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Proceedings Paper

Quantitative image feature variability amongst CT scanners with a controlled scan protocol
Author(s): Rachel B. Ger; Shouhao Zhou; Pai-Chun Melinda Chi; David L. Goff; Lifei Zhang; Hannah J. Lee; Clifton D. Fuller; Rebecca M. Howell; Heng Li; R. Jason Stafford; Laurence E. Court; Dennis S. Mackin
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Paper Abstract

Radiomics studies often analyze patient computed tomography (CT) images acquired from different CT scanners. This may result in differences in imaging parameters, e.g. different manufacturers, different acquisition protocols, etc. However, quantifiable differences in radiomics features can occur based on acquisition parameters. A controlled protocol may allow for minimization of these effects, thus allowing for larger patient cohorts from many different CT scanners. In order to test radiomics feature variability across different CT scanners a radiomics phantom was developed with six different cartridges encased in high density polystyrene. A harmonized protocol was developed to control for tube voltage, tube current, scan type, pitch, CTDIvol, convolution kernel, display field of view, and slice thickness across different manufacturers. The radiomics phantom was imaged on 18 scanners using the control protocol. A linear mixed effects model was created to assess the impact of inter-scanner variability with decomposition of feature variation between scanners and cartridge materials. The inter-scanner variability was compared to the residual variability (the unexplained variability) and to the inter-patient variability using two different patient cohorts. The patient cohorts consisted of 20 non-small cell lung cancer (NSCLC) and 30 head and neck squamous cell carcinoma (HNSCC) patients. The inter-scanner standard deviation was at least half of the residual standard deviation for 36 of 49 quantitative image features. The ratio of inter-scanner to patient coefficient of variation was above 0.2 for 22 and 28 of the 49 features for NSCLC and HNSCC patients, respectively. Inter-scanner variability was a significant factor compared to patient variation in this small study for many of the features. Further analysis with a larger cohort will allow more thorough analysis with additional variables in the model to truly isolate the interscanner difference.

Paper Details

Date Published: 27 February 2018
PDF: 7 pages
Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105751O (27 February 2018); doi: 10.1117/12.2293701
Show Author Affiliations
Rachel B. Ger, The Univ. of Texas M.D. Anderson Cancer Ctr. (United States)
Shouhao Zhou, The Univ. of Texas M.D. Anderson Cancer Ctr. (United States)
Pai-Chun Melinda Chi, The Univ. of Texas M.D. Anderson Cancer Ctr. (United States)
David L. Goff, Medical and Radiation Physics, Inc. (United States)
Lifei Zhang, The Univ. of Texas M.D. Anderson Cancer Ctr. (United States)
Hannah J. Lee, The Univ. of Texas M.D. Anderson Cancer Ctr. (United States)
Clifton D. Fuller, The Univ. of Texas M.D. Anderson Cancer Ctr. (United States)
Rebecca M. Howell, The Univ. of Texas M.D. Anderson Cancer Ctr. (United States)
Heng Li, The Univ. of Texas M.D. Anderson Cancer Ctr. (United States)
R. Jason Stafford, The Univ. of Texas M.D. Anderson Cancer Ctr. (United States)
Laurence E. Court, The Univ. of Texas M.D. Anderson Cancer Ctr. (United States)
Dennis S. Mackin, The Univ. of Texas M.D. Anderson Cancer Ctr. (United States)


Published in SPIE Proceedings Vol. 10575:
Medical Imaging 2018: Computer-Aided Diagnosis
Nicholas Petrick; Kensaku Mori, Editor(s)

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