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BACKGROUND Differentiation between glioblastoma and brain metastasis is highly important due to differing medical treatment strategies. While MRI is the modality of choice for the assessment of patients with brain tumors, differentiation between glioblastoma and solitary brain metastasis may be challenging due to their similar appearance on MRI. PURPOSE To differentiate between glioblastoma and brain metastasis subtypes using radiomics analysis based on conventional post-contrast T1 -weighted (T1 W) MRI. STUDY TYPE Retrospective. SUBJECTS Data were acquired from 439 patients: 212 patients with glioblastoma and 227 patients with brain metastasis (breast, lung, and others). FIELD STRENGTH/SEQUENCE Post-contrast 3D T1 W gradient echo images, acquired with 1.5 and 3.0 T MR systems. ASSESSMENT Analysis included image preprocessing, segmentation of tumor area, and features extraction including: patients' clinical information, tumor location, first- and second-order statistical, morphological, wavelet features, and bag-of-features. Following dimension reduction, classification was performed using various machine-learning algorithms including support-vector machine (SVM), k-nearest neighbor, decision trees, and ensemble classifiers. STATISTICAL TESTS For classification, the data were divided into training (80%) and testing datasets (20%). Following optimization of the classifiers, mean sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were calculated. RESULTS For the testing dataset, the best results for differentiation of glioblastoma from brain metastasis were obtained using the SVM classifier with mean accuracy = 0.85, sensitivity = 0.86, specificity = 0.85, and AUC = 0.96. The best classification results between glioblastoma and brain metastasis subtypes were obtained using SVM classifier with mean accuracy = 0.85, 0.89, 0.75, 0.90; sensitivity = 1.00, 0.60, 0.57, 0.11; specificity = 0.76, 0.92, 0.87, 0.99; and AUC = 0.98, 0.81, 0.83, 0.57 for the glioblastoma, breast, lung, and other brain metastases, respectively. DATA CONCLUSION Differentiation between glioblastoma and brain metastasis showed a high success rate based on postcontrast T1 W MRI. Classification between glioblastoma and brain metastasis subtypes may require additional MR sequences with other tissue contrasts. LEVEL OF EVIDENCE 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:519-528.

BACKGROUND High-grade gliomas (HGGs) induce both vasogenic edema and extensive infiltration of tumor cells, both of which present with similar appearance on conventional MRI. Using current radiological criteria, differentiation between these tumoral and nontumoral areas within the nonenhancing lesion area remains challenging. PURPOSE To use radiomics patch-based analysis, based on conventional MRI, for the classification of the nonenhancing lesion area in patients with HGG into tumoral and nontumoral components. STUDY TYPE Prospective. SUBJECTS In all, 179 MRI scans were obtained from 102 patients: 67 patients with HGG and 35 patients with brain metastases. A subgroup of 15 patients with HGG were scanned before and following administration of bevacizumab. FIELD STRENGTH/SEQUENCE Pre and postcontrast agent T1 -weighted-imaging (WI), T2 WI, FLAIR, diffusion-tensor-imaging (DTI), and dynamic-contrast-enhanced (DCE)-MRI at 3T. ASSESSMENT A total of 225 histograms and gray-level-co-occurrence matrix-based features were extracted from the nonenhancing lesion area. Tumoral volumes of interest (VOIs) were defined at the peritumoral area in patients with HGG; nontumoral VOIs were defined in patients with brain metastasis. Twenty machine-learning algorithms including support-vector-machine (SVM), k-nearest neighbor, decision-trees, and ensemble classifiers were tested. The best classifier was trained on the entire labeled data, and was used to classify the entire data. STATISTICAL TESTS Dimensional reduction was performed on the 225 features using principal component analysis. Classification results were evaluated based on the sensitivity, specificity, and accuracy of each of the 20 classifiers, first based on a training and testing dataset (80% of the labeled data) in a 5-fold manner, and next by applying the best classifier to the validation data (the remaining 20% of the labeled data). Results were additionally evaluated by assessing differences in dynamic-contrast-enhanced plasma-volume (vp ) and volume-transfer-constant (ktrans ) values between the two components using Mann-Whitney U-test/t-test. RESULTS The best classification into tumoral and nontumoral lesion components was obtained using a linear SVM classifier, with average accuracy of 87%, sensitivity 86%, and specificity of 89% (for the training and testing data). Significantly higher vp and ktrans values (P < 0.0001) were detected in the tumoral compared to the nontumoral component. Preliminary classification results in a subgroup of patients treated with bevacizumab demonstrated a reduction mainly in the nontumoral component following administration of bevacizumab, enabling early assessment of disease progression in some patients. DATA CONCLUSION A radiomics patch-based analysis enables classification of the nonenhancing lesion area in patients with HGG. Preliminary results were promising and the proposed method has the potential to assist in clinical decision-making and to improve therapy response assessment in patients with HGG. LEVEL OF EVIDENCE 1 Technical Efficacy Stage 4 J. Magn. Reson. Imaging 2018.

