|تعداد مشاهده مقاله||12,795,178|
|تعداد دریافت فایل اصل مقاله||6,126,856|
Simplifying Radiomics Workflow for Predicting Grade of Glioma: An Approach for Rapid and Reproducible Radiomics
|Journal of Biomedical Physics and Engineering|
|مقالات آماده انتشار، اصلاح شده برای چاپ، انتشار آنلاین از تاریخ 14 تیر 1402 اصل مقاله (1.31 M)|
|نوع مقاله: Original Research|
|شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.2208-1525|
|Yunus Soleymani1، 2؛ Peyman Sheikhzadeh3؛ Mohammad Mohammadzadeh4؛ Davood Khezerloo* 2، 5|
|1Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran|
|2Department of Radiology, Faculty of Alliance Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran|
|3Department of Nuclear Medicine, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran|
|4Department of Radiology and Radiotherapy, School of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran|
|5Medical Radiation Sciences Research Group, Tabriz University of Medical Sciences, Tabriz, Iran|
|Background: Radiomics with single Region of Interest (ROI) and single-sequence Magnetic Resonance Imaging (MRI) may facilitate the segmentation reproducibility and radiomics workflow due to a time-consuming and complicated delineation of that in multi-sequence MRI images. |
Objective: This study aimed to evaluate the performance of the radiomics
approach in grading glioma based on a single-ROI delineation as Gross Tumor Volume (GTV) in a single – sequence as contrast-enhanced T1-weighted MRI.
Material and Methods: This retrospective study was conducted on contrast-enhanced T1 weighted (CE T1W) MRI images of 60 grade II and 60 grade III glioma patients. The GTV regions were manually delineated. Radiomics features were
extracted per patient. The segmentation reproducibility of the robust features was evaluated in several repetitions of GTV delineation. Finally, a linear Support Vector Machine (SVM) assessed the classification performance of the robust features.
Results: Four significant robust features were selected for training the model
(P-value<0.05). The average Intraclass Correlation Coefficient (ICC) of the four features was 0.96 in several repetitions of GTV delineation. The linear SVM model differentiated grades II and III of glioma with an Area Under the Curve (AUC) of 0.9 in the training group.
Conclusion: High predicting power for glioma grading can be achieved with radiomics analysis by a single-ROI delineated on a single-sequence MRI image (CE T1W). In addition, single-ROI segmentation can increase radiomics reproducibility.
|Radiomics؛ Glioma؛ Magnetic Resonance Imaging؛ Machine Learning؛ Radiomics Reproducibility|
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