Influence of image preprocessing on the segmentation-based reproducibility of radiomic features: <i>in vivo</i> experiments on discretization and resampling parameters
Burak Koçak, Sabahattin Yüzkan, Samet Mutlu, Mehmet Karagülle, Ahmet Kala, Mehmet Kadıoğlu, Sıla Solak, Şeyma Sunman, Zişan Hayriye Temiz, Ali Kürşad Ganiyusufoğlu
Abstract
PURPOSE: To systematically investigate the impact of image preprocessing parameters on the segmentation-based reproducibility of magnetic resonance imaging (MRI) radiomic features. METHODS: The MRI scans of 50 patients were included from the multi-institutional Brain Tumor Segmentation 2021 public glioma dataset. Whole tumor volumes were manually segmented by two independent readers, with the participation of eight readers. Radiomic features were extracted from two sequences: T2-weighted (T2) and contrast-enhanced T1-weighted (T1ce). Two methods were considered for discretization: bin count (i.e., relative discretization) and bin width (i.e., absolute discretization). Ten discretization (five for each method) and five resampling parameters were varied while other parameters were fixed. The intraclass correlation coefficient (ICC) was used for reliability analysis based on two commonly used cut-off values (0.75 and 0.90). RESULTS: ). CONCLUSION: The segmentation-based reproducibility of radiomic features appears to be substantially influenced by discretization and resampling parameters. Our findings indicate that the bin width method should be used for discretization and lower bin width and higher resampling values should be used to allow more reproducible features.