Reliability of 2D Magnetic Resonance Imaging Texture Analysis in Cerebral Gliomas: Influence of Slice Selection Bias on Reproducibility of Radiomic Features

Introduction: In this study, we aimed to investigate the reproducibility of two-dimensional (2D) texture features between adjacent magnetic resonance imaging (MRI) slices in patients with cerebral gliomas. Methods: For this retrospective methodological study, T2-weighted MRI and semi-automatic segmentation data of 25 patients with lower-grade gliomas were obtained from a public database. Only two regions of interests were used in this study: (i), the largest slice and (ii) one of the adjacent slices. Using PyRadiomics, an open source software to extract radiomic features from medical images, a total of 1116 texture features from six different feature classes were extracted from original, Laplacian of Gaussian-filtered, and wavelet-transformed images. Intra-class correlation coefficient (ICC) values with and without 95% confidence interval (CI) were used for reliability analysis. The ICC threshold for excellent reproducibility was 0.9. Results: In the reliability analysis without considering the 95% CI for the ICC values, 28% of the texture features had excellent reproducibility. On the other hand, considering the 95% CI, only 10% of the texture features had excellent reproducibility. Neither a feature class (range of excellent reproducibility rates without 95% CI, 21.2%-34.4%; with 95% CI, 2.1%-18.3%) nor an image type (range of excellent reproducibility rates without 95% CI, 22.3%-41.9%; with 95% CI, 9.1%-14%) had considerable reliability in two adjacent MRI slices. Conclusion: 2D MRI texture analysis of gliomas using T2- weighted sequence is substantially sensitive to slice selection bias, which may lead to non-reproducible results in radiomic works.