Universität zu Köln

Bernatz, Simon, Zhdanovich, Yauheniya, Ackermann, Jorg, Koch, Ina ORCID: 0000-0002-3621-003X, Wild, Peter J., dos Santos, Daniel Pinto, Vogl, Thomas J., Kaltenbach, Benjamin and Rosbach, Nicolas (2021). Impact of rescanning and repositioning on radiomic features employing a multi-object phantom in magnetic resonance imaging. Sci Rep, 11 (1). BERLIN: NATURE RESEARCH. ISSN 2045-2322

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Abstract

Our purpose was to analyze the robustness and reproducibility of magnetic resonance imaging (MRI) radiomic features. We constructed a multi-object fruit phantom to perform MRI acquisition as scan-rescan using a 3 Tesla MRI scanner. We applied T2-weighted (T2w) half-Fourier acquisition single-shot turbo spin-echo (HASTE), T2w turbo spin-echo (TSE), T2w fluid-attenuated inversion recovery (FLAIR), T2 map and T1-weighted (T1w) TSE. Images were resampled to isotropic voxels. Fruits were segmented. The workflow was repeated by a second reader and the first reader after a pause of one month. We applied PyRadiomics to extract 107 radiomic features per fruit and sequence from seven feature classes. We calculated concordance correlation coefficients (CCC) and dynamic range (DR) to obtain measurements of feature robustness. Intraclass correlation coefficient (ICC) was calculated to assess intra- and inter-observer reproducibility. We calculated Gini scores to test the pairwise discriminative power specific for the features and MRI sequences. We depict Bland Altmann plots of features with top discriminative power (Mann-Whitney U test). Shape features were the most robust feature class. T2 map was the most robust imaging technique (robust features (rf), n=84). HASTE sequence led to the least amount of rf (n=20). Intra-observer ICC was excellent (>= 0.75) for nearly all features (max-min; 99.1-97.2%). Deterioration of ICC values was seen in the inter-observer analyses (max-min; 88.7-81.1%). Complete robustness across all sequences was found for 8 features. Shape features and T2 map yielded the highest pairwise discriminative performance. Radiomics validity depends on the MRI sequence and feature class. T2 map seems to be the most promising imaging technique with the highest feature robustness, high intra-/inter-observer reproducibility and most promising discriminative power.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Bernatz, Simon UNSPECIFIED UNSPECIFIED UNSPECIFIED Zhdanovich, Yauheniya UNSPECIFIED UNSPECIFIED UNSPECIFIED Ackermann, Jorg UNSPECIFIED UNSPECIFIED UNSPECIFIED Koch, Ina UNSPECIFIED orcid.org/0000-0002-3621-003X UNSPECIFIED Wild, Peter J. UNSPECIFIED UNSPECIFIED UNSPECIFIED dos Santos, Daniel Pinto UNSPECIFIED UNSPECIFIED UNSPECIFIED Vogl, Thomas J. UNSPECIFIED UNSPECIFIED UNSPECIFIED Kaltenbach, Benjamin UNSPECIFIED UNSPECIFIED UNSPECIFIED Rosbach, Nicolas UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-586792
DOI:	10.1038/s41598-021-93756-x
Journal or Publication Title:	Sci Rep
Volume:	11
Number:	1
Date:	2021
Publisher:	NATURE RESEARCH
Place of Publication:	BERLIN
ISSN:	2045-2322
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language TEXTURE ANALYSIS; CORRELATION-COEFFICIENT; FEATURE TRACKING; REPRODUCIBILITY; IMAGES; DISCRIMINATION; MULTICENTER; PROTOCOLS; STRAIN Multiple languages Multidisciplinary Sciences Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/58679