Quantitative Dynamic Contrast-Enhanced Magnetic Resonance Imaging for the Analysis of Microvascular Permeability in Peritumor Brain Edema of Fibrous Meningiomas.

INTRODUCTION: This study aims to analyze the permeability of intra- and peri-meningiomas regions and compare the microvascular permeability between peritumoral brain edema (PTBE) and non-PTBE using DCE-MRI. METHODS: This was a retrospective of patients with meningioma who underwent surgery. The patients were grouped as PTBE and non-PTBE. The DCE-MRI quantitative parameters, including volume transfer constant (Ktrans), rate constant (Kep), extracellular volume (Ve), and mean plasma volume (Vp), obtained using the extended Tofts-Kety 2-compartment model. Logistic regression analysis was conducted to explore the risk factor of PTBE. RESULTS: Sixty-three patients, diagnosed as fibrous meningioma, were included in this study. They were 17 males and 46 females, aged from 32 to 88 years old. Kep and Vp were significantly lower in patients with PTBE compared with those without (Kep: 0.1852 ± 0.0369 vs. 0.5087 ± 0.1590, p = 0.010; Vp: 0.0090 ± 0.0020 vs. 0.0521 ± 0.0262, p = 0.007), while there were no differences regarding Ktrans and Ve (both p > 0.05). The multivariable analysis showed that tumor size ≥10 cm3 (OR = 4.457, 95% CI: 1.322-15.031, p = 0.016) and Vp (OR = 0.572, 95%CI: 0.333-0.981, p = 0.044) were independently associated with PTBE in patients with meningiomas. CONCLUSION: DCE-magnetic resonance imaging·Meningioma·Blood vessel MRI can be used to quantify the microvascular permeability of PTBE in patients with meningioma.

Usefulness of Noncontrast MRI-Based Radiomics Combined Clinic Biomarkers in Stratification of Liver Fibrosis.

Purpose: To develop and validate a radiomic nomogram based on texture features from out-of-phase T1W images and clinical biomarkers in prediction of liver fibrosis. Materials and Methods: Patients clinically diagnosed with chronic liver fibrosis who underwent liver biopsy and noncontrast MRI were enrolled. All patients were assigned to the nonsignificant fibrosis group with fibrosis stage <2 and the significant fibrosis group with stage ≥2. Texture parameters were extracted from out-of-phase T1-weighted (T1W) images and calculated using the Artificial Intelligent Kit (AK). Boruta and LASSO regressions were used for feature selection and a multivariable logistic regression was used for construction of a combinational model integrating radiomics and clinical biomarkers. The performance of the models was assessed by using the receiver operator curve (ROC) and decision curve. Results: ROC analysis of the radiomics model that included the most discriminative features showed AUCs of the training and test groups were 0.80 and 0.78. A combinational model integrating RADscore and fibrosis 4 index was established. ROC analysis of the training and test groups showed good to excellent performance with AUC of 0.93 and 0.86. Decision curves showed the combinational model added more net benefit than radiomic and clinical models alone. Conclusions: The study presents a combinational model that incorporates RADscore and clinical biomarkers, which is promising in classification of liver fibrosis.

Use of Texture Analysis on Noncontrast MRI in Classification of Early Stage of Liver Fibrosis.

Purpose. To compare the diagnostic value of texture analysis- (TA-) derived parameters from out-of-phase T1W, in-phase T1W, and T2W images in the classification of the early stage of liver fibrosis. Methods. Patients clinically diagnosed with hepatitis B infection, who underwent liver biopsy and noncontrast MRI scans, were enrolled. TA parameters were extracted from out-of-phase T1-weighted (T1W), in-phase T1W, and T2-weighted (T2W) images and calculated using Artificial Intelligent Kit (AK). Features were extracted including first-order, shape, gray-level cooccurrence matrix, gray-level run-length matrix, neighboring gray one tone difference matrix, and gray-level differential matrix. After statistical analyses, final diagnostic models were constructed. Receiver operating curves (ROCs) and areas under the ROC (AUCs) were used to assess the diagnostic value of each final model and 100-time repeated cross-validation was applied to assess the stability of the logistic regression models. Results. A total of 57 patients were enrolled in this study, with 27 in the fibrosis stage < 2 and 30 in stages ≥ 2. Overall, 851 features were extracted per ROI. Eight features with high correlation were selected by the maximum relevance method in each sequence, and all had a good diagnostic performance. ROC analysis of the final models showed that all sequences had a preferable performance with AUCs of 0.87, 0.90, and 0.96 in T2W and in-phase and out-of-phase T1W, respectively. Cross-validation results reported the following values of mean accuracy, specificity, and sensitivity: 0.98 each for out-of-phase T1W; 0.90, 0.89, and 0.90 for in-phase T1W; and 0.86, 0.88, 0.84 for T2W in the training set, and 0.76, 0.81, and 0.72 for out-of-phase T1W; 0.74, 0.72, and 0.75 for in-phase T1W; and 0.63, 0.64, and 0.63 for T2W for the test group, respectively. Conclusion. Noncontrast MRI scans with texture analysis are viable for classifying the early stages of liver fibrosis, exhibiting excellent diagnostic performance.