Test-Retest Reproducibility and Robustness Analysis of Recurrent Glioblastoma MRI Radiomics Texture Features

Abstract

Background: Advanced quantitative information such as radiomics features derived from magnetic resonance (MR) image may be useful for outcome prediction, prognostic models or response biomarkers in Glioblastoma (GBM), knowledge about their Repro-ducibility and robustness is essential. The purpose of this study was to investigate the reproducible and non-redundant radiomics texture features in oncological GBM MRI based on test-retest setup. Methods: Thirteen patients with recurrent GBM who underwent repeated MR imaging approximately two day interval were sub-jected to this study. Lesions were segmented using competitive region-growing based algorithm. Following delineation and seg-mentation of lesions, 158 quantitative 3D features based on intensity histograms (IH), gray level run-length (GLRLM), gray level co-occurrence (GLCM), gray level size-zone texture matrices (GLSZM), neighborhood-difference matrices (NDM), and geometric features were extracted from the 3D-tumor volumes of each lesion. For every radiomics feature, test-retest was assessed with the intra-class correlation coefficient (ICC) and the concordance correlation coefficient (CCC) and finally the most reproducible and robust ra-diomics features were selected. Results: Results shows that the '74%' of assessed radiomics features had a high test-retest stability in terms of their ICC. There were