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Fathi Kazerooni, Anahita, Nabil, Mahnaz, Alviri, Mohammadreza, Koopaei, Soheila, Salahshour, Faezeh, Assili, Sanam, Saligheh Rad, Hamidreza, Aghaghazvini, Leila. (1401). Radiomic Analysis of Multi-parametric MR Images (MRI) for Classification of Parotid Tumors. سامانه مدیریت نشریات علمی, 12(6), 599-610. doi: 10.31661/jbpe.v0i0.2007-1140
Anahita Fathi Kazerooni; Mahnaz Nabil; Mohammadreza Alviri; Soheila Koopaei; Faezeh Salahshour; Sanam Assili; Hamidreza Saligheh Rad; Leila Aghaghazvini. "Radiomic Analysis of Multi-parametric MR Images (MRI) for Classification of Parotid Tumors". سامانه مدیریت نشریات علمی, 12, 6, 1401, 599-610. doi: 10.31661/jbpe.v0i0.2007-1140
Fathi Kazerooni, Anahita, Nabil, Mahnaz, Alviri, Mohammadreza, Koopaei, Soheila, Salahshour, Faezeh, Assili, Sanam, Saligheh Rad, Hamidreza, Aghaghazvini, Leila. (1401). 'Radiomic Analysis of Multi-parametric MR Images (MRI) for Classification of Parotid Tumors', سامانه مدیریت نشریات علمی, 12(6), pp. 599-610. doi: 10.31661/jbpe.v0i0.2007-1140
Fathi Kazerooni, Anahita, Nabil, Mahnaz, Alviri, Mohammadreza, Koopaei, Soheila, Salahshour, Faezeh, Assili, Sanam, Saligheh Rad, Hamidreza, Aghaghazvini, Leila. Radiomic Analysis of Multi-parametric MR Images (MRI) for Classification of Parotid Tumors. سامانه مدیریت نشریات علمی, 1401; 12(6): 599-610. doi: 10.31661/jbpe.v0i0.2007-1140

Radiomic Analysis of Multi-parametric MR Images (MRI) for Classification of Parotid Tumors

Journal of Biomedical Physics and Engineering
دوره 12، شماره 6، اسفند 2022، صفحه 599-610    اصل مقاله (882.65 K)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.2007-1140
نویسندگان
Anahita Fathi Kazerooni1؛ Mahnaz Nabil2؛ Mohammadreza Alviri3؛ Soheila Koopaei3؛ Faezeh Salahshour4؛ Sanam Assili3؛ Hamidreza Saligheh Rad1، 5؛ Leila Aghaghazvini* 6
1PhD, Quantitative MR Imaging and Spectroscopy Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Iran
2PhD, Department of Mathematics, Islamic Azad University, Qazvin Branch, Qazvin, Iran
3MSc, Quantitative MR Imaging and Spectroscopy Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Iran
4MD, Department of Radiology, Advanced Diagnostic and Invasive Radiology Research Center, Tehran University of Medical Sciences, Tehran, Iran
5PhD, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Iran
6MD, Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
چکیده
Background: Characterization of parotid tumors before surgery using multi-parametric 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 pharmacokinetic (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-enhancement 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 outperformed the T2-w and DCE-MRI techniques using the simpler classifier, suggestive 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. 
کلیدواژه‌ها
Parotid Neoplasms؛ Radiomics؛ Texture Analysis؛ Magnetic Resonance Imaging؛ Machine Learning؛ Diagnosis
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