To investigate the correlation between perfusion-related parameters obtained with intravoxel incoherent motion (IVIM) and classical perfusion parameters obtained with dynamic contrast-enhanced (DCE) magnetic resonance imaging in patients with head and neck squamous cell carcinoma (HNSCC), and to compare direct and asymptotic fitting, the pixel-by-pixel approach, and a region of interest (ROI)-based approach respectively for IVIM parameter calculation. Seventeen patients with HNSCC were included in this retrospective study. All magnetic resonance (MR) scanning was performed using a 3T MR unit. Acquisition of IVIM was performed using single-shot spin-echo echo-planar imaging with three orthogonal gradients with 12 b-values (0, 10, 20, 30, 50, 80, 100, 200, 400, 800, 1000, and 2000). Perfusion-related parameters of perfusion fraction 'f' and the pseudo-diffusion coefficient 'D*' were calculated from IVIM data by using least square fitting with the two fitting methods of direct and asymptotic fitting, respectively. DCE perfusion was performed in a total of 64 dynamic phases with a 3.2-s phase interval. The two-compartment exchange model was used for the quantification of tumor blood volume (TBV) and tumor blood flow (TBF). Each tumor was delineated with a polygonal ROI for the calculation of f, f∙D* performed using both the pixel-by-pixel approach and the ROI-based approach. In the pixel-by-pixel approach, after fitting each pixel to obtain f, f∙D* maps, the mean value in the delineated ROI on these maps was calculated. In the ROI-based approach, the mean value of signal intensity was calculated within the ROI for each b-value in IVIM images, and then fitting was performed using these values. Correlations between f in a total of four combinations (direct or asymptotic fitting and pixel-by-pixel or ROI-based approach) and TBV were respectively analyzed using Pearson's correlation coefficients. Correlations between f∙D* and TBF were also similarly analyzed. In all combinations of f and TBV, f∙D* and TBF, there was a significant correlation. In the comparison of f and TBV, a moderate correlation was observed only between f obtained by direct fitting with the pixel-by-pixel approach, whereas a good correlation was observed in the comparisons using the other three combinations. In the comparison of f∙D* and TBF, a good correlation was observed only with f∙D* obtained by asymptotic fitting with the ROI-based approach. In contrast, moderate correlations were observed in the comparisons using the other three combinations. IVIM was found to be feasible for the analysis of perfusion-related parameters in patients with HNSCC. Especially, the combination of asymptotic fitting with the ROI-based approach was better correlated with DCE perfusion.

To assess the utility of histogram and texture analysis of magnetic resonance (MR) fat-suppressed T2-weighted imaging (Fs-T2WI) for the prediction of histological diagnosis of head and neck squamous cell carcinoma (SCC) and malignant lymphoma (ML). The cases of 57 patients with SCC (45 well/moderately and 12 poorly differentiated SCC) and 10 patients with ML were retrospectively analyzed. Quantitative parameters with histogram features (relative mean signal, coefficient of variation, kurtosis and skewness) and gray-level co-occurrence matrix (GLCM) features (contrast, correlation, energy and homogeneity) were calculated using Fs-T2WI data with a manual tumor region of interest (ROI). The following significantly different values were obtained for the total SCC versus ML groups: relative mean signal (3.65 ± 0.86 vs. 2.61 ± 0.49), contrast (72.9 ± 16.2 vs. 49.3 ± 8.7) and homogeneity (2.22 ± 0.25 × 10− 1 vs. 2.53 ± 0.12 × 10− 1). In the comparison of the SCC histological grades, the relative mean signal and contrast were significantly lower in the poorly differentiated SCC (2.89 ± 0.63, 56.2 ± 12.9) compared to the well/moderately SCC (3.85 ± 0.81, 77.5 ± 13.9). The homogeneity in poorly differentiated SCC (2.56 ± 0.15 × 10− 1) was higher than that of the well/moderately SCC (2.1 ± 0.18 × 10− 1). Parameters obtained by histogram and texture analysis of Fs-T2WI may be useful for noninvasive prediction of histological type and grade in head and neck malignancy.

