Dynamic contrast-enhanced MRI and diffusion kurtosis imaging for the differentiation of salivary gland tumours.

Abstract

Objectives: This study aimed to evaluate the diagnostic ability of relative values of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion kurtosis imaging (DKI) quantitative parameters for salivary gland tumours (SGTs).

Methods: A total of 107 patients with histopathologically confirmed SGTs (18 malignant [MTs], 38 pleomorphic adenomas [PAs], 31 Warthin tumours [WTs], 20 basal cell adenomas [BCAs]) underwent MRI with DKI and DCE-MRI sequences. Quantitative parameters included DCE-MRI-derived volume transfer constant (Ktrans), rate constant (Kep), fractional volume of the extravascular-extracellular space (Ve), plasma fraction (Vp), and DKI-derived mean kurtosis (MK) and mean diffusion (MD).The receiver operating characteristic (ROC) curve were used for statistical analysis. Statistical significance was set at P < 0.05.

Results: PAs exhibited the lowest MK (0.49 ± 0.15) among all groups (P < 0.05). Compared to WTs, PAs showed lower Kep (366.89 [260.06, 568.32]×10-³ min-1), higher MD (2.02 ± 0.42 × 10-³ mm2/s), and higher Ve (551.83 [388.10, 883.19]×10-³). PAs also displayed higher Ve, lower Kep, and lower Vp (85.42 [20.53, 332.72])×10-³) than BCAs, and lower Vp with higher Ve than MTs (all P < 0.05). WTs had significantly lower Ve (218.86 [142.07, 341.76]×10-³) than MTs (P = 0.001). BCAs demonstrated lower MK (0.61 ± 0.23) and higher MD (1.97 ± 0.44 × 10-³ mm2/s) compared to WTs and MTs (P < 0.05), alongside lower Ktrans (355.25 [211.88, 506.92]×10-³ min-1) and Ve (380.89 [271.28, 589.53]×10-³) than WTs (P < 0.05). Logistic regression analysis revealed enhanced discrimination: MK+Ve (AUC = 0.895) and MK + MD + Ve + Kep (AUC = 0.936) differentiated PAs from WTs; Kep + Vp + Ve distinguished PAs from BCAs (AUC = 0.843); MD + MK + Vp separated PAs from MTs (AUC = 0.854); Ktrans + MK differentiated WTs from BCAs (AUC = 0.856).

Conclusions: DKI and DCE-MRI parameters complement each other, enabling accurate SGT subtype differentiation. Combined use of these parameters achieves high diagnostic accuracy, and a stepwise diagnostic flowchart was designed to facilitate systematic discrimination among the four tumour types.