BACKGROUND AND PURPOSE:CT and MR imaging features of benign and malignant sinonasal lesions are often nonspecific. Therefore, we evaluated the ADC-based differentiation of these lesions.
Purpose:To establish an MR factor analysis technique for two-dimensional (2D) MR dynamic structures of benign and malignant salivary gland tumors. Materials and Methods:Dynamic contrast-enhanced MRI using a surface coil was performed on 36 patients with benign (N ϭ 24) or malignant (N ϭ 12) salivary gland tumors. Signal intensity kinetics in each pixel of the tumors after contrast medium injections were semiautomatically categorized into four patterns (slow uptake, rapid uptake with high washout, rapid uptake with low washout, and flat). The 2D distributions of the kinetic patterns in the tumors were compared with the histological features of the corresponding parts of the excised tumors and with overall kinetics obtained by a conventional analysis. Results:The MR factor analysis technique allowed the pixel-to-pixel evaluation of the contrast enhancement kinetics of the salivary gland tumors. The 2D distributions of the time-intensity curve (TIC) patterns correlated well with the histological features of the salivary gland tumors and allowed more detailed dynamic structures of the tumors compared with the results obtained by the conventional dynamic study analysis. Conclusion:The proposed MR factor analysis would be clinically feasible to diagnose salivary gland tumors and tumor-like lesions.
Purpose: To evaluate the stepwise approach in differentiating between benign and malignant salivary gland tumors using time-intensity curves (TICs) and apparent diffusion coefficients (ADCs). Materials and Methods:TICs and ADCs were analyzed on the tumor-by-tumor (overall) and pixel-by-pixel (TIC and ADC maps) bases in patients with benign (n ¼ 52) or malignant (n ¼ 18) salivary gland tumor. TICs were categorized into Types 1 (<20% increment ratio), 2 (!20% increment ratio and >120 sec peak time), 3 (!20% increment ratio, 120 sec peak time, and <30% washout ratio), or 4 (!20% increment ratio, 120 sec peak time, and !30% washout ratio). ADCs were classified as extremely low (<0.6 Â 10 À3 mm 2 /sec), low (<1.2), intermediate (<1.8), or high (!1.8).Results: Malignant tumors had small (<30%) areas with Type 1 TIC with one of the following magnetic resonance imaging (MRI) characteristics: Type 3 overall TIC patterns, Type 4 overall TIC patterns and extremely low (<0.60 Â 10 À3 mm 2 /sec) overall ADCs, or Type 2 overall TIC patterns and large (>40%) areas with low or extremely low ADCs. Conclusion:We propose a stepwise approach by using multiparametric MRI techniques as an effective tool for differentiating between benign and malignant salivary gland tumors BENIGN TUMORS REPRESENT 54-79% of salivary gland tumors. The majority (70%-85%) of parotid gland tumors are benign (1). Tumors of the submandibular and sublingual glands are more likely to be malignant; about 40% of submandibular tumors and 70%-90% of sublingual tumors are malignant. Salivary gland tumors also occur in minor salivary glands of the palate, buccal mucosa, lips, tongue, retromolar pad, and glossopharyngeal area. Approximately half of all minor salivary gland tumors are malignant.Several magnetic resonance imaging (MRI) techniques have been applied to differentiate between benign and malignant salivary gland tumors. Of these, dynamic contrast-enhanced (DCE) and diffusionweighted (DW) MRI have been the most frequently evaluated for that purpose (2,3). Studies have shown that analyses using time-intensity curves (TICs) that are obtained by DCE imaging and apparent diffusion coefficients (ADCs) obtained by DW imaging could be potential tools for the differentiation of tumor types. The single use of these MRI techniques has yielded acceptable results, but the diagnostic efficacy was not high (4).Salivary gland tumors comprise distinctive tissues, including proliferating tumor cells, myxomatous tissues, lymphoid tissues, necrosis, and cysts (5). The analysis using a large region of interest (ROI) in a histologically heterogeneous tumor may, therefore, result in spurious results with regard to tumor histology. To avoid this potential error, 2D analysis using highresolution imaging is mandatory for precise tissue characterization and for differentiation between benign and malignant salivary gland tumors.Recently, Yabuuchi et al (6) raised the possibility that the combined use of these two MRI techniques can greatly improve the diagnostic efficacy of MRI in di...
BACKGROUND AND PURPOSE:The sinonasal region is a platform for a broad spectrum of benign and malignant diseases, and image-based differentiation between benign and malignant diseases in this area is often difficult. Here, we evaluated multiparametric MR imaging with combined use of TICs and ADCs for the differentiation between benign and malignant sinonasal tumors and tumorlike diseases.
Intravoxel incoherent motion (IVIM) imaging can characterize diffusion and perfusion of normal and diseased tissues, and IVIM parameters are authentically determined by using cumbersome least-squares method. We evaluated a simple technique for the determination of IVIM parameters using geometric analysis of the multiexponential signal decay curve as an alternative to the least-squares method for the diagnosis of head and neck tumors. Pure diffusion coefficients (D), microvascular volume fraction (f), perfusion-related incoherent microcirculation (D*), and perfusion parameter that is heavily weighted towards extravascular space (P) were determined geometrically (Geo D, Geo f, and Geo P) or by least-squares method (Fit D, Fit f, and Fit D*) in normal structures and 105 head and neck tumors. The IVIM parameters were compared for their levels and diagnostic abilities between the 2 techniques. The IVIM parameters were not able to determine in 14 tumors with the least-squares method alone and in 4 tumors with the geometric and least-squares methods. The geometric IVIM values were significantly different (p<0.001) from Fit values (+2±4% and −7±24% for D and f values, respectively). Geo D and Fit D differentiated between lymphomas and SCCs with similar efficacy (78% and 80% accuracy, respectively). Stepwise approaches using combinations of Geo D and Geo P, Geo D and Geo f, or Fit D and Fit D* differentiated between pleomorphic adenomas, Warthin tumors, and malignant salivary gland tumors with the same efficacy (91% accuracy = 21/23). However, a stepwise differentiation using Fit D and Fit f was less effective (83% accuracy = 19/23). Considering cumbersome procedures with the least squares method compared with the geometric method, we concluded that the geometric determination of IVIM parameters can be an alternative to least-squares method in the diagnosis of head and neck tumors.
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