Discriminating between benign and malignant salivary gland tumors using diffusion-weighted imaging and intravoxel incoherent motion at 3 Tesla

Differentiation of salivary gland tumours using diffusion-weighted image-based virtual MR elastography: a pilot study

OBJECTIVES

Differentiation among benign salivary gland tumours, Warthin tumours (WTs), and malignant salivary gland tumours is crucial to treatment planning and predicting patient prognosis. However, differentiation of those tumours using imaging findings remains difficult. This study evaluated the usefulness of elasticity determined from diffusion-weighted image (DWI)-based virtual MR elastography (MRE) compared with conventional magnetic resonance imaging (MRI) findings in differentiating the tumours.

METHODS

This study included 17 benign salivary gland tumours, 6 WTs, and 11 malignant salivary gland tumours scanned on neck MRI. The long and short diameters, T1 and T2 signal intensities, tumour margins, apparent diffusion coefficient (ADC) values, and elasticity from DWI-based virtual MRE of the tumours were evaluated. The interobserver agreement in measuring tumour elasticity and the receiver operating characteristic (ROC) curves were also assessed.

RESULTS

The long and short diameters and the T1 and T2 signal intensities showed no significant difference among the 3 tumour groups. Tumour margins and the mean ADC values showed significant differences among some tumour groups. The elasticity from virtual MRE showed significant differences among all 3 tumour groups and the interobserver agreement was excellent. The area under the ROC curves of the elasticity were higher than those of tumour margins and mean ADC values.

CONCLUSION

Elasticity values based on DWI-based virtual MRE of benign salivary gland tumours, WTs, and malignant salivary gland tumours were significantly different. The elasticity of WTs was the highest and that of benign tumours was the lowest. The elasticity from DWI-based virtual MRE may aid in the differential diagnosis of salivary gland tumours.

Radiomics for Discriminating Benign and Malignant Salivary Gland Tumors; Which Radiomic Feature Categories and MRI Sequences Should Be Used?

The lack of a consistent MRI radiomic signature, partly due to the multitude of initial feature analyses, limits the widespread clinical application of radiomics for the discrimination of salivary gland tumors (SGTs). This study aimed to identify the optimal radiomics feature category and MRI sequence for characterizing SGTs, which could serve as a step towards obtaining a consensus on a radiomics signature. Preliminary radiomics models were built to discriminate malignant SGTs (n = 34) from benign SGTs (n = 57) on T1-weighted (T1WI), fat-suppressed (FS)-T2WI and contrast-enhanced (CE)-T1WI images using six feature categories. The discrimination performances of these preliminary models were evaluated using 5-fold-cross-validation with 100 repetitions and the area under the receiver operating characteristic curve (AUC). The differences between models’ performances were identified using one-way ANOVA. Results show that the best feature categories were logarithm for T1WI and CE-T1WI and exponential for FS-T2WI, with AUCs of 0.828, 0.754 and 0.819, respectively. These AUCs were higher than the AUCs obtained using all feature categories combined, which were 0.750, 0.707 and 0.774, respectively (p < 0.001). The highest AUC (0.846) was obtained using a combination of T1WI + logarithm and FS-T2WI + exponential features, which reduced the initial features by 94.0% (from 1015 × 3 to 91 × 2). CE-T1WI did not improve performance. Using one feature category rather than all feature categories combined reduced the number of initial features without compromising radiomic performance.