Simple SummaryDifferentiating between benign and malignant parotid gland tumors is of paramount importance as it impacts therapeutical management. MRI represents the best imaging technique in diagnosing and characterizing parotid gland tumors, offering a high soft tissue contrast resolution. However, there are still overlapping radiological features between tumoral types; thus, accurate malignancy detection remains a challenge. Recently, radiomics has gained recognition as a promising new non-invasive approach in oncological imaging, especially related to tumor classification, and has the potential to become an additional diagnostic tool that might offer support in the clinical decision-making scenario.AbstractThe aim of this study was to assess the ability of MRI radiomic features to differentiate between benign parotid gland tumors (BPGT) and malignant parotid gland tumors (MPGT). This retrospective study included 93 patients who underwent MRI examinations of the head and neck region (78 patients presenting unique PGT, while 15 patients presented double PGT). A total of 108 PGT with histological confirmation were eligible for the radiomic analysis and were assigned to a training group (n= 83; 58 BPGT; 25 MPGT) and a testing group (n= 25; 16 BPGT; 9 MPGT). The radiomic features were extracted from 3D segmentations of the PGT on the T2-weighted and fat-saturated, contrast-enhanced T1-weighted images. Following feature reduction techniques, including LASSO regression analysis, a radiomic signature (RS) was built with five radiomic features. The RS presented a good diagnostic performance in differentiating between PGT, achieving an area under the curve (AUC) of 0.852 (p< 0.001) in the training set and 0.786 (p= 0.017) in the testing set. In both datasets, the RS proved to have lower values in the BPGT group as compared to MPGT group (p< 0.001 andp= 0.023, respectively). The multivariate analysis revealed that RS was independently associated with PGT malignancy, together with the ill-defined margin pattern (p= 0.031,p= 0.001, respectively). The complex model, using clinical data, MRI features and the RS, presented a higher diagnostic performance (AUC of 0.976) in comparison to the RS alone. MRI-based radiomic features could be considered potential additional imaging biomarkers able to discriminate between benign and malignant parotid gland tumors.