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The aim of this study is to investigate the possibility of predicting histological grade in patients with endometrial cancer on the basis of intravoxel incoherent motion (IVIM)-related histogram analysis parameters. This prospective study included 52 women with endometrial cancer (EC) who underwent MR imaging as initial staging in our hospital, allocated into low-grade (G1 and G2) and high-grade (G3) tumors according to the pathology reports. Regions of interest (ROIs) were drawn on the diffusion weighted images and apparent diffusion coefficient (ADC), true diffusivity (D), and perfusion fraction (f) using diffusion models were computed. Mean, median, skewness, kurtosis, and interquartile range (IQR) were calculated from the whole-tumor histogram. The IQR of the diffusion coefficient (D) was significantly lower in the low-grade tumors from that of the high-grade group with an adjusted p-value of less than 5% (0.048). The ROC curve analysis results of the statistically significant IQR of the D yielded an accuracy, sensitivity, and specificity of 74.5%, 70.1%, and 76.5% respectively, for discriminating low from high-grade tumors, with an optimal cutoff of 0.206 (×10−3 mm2/s) and an AUC of 75.4% (95% CI: 62.1 to 88.8). The IVIM modeling coupled with histogram analysis techniques is promising for preoperative differentiation between low- and high-grade EC tumors. [ABSTRACT FROM AUTHOR]

Abstract Purpose The purpose of this study was to correlate diffusion and perfusion quantitative and semi-quantitative MR parameters, on patients with peripheral arterial disease. In addition, we investigated which perfusion model better describes the behavior of the dynamic contrast-enhanced (DCE) MR data signal on ischemic regions of the lower limb. Methods Linear and nonlinear least squares algorithms, were incorporated for the quantification of the parameters through a variety of widely used models, able to extract physiological information from each imaging technique. All numerical calculations were implemented in Python 3.5 and include the: Intra voxel incoherent motion for diffusion and Patlak's, Extended Toft's and Gamma Capillary Transit time (GCTT) models for perfusion MRI. Results Our initial voxel by voxel correlation analysis didn't show any significant correlation based on the Pearson's Correlation metric between diffusion and perfusion parameters. To account for the inherited noise from the raw data, a Gaussian filter was applied to the parametric maps in order for the data to be comparable. By repeating our analysis in the filtered image maps, a good correlation (>0.5) of diffusion and perfusion parameters was achieved. Conclusions Perfusion and diffusion MRI quantitative and semi-quantitative parameters can be obtained through a variety of physiological-pharmacokinetic models. This paper compares most of the widely-known models and parameters in both techniques with data from patients with peripheral arterial disease. Initial analysis showed no correlation in the perfusion parametric maps of DWI and DCE MRI data but a good correlation was obtained after smoothing the parametric maps indicating that perfusion information could be obtained from diffusion MRI images in patients with peripheral arterial disease. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to compare the performance of four diffusion models, including mono and bi-exponential both Gaussian and non-Gaussian models, in diffusion weighted imaging of rectal cancer. Material and methods: Nineteen patients with rectal adenocarcinoma underwent MRI examination of the rectum before chemoradiation therapy including a 7 b-value diffusion sequence (0, 25, 50, 100, 500, 1000 and 2000 s/mm2) at a 1.5T scanner. Four different diffusion models including mono- and bi-exponential Gaussian (MG and BG) and non-Gaussian (MNG and BNG) were applied on whole tumor volumes of interest. Two different statistical criteria were recruited to assess their fitting performance, including the adjusted-R2 and Root Mean Square Error (RMSE). To decide which model better characterizes rectal cancer, model selection was relied on Akaike Information Criteria (AIC) and F-ratio. Results: All candidate models achieved a good fitting performance with the two most complex models, the BG and the BNG, exhibiting the best fitting performance. However, both criteria for model selection indicated that the MG model performed better than any other model. In particular, using AIC Weights and F-ratio, the pixel-based analysis demonstrated that tumor areas better described by the simplest MG model in an average area of 53% and 33%, respectively. Non-Gaussian behavior was illustrated in an average area of 37% according to the F-ratio, and 7% using AIC Weights. However, the distributions of the pixels best fitted by each of the four models suggest that MG failed to perform better than any other model in all patients, and the overall tumor area. Conclusion: No single diffusion model evaluated herein could accurately describe rectal tumours. These findings probably can be explained on the basis of increased tumour heterogeneity, where areas with high vascularity could be fitted better with bi-exponential models, and areas with necrosis would mostly follow mono-exponential behavior. [ABSTRACT FROM AUTHOR]

Purpose: To investigate and histopathologically validate the role of model selection in the design of novel parametric meta-maps towards the discrimination of low from high-grade soft tissue sarcomas (STSs) using multiple Diffusion Weighted Imaging (DWI) models.Methods: DWI data of 28 patients were quantified using the mono-exponential, bi-exponential, stretched-exponential and the diffusion kurtosis model. Akaike Weights (AW) were calculated from the corrected Akaike Information Criteria (AICc) to select the most suitable model for every pixel within the tumor volume. Pseudo-colorized classification maps were then generated to depict model suitability, hypothesizing that every single model underpins different tissue properties and cannot solely characterize the whole tumor. Single model parametric maps were turned into meta-maps using the classification map and a histological validation of the model suitability results was conducted on several subregions of different tumors. Several histogram metrics were calculated from all derived maps before and after model selection, statistical analysis was conducted using the Mann-Whitney U test, p-values were adjusted for multiple comparisons and performance of all statistically significant metrics was evaluated using the Receiver Operator Characteristic (ROC) analysis.Results: The histologic analysis on several tumor subregions confirmed model suitability results on these areas. Only 3 histogram metrics, all derived from the meta-maps, were found to be statistically significant in differentiating low from high-grade STSs with an AUC higher than 89 %.Conclusion: Embedding model selection in the design of the diffusion parametric maps yields to histogram metrics of high discriminatory power in grading STSs. [ABSTRACT FROM AUTHOR]