Document Type : Original Paper
Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
Department of Radiation Oncology, Isfahan Milad Hospital, Isfahan, Iran
Department of Radiotherapy Oncology, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
Department of Radiologic Sciences and Medical Physics, Faculty of Allied Medicine, Kerman University of Medical Sciences, Kerman, Iran
Department of Genetics and Molecular Biology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
Introduction: Radiomic features robustness analysis is a critical issue before clinical decision making. In this study, the reproducibility and robustness of radiomic features in computed tomography (CT) and magnetic resonance (MR) images of glioblastoma cancer patients were analyzed regarding inter-scanner and inter-modality variations.
Material and Methods: CT and MR Images of eighteen glioblastoma cancer patients were used to extract the radiomic features following image segmentation. Coefficient of variation (COV), intraclass correlation coefficient (ICC), and concordance correlation coefficient (CCC) analysis were done to select the most robust features in all paired combinations of CT and MR images include T1-T2, T1-FLAIR, T1-ADC, T1-CT, T2-FLAIR, T2-ADC, T2-CT, FLAIR-ADC, FLAIR-CT, and ADC-CT.
Results: The features with COV ≤ 5% or ICC ≥ 90% or CCC ≥ 90%, considered as the most robust features, include the shape features, Minimum (belong to first-order Features), IMC1, IDN, IDMN (belong to GLCM), and Run Length Non-Uniformity (belongs to Gray Level Run Length Matrix).
Conclusion: In this study we presented a large image feature variation among different imaging modalities including CT and MRI. Our results identified several robust features that could be used for further clinical analysis.