Differentiation of salivary gland tumor using diffusion-weighted imaging with a fractional order calculus model.

OBJECTIVE: To evaluate the feasibility of using imaging parameters (D, beta and mu) obtained from fractional order calculus (FROC) diffusion model to differentiate salivary gland tumors. METHODS: 15 b-value (0-2000 s/mm(2)) diffusion-weighted imaging (DWI) was scanned in 62 patients with salivary gland tumors (47 benign and 15 malignant). Diffusion coefficient D, fractional order parameter beta (which correlates with tissue heterogeneity) and a microstructural quantity mu of the solid portion within the tumor were calculated, and compared between benign and malignant groups, or among pleomorphic adenoma (PA), Warthin's tumor (WT), and malignant tumor (MT) groups. Performance of FROC parameters for differentiation was assessed using receiver operating characteristic analysis. RESULTS: None of the FROC parameters exhibited significant differences between benign and malignant group (D, p = 0.150; beta, p = 0.967; mu, p = 0.693). WT showed significantly lower D (p < 0.001) and beta (p < 0.001), while higher mu (p = 0.001) than PA. Combination of D, beta and mu showed optimal diagnostic performance (area under the curve, AUC, 0.998). MT showed significantly lower D (p = 0.001) and beta (p = 0.025) than PA, while no significant difference was found on mu (p = 0.064). Combination of D and beta showed optimal diagnostic performance (AUC, 0.933). Significant difference was found on beta (p = 0.027) between MT and WT, while not on D (p = 0.806) and mu (p = 0.789). Setting a betaof 0.615 as the cut-off value, optimal diagnostic performance could be obtained (AUC = 0.806). CONCLUSION: A non-Gaussian FROC diffusion model can serve as a noninvasive and quantitative imaging technique for differentiating salivary gland tumors. ADVANCES IN KNOWLEDGE: (1) PA showed higher D and beta and lower mu than WT. (2) PA had higher D and beta than MT. (3) WT demonstrated lower beta than MT. (4) beta, as a new FROC parameter, could offer an added value to the differentiation.