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Variability in radiomics features among iDose4 reconstruction levels
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Paper Abstract

The assessment of tissues depicted in medical images using radiomics has been shown to depend on a number of imageacquisition and reconstruction parameters. This study assessed the variability in radiomics features due to variations in iDose4 reconstruction level. A database of 109 normal head and neck (HN) computed tomography (CT) scans was obtained for analysis with three levels of iDose4 reconstruction: 2, 4, and 6. Various two-dimensional regions of interest (ROIs) containing different tissues were manually contoured including the globes, sternocleidomastoid muscle (SCM), thyroid, clivus, and supraclavicular subcutaneous fat. A square ROI containing only air was also contoured. Each region was contoured at its largest axial cross section by area. Pixel information was extracted from each region in each patient for each iDose4 reconstruction, and 142 texture features were calculated using an in-house texture package. Differences in radiomics features between iDose4 levels were assessed using parametric paired Student’s t-tests or non-parametric Wilcoxon signed-rank tests for each tissue type after assessing normality using the Shapiro-Wilk test. Relative agreement among iDose4 reconstructions was quantified using the Spearman’s rank correlation coefficient. For all ROIs besides those containing only air, most features differed significantly between pairwise combinations of the three iDose4 levels. For air, all features were robust to differences in iDose4 levels. Therefore, if radiomics studies include images reconstructed using different iDose4 levels, robust radiomics features should be used. Additionally, to aid in the validation of radiomics research, iDose4 reconstruction levels should be reported.

Paper Details

Date Published: 13 March 2019
PDF: 6 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109501M (13 March 2019); doi: 10.1117/12.2512634
Show Author Affiliations
Joseph J. Foy, The Univ. of Chicago (United States)
Mena Shenouda, Univ. of Michigan (United States)
Sahar Ramahi, Univ. of Illinois (United States)
Samuel G. Armato, The Univ. of Chicago (United States)
Daniel Ginat, The Univ. of Chicago (United States)


Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

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