that of the tumors. The interscanner variability of the feature values depended on both the cartridge material and the feature, and the variability was large relative to the interpatient variability in the NSCLC tumors for some features. The feature interscanner noise was greatest for busyness and least for texture strength. Hierarchical clustering produced different clusters of the phantom scans for each cartridge, although there was some consistent clustering by scanner manufacturer. Conclusions: The variability in the values of radiomics features calculated on CT images from different CT scanners can be comparable to the variability in these features found in CT images of NSCLC tumors. These interscanner differences should be considered, and their effects should be minimized in future radiomics studies.