Program Information
Interscanner Variability of Radiomics Features in Computed Tomography (CT) Using a Standard ACR Phantom
M Shafiq ul Hassan1,2*, M Budzevich1 , G Zhang1,2 , K Latifi1 , D Hunt1 , R Gillies1 , E Moros1,2* , (1) H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, (2) Department of Physics, University of South Florida, Tampa, FL
Presentations
SU-F-R-30 (Sunday, July 31, 2016) 3:00 PM - 6:00 PM Room: Exhibit Hall
Purpose:A simple approach to investigate Interscanner variability of Radiomics features in computed tomography (CT) using a standard ACR phantom.
Methods:The standard ACR phantom was scanned on CT scanners from three different manufacturers. Scanning parameters of 120 KVp, 200 mA were used while slice thickness of 3.0 mm on two scanners and 3.27 mm on third scanner was used. Three spherical regions of interest (ROI) from water, medium density and high density inserts were contoured. Ninety four Radiomics features were extracted using an in-house program. These features include shape (11), intensity (22), GLCM (26), GLZSM (11), RLM (11), and NGTDM (5) and 8 fractal dimensions features. To evaluate the Interscanner variability across three scanners, a coefficient of variation (COV) is calculated for each feature group. Each group is further classified according to the COV- by calculating the percentage of features in each of the following categories: COV less than 2%, between 2 and 10% and greater than 10%.
Results:For all feature groups, similar trend was observed for three different inserts. Shape features were the most robust for all scanners as expected. 70% of the shape features had COV <2%. For intensity feature group, 2% COV varied from 9 to 32% for three scanners. All features in four groups GLCM, GLZSM, RLM and NGTDM were found to have Interscanner variability ≥2%. The fractal dimensions dependence for medium and high density inserts were similar while it was different for water inserts.
Conclusion:We concluded that even for similar scanning conditions, Interscanner variability across different scanners was significant. The texture features based on GLCM, GLZSM, RLM and NGTDM are highly scanner dependent. Since the inserts of the ACR Phantom are not heterogeneous in HU values suggests that matrix based 2nd order features are highly affected by variation in noise.
Funding Support, Disclosures, and Conflict of Interest: Research partly funded by NIH/NCI R01CA190105-01
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