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      Measuring CT scanner variability of radiomics features

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          The purpose of this study was to determine the significance of inter-scanner 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 four 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 inter-scanner variability, we introduced the metric feature noise. To look for patterns in the scans, we performed hierarchical clustering for each cartridge.


          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 inter-scanner variability of the feature values depended on both the cartridge material and the feature, and the variability was large relative to the inter-patient variability in the NSCLC tumors for some features. The feature inter-scanner 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.


          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 inter-scanner differences should be considered, and their effects should be minimized in future radiomics studies.

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          Author and article information

          Invest Radiol
          Invest Radiol
          Investigative radiology
          14 June 2015
          November 2015
          01 November 2016
          : 50
          : 11
          : 757-765
          [1 ]Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030
          [2 ]Graduate School of Biomedical Sciences, The University of Texas Health Science Center at Houston, Houston, TX 77030
          [3 ]Research Service Line and Diagnostic & Therapeutic Care Line, Michael E. DeBakey VA Medical Center, Houston, TX 77030
          [4 ]Department of Radiology, Baylor College of Medicine, Houston, TX 77030
          [5 ]Radiation Oncology Department, Houston Methodist Hospital, Houston, TX 77030
          [6 ]Department of Diagnostic Imaging, Texas Children’s Hospital, Houston, TX 77030
          [7 ]Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030
          Author notes
          Corresponding author address: Dennis Mackin, Dept. Radiation Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030-4017, Phone: 713-745-1642, Fax: 713-563-2545
          PMC4598251 PMC4598251 4598251 nihpa687665


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