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

Anahita Fathi Kazerooni; Mahnaz Nabil; Mohammadreza Alviri; Soheila Koopaei; Faeze Salahshour; Sanam Assili; Hamidreza Saligheh Rad; Leila Aghaghazvini

doi:10.31661/jbpe.v0i0.2007-1140

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

J Biomed Phys Eng. 2022 Dec 1;12(6):599-610. doi: 10.31661/jbpe.v0i0.2007-1140. eCollection 2022 Dec.

Authors

Anahita Fathi Kazerooni¹, Mahnaz Nabil², Mohammadreza Alviri³, Soheila Koopaei³, Faeze Salahshour⁴, Sanam Assili³, Hamidreza Saligheh Rad^{1

5}, Leila Aghaghazvini⁶

Affiliations

¹ PhD, Quantitative MR Imaging and Spectroscopy Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Iran.
² PhD, Department of Mathematics, Islamic Azad University, Qazvin Branch, Qazvin, Iran.
³ MSc, Quantitative MR Imaging and Spectroscopy Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Iran.
⁴ MD, Department of Radiology, Advanced Diagnostic and Invasive Radiology Research Center, Tehran University of Medical Sciences, Tehran, Iran.
⁵ PhD, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Iran.
⁶ MD, Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran.

Abstract

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 K^trans 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.

Keywords: Diagnosis; Machine Learning; Magnetic Resonance Imaging; Parotid Neoplasms; Radiomics; Texture Analysis.