PURPOSE To optimize the analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) under the two-compartment-exchange-model (2CXM) and to incorporate voxelwise bolus-arrival-time (BAT). MATERIALS AND METHODS The accuracy of the pharmacokinetic (PK) parameters, extracted from 3T DCE-MRI using 2CXM, was tested under several conditions: eight algorithms for data estimation; correction for BAT; using model selection; different temporal resolution and scan duration. Comparisons were performed on simulated data. The best algorithm was applied to seven patients with brain tumors or following stroke. The extracted perfusion parameters were compared to those of dynamic susceptibility contrast MRI (DSC-MRI). RESULTS ACoPeD (AIF-corrected-perfusion-DCE-MRI), an analysis using a 2nd derivative regularized-spline and incorporating BAT, achieved the most accurate estimation in simulated data, mean-relative-error: Fp , F, vp , ve : 24.8%, 41.7%, 26.4%, 27.2% vs. 76.5%, 190.8%, 78.8%, 82.39% of the direct four parameters estimation (one-sided two-sample t-test, P < 0.001). Correction for BAT increased the estimation accuracy of the PK parameters by more than 30% and provided a supertemporal resolution estimation of the BAT (higher than the acquired resolution, mean-absolute-error 0.2 sec). High temporal resolution (∼2 sec) is required to avoid biased estimation of PK parameters, and long scan duration (∼20 min) is important for reliable permeability but not for perfusion estimations, mean-error-reduction: E: ∼12%, ve : ∼6%. Using ACoPeD, PK values from normal-appearing white matter, gray matter, and lesion were extracted from patients. Preliminary results showed significant voxelwise correlations to DSC-MRI, between flow values in a patient following stroke (r = 0.49, P < 0.001), and blood volume in a patient with a brain tumor (r = 0.62, P < 0.001). CONCLUSION This study proposes an optimized analysis method, ACoPeD, for tissue perfusion and permeability estimation using DCE-MRI, to be used in clinical settings. LEVEL OF EVIDENCE 1 J. Magn. Reson. Imaging 2017;45:237-249.

relaxationtime from a set of variable flip-angle (FA) spoiled gradientrecalled echo images.Materials and Methods: The proposed method: (a) usesa uniform weighting of all FAs, (b) takes into accountglobal inaccuracies in the generation of the prescribedFAs by estimating the actual FAs, and (c) incorporatesdata-driven local B

Purpose: To evaluate the sensitivity of high b value diffusion weight magnetic resonance imaging (DWI) in detecting normal white matter maturation, compare it to conventional diffusion tensor imaging (DTI), and to obtain normative quantitative data using this method. Materials and Methods: High b value DWI (bmax 6000 sec/mm 2 ) using q-space analysis and conventional DTI (b 1000 sec/mm 2 ) were performed on 36 healthy subjects aged 4 months to 23 years. Fractional-anisotropy (FA), apparent-displacement, and apparent-probability values were measured in all slices and in six regions of interest (ROIs) of large fiber tracks. Values were correlated with each other and with age using regression analysis. Results: FA, displacement, and probability indices from all slices were highly correlated with each other (r 0.87, P 0.0001) and with age (r 0.82, P 0.0001). All age-related changes in the six pre-determined ROIs were best fitted by mono-exponential functions. Changes in the splenium extended to a later age when compared with the genu of the corpus-callosum, while the centrum semi-ovale demonstrated the latest changes with age. Conclusions: High b-value DWI and DTI showed changes in white matter from infancy through adulthood. However, high b-value detects a signal that is likely to originate mainly from the intra-axonal water population, and thus may represent different aspects of development and different sensitivity to pathology.