MRI-Based Radiomics to Differentiate between Benign and Malignant Parotid Tumors With External Validation

Abstract

BackgroundThe differentiation between benign and malignant parotid lesions is crucial to defining the treatment plan, which highly depends on the tumor histology. We aimed to evaluate the role of MRI-based radiomics using both T2-weighted (T2-w) images and Apparent Diffusion Coefficient (ADC) maps in the differentiation of parotid lesions, in order to develop predictive models with an external validation cohort.Materials and MethodsA sample of 69 untreated parotid lesions was evaluated retrospectively, including 37 benign (of which 13 were Warthin’s tumors) and 32 malignant tumors. The patient population was divided into three groups: benign lesions (24 cases), Warthin’s lesions (13 cases), and malignant lesions (32 cases), which were compared in pairs. First- and second-order features were derived for each lesion. Margins and contrast enhancement patterns (CE) were qualitatively assessed. The model with the final feature set was achieved using the support vector machine binary classification algorithm.ResultsModels for discriminating between Warthin’s and malignant tumors, benign and Warthin’s tumors and benign and malignant tumors had an accuracy of 86.7%, 91.9% and 80.4%, respectively. After the feature selection process, four parameters for each model were used, including histogram-based features from ADC and T2-w images, shape-based features and types of margins and/or CE. Comparable accuracies were obtained after validation with the external cohort.ConclusionsRadiomic analysis of ADC, T2-w images, and qualitative scores evaluating margins and CE allowed us to obtain good to excellent diagnostic accuracies in differentiating parotid lesions, which were confirmed with an external validation cohort.

Usefulness of MRI-based radiomic features for distinguishing Warthin tumor from pleomorphic adenoma: performance assessment using T2-weighted and post-contrast T1-weighted MR images

“…Among benign parotid tumors, WT tends to be more heterogeneous than PA [11] , so our finding of a higher GLCM_inverse variance in WT would seem contradictory. This finding is in line with those by Piludu et al, who hypothesized that larger lesions had lower dissimilarity and higher energy (hence, higher homogeneity and uniformity) than smaller ones [14] , and the WT lesions assessed in our study were larger than PA (9.476 vs 5.570 cm 3 , p = 0.001).…”

“…Radiomics is an emerging method that could improve diagnostic accuracy through the quantitative analysis of data from images, and offers advantages over conventional biopsy including noninvasiveness, virtually unlimited repeatability, and possibility to assess the whole tumor tissue and to perform longitudinal follow-up testing [25] , [26] , [27] , [28] . While several articles have been published trying to differentiate benign from malignant salivary gland tumors based on radiomics features extracted from head-and-neck MRI examinations, there is still no consensus on which image sequences should be analyzed, with some studies using T2w images [11] and other ones DWI [15] or a combination of sequences (such as post-contrast T1w, T2w, DCE or apparent diffusion coefficient [ADC] images) [13] , [14] , [17] , [29] . In our study we selected to analyze T2w and pcfsT1w images, because these sequences are an essential part of MRI examinations aimed to the evaluation of a parotid mass, providing key information for the differential diagnosis.…”

“…Skewness was the feature extracted from T2w images with the highest specificity (88 %) in discriminating WT from PA. Some previous articles [11] , [12] , [14] reported skewness to allow discriminating WT from PA. In particular, Sarioglu et al [12] found that WT showed significantly higher skewness on T2w images ( p < 0.001) than PA, and that skewness was the feature with the largest AUC (0.789; 95 % CI, 0.694–0.885), with a cutoff lower than −0.6895 being associated with PA diagnosis with a sensitivity of 84.4 % and specificity of 62.5 %.…”

“…Radiomics is an emerging approach that allows the extraction of quantitative data from medical images (including MRI) which, correlated with genomic and clinical parameters, are able to improve diagnostic accuracy and aid individualized patient treatment and outcome prediction [10] . Over the last years, some articles have been published trying to differentiate benign from malignant salivary gland tumors, but only some of them have addressed the discrimination of WT from PA, yielding mixed results [11] , [12] , [13] , [14] , [15] , [16] , [17] .…”

Differential diagnosis of parotid gland tumours: Application of SWI combined with DWI and DCE-MRI

“…Over the past decades, diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE)-MRI and radiomics, have been used to improve the differential diagnosis of PTs [14][15][16][17][18][19]. However, the apparent diffusion coefficient (ADC) value of Warthin tumours (WTs) overlaps with that of malignant tumours, and ADC alone cannot accurately distinguish benign and malignant PTs [14,20].…”