Objectives To evaluate the diagnostic value of intravoxel incoherent motion (IVIM) and diffusional kurtosis imaging (DKI) parameters in nasal or sinonasal squamous cell carcinoma (SCC) patients to determine local control/failure. Methods Twenty-eight patients were evaluated. MR acquisition used single-shot spin-echo EPI with 12 b-values. Quantitative parameters (mean value, 25th, 50th and 75th percentiles) of IVIM (perfusion fraction f, pseudo-diffusion coefficient D*, and true-diffusion coefficient D), DKI (kurtosis value K, kurtosis corrected diffusion coefficient D_k) and apparent diffusion coefficient (ADC) were calculated. Parameter values at both the pretreatment and early-treatment period, and the percentage change between these two periods were obtained. Results Multivariate logistic regression analysis: the percentage changes of D (mean, 25th, 50th, 75th), K (mean, 50th, 75th), Dk (mean, 25th, 50th), and ADC (mean, 25th, 50th) were predictors of local control. ROC curve analysis: the parameter with the highest accuracy = the percentage change of D value with the histogram 25th percentile (0.93 diagnostic accuracy). Multivariate Cox regression analyses: the percentage changes of D (mean, 25th, 50th), K (mean, 50th, 75th), Dk (mean, 25th, 50th) and ADC (mean, 25th, 50th) are predictors. Conclusions IVIM and DKI parameters, especially the D-value’s histogram 25th percentile, are useful for predicting local control. Key Points • Noninvasive assessment of treatment outcome in SCC patients was achieved using IVIM/DKI. • Several IVIM and DKI parameters can predict the local control. • Especially, the D-value’s histogram 25th percentile has high diagnostic accuracy.

Purpose To determine whether diffusion-tensor (DT) imaging can demonstrate microstructural white matter abnormalities of multiple system atrophy (MSA) and to correlate these imaging findings with clinical signs and symptoms. Materials and Methods Institutional review board approval and written informed consent were obtained. DT imaging was performed in 16 patients with MSA with predominant cerebellar symptoms (MSA-C) (mean age, 60.0 years ± 5.1 [standard deviation]; range, 51–69 years) and 16 age-matched healthy subjects. Fractional anisotropy (FA) and mean diffusivity (MD) were compared voxel-by-voxel between the two groups by using a two-sample t test. Overlap maps were created to illustrate areas with FA and MD alterations. Correlation between DT imaging indexes and Barthel index score, scale for assessment and rating of ataxia (SARA) score, severity of orthostatic hypotension, age of disease onset, and disease duration was tested by using Spearman rank or Pearson product-moment correlation analysis. T2-weighted and proton density–weighted images of the patients were visually assessed. Results Widespread areas of FA reduction and MD elevation were observed in supra- and infratentorial white matter structures in patients with MSA (P < .05, false discovery rate corrected). Significant correlation (P < .01) between DT imaging indexes and Barthel index score, SARA score, severity of orthostatic hypotension, and disease duration was observed for multiple areas with FA and/or MD alterations. T2-weighted and proton density–weighted images showed no significant abnormality in supratentorial white matter. Conclusion DT imaging may help identify the microstructural white matter abnormalities of MSA-C. DT imaging may be useful for severity assessment of MSA-C. © RSNA, 2010 Supplemental material: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.10090988/-/DC1

The purpose of this study was to determine the predictive power for treatment outcome of a machine-learning algorithm combining magnetic resonance imaging (MRI)-derived data in patients with sinonasal squamous cell carcinomas (SCCs). Thirty-six primary lesions in 36 patients were evaluated. Quantitative morphological parameters and intratumoral characteristics from T2-weighted images, tumor perfusion parameters from arterial spin labeling (ASL) and tumor diffusion parameters of five diffusion models from multi-b-value diffusion-weighted imaging (DWI) were obtained. Machine learning by a non-linear support vector machine (SVM) was used to construct the best diagnostic algorithm for the prediction of local control and failure. The diagnostic accuracy was evaluated using a 9-fold cross-validation scheme, dividing patients into training and validation sets. Classification criteria for the division of local control and failure in nine training sets could be constructed with a mean sensitivity of 0.98, specificity of 0.91, positive predictive value (PPV) of 0.94, negative predictive value (NPV) of 0.97, and accuracy of 0.96. The nine validation data sets showed a mean sensitivity of 1.0, specificity of 0.82, PPV of 0.86, NPV of 1.0, and accuracy of 0.92. In conclusion, a machine-learning algorithm using various MR imaging-derived data can be helpful for the prediction of treatment outcomes in patients with sinonasal SCCs.