radiomic analysis of multi-parametric mr images (mri) for classification of parotid tumors

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.