Dynamic contrast‑enhanced MRI and diffusion kurtosis imaging for the differentiation of Salivary gland tumors

Abstract Objective 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 tumors (SGTs). Methods A total of 107 patients with histopathologically confirmed SGTs (18 malignant [MTs], 38 pleomorphic adenomas [PAs], 31 Warthin tumors [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). Conclusion 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 tumor types.