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Radiomic Analysis of Multi-Parametric Mr Images (Mri) for Classification of Parotid Tumors Publisher



Fathi Kazerooni A1 ; Nabil M2 ; Alviri M3 ; Koopaei S3 ; Salahshour F4 ; Assili S3 ; Saligheh Rad H1, 5 ; Aghaghazvini L6
Authors
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Authors Affiliations
  1. 1. Quantitative MR maging and Spectroscopy Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Iran
  2. 2. Department of Mathematics, Islamic Azad University, Qazvin Branch, Qazvin, Iran
  3. 3. Quantitative MR Imaging and Spectroscopy Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Iran
  4. 4. Department of Radiology, Advanced Diagnostic and Invasive Radiology Research Center, Tehran University of Medical Sciences, Tehran, Iran
  5. 5. Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Iran
  6. 6. Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran

Source: Journal of Biomedical Physics and Engineering Published:2022


Abstract

Background: Characterization of parotid tumors before surgery using multiparametric magnetic resonance imaging (MRI) scans can support clinical decision making about the best-suited therapeutic strategy for each patient. Objective: This study aims to differentiate benign from malignant parotid tumors through radiomics analysis of multi-parametric MR images, incorporating T2-w images with ADC-map and parametric maps generated from Dynamic Contrast Enhanced MRI (DCE-MRI). Material and Methods: MRI scans of 31 patients with histopathologically-confirmed parotid gland tumors (23 benign, 8 malignant) were included in this retrospective study. For DCE-MRI, semi-quantitative analysis, Tofts pharmacoki-netic (PK) modeling, and five-parameter sigmoid modeling were performed and parametric maps were generated. For each patient, borders of the tumors were delineated on whole tumor slices of T2-w image, ADC-map, and the late-enhance-ment dynamic series of DCE-MRI, creating regions-of-interest (ROIs). Radiomic analysis was performed for the specified ROIs. Results: Among the DCE-MRI-derived parametric maps, wash-in rate (WIR) and PK-derived Ktrans parameters surpassed the accuracy of other parameters based on support vector machine (SVM) classifier. Radiomics analysis of ADC-map out-performed the T2-w and DCE-MRI techniques using the simpler classifier, sug-gestive of its inherently high sensitivity and specificity. Radiomics analysis of the combination of T2-w image, ADC-map, and DCE-MRI parametric maps resulted in accuracy of 100% with both classifiers with fewer numbers of selected texture features than individual images. Conclusion: In conclusion, radiomics analysis is a reliable quantitative approach for discrimination of parotid tumors and can be employed as a computer-aided approach for pre-operative diagnosis and treatment planning of the patients. © Journal of Biomedical Physics and Engineering.
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