A Comprehensive Evaluation of Radiomic Features in Normal Brain Magnetic Resonance Imaging: Investigating Robustness and Region Variations.
Mahsa Shakeri, Ahmad Mostaar, Arash Zare Sadeghi, Seyyed Mohammad Hosseini, Ali Yaghobi Joybari, Hossein Ghadiri
Author Information
Mahsa Shakeri: Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
Ahmad Mostaar: Department of Medical Physics and Biomedical Engineering, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Arash Zare Sadeghi: Medical Physics Department, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
Seyyed Mohammad Hosseini: Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
Ali Yaghobi Joybari: Department of Radiation Oncology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Hossein Ghadiri: Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
Background: Despite extensive research on various brain diseases, a few studies have focused on radiomic feature distribution in healthy brain images. The present study applied a novel radiomic framework to investigate the robustness and baseline values of radiomic features in normal brain magnetic resonance imaging (MRIs) regions. Materials and Methods: Analyses were performed on T1 and T2 images including 276 normal brains and 14 healthy volunteers were scanned with three scanners using the same protocols. The images were divided into 1024 three-dimensional nonoverlap patches with the same pixel size. Seven patches located in the thalamus, putamen, hippocampus and brain stem were selected as volume of interest (VOI). Eighty-five radiomic features were generated. To investigate the variation of features across VOIs, the analysis of variance was performed and coefficient of variation (COV) and intraclass correlation coefficient (ICC) were explored to examine the features repeatability. Results: Thalamus (right and left) and hippocampus (left) resulted in more stable features (COV ��� 6%) in T1 and T2 images, respectively. The inter-scanner ICC analysis demonstrated the features of T2 sequences represented more repeatable results and the brain stem and thalamus (both T1 and T2) showed particularly high repeatability (higher ICC values). Robust results (ICC ��� 0.9) were identified for energy and range features of the first order class and several textures features across different brain regions. Conclusion: Our results indicated the baselines of the repeatable texture features in healthy brain structural MRI highlighting inter-scanner stability. According to the findings, MRI sequencing and VOI location impact feature robustness and should be considered in brain radiomic studies.
