Dynamic susceptibility contrast MRI (DSC‐MRI) is the current standard for the measurement of Cerebral Blood Flow (CBF) and Cerebral Blood Volume (CBV), but it is not suitable for the measurement of Extraction Flow (EF) and may not allow for absolute quantification. The objective of this study was to develop and evaluate a methodology to measure CBF, CBV, and EF from T1‐weighted dynamic contrast‐enhanced MRI (DCE‐MRI). A two‐compartment modeling approach was developed, which applies both to tissues with an intact and with a broken Blood‐Brain‐Barrier (BBB). The approach was evaluated using measurements in normal grey matter (GM) and white matter (WM) and in tumors of 15 patients. Accuracy and precision were estimated with simulations of normal brain tissue. All tumor and normal tissue curves were accurately fitted by the model. CBF (mL/100 mL/min) was 82 ± 21 in GM and 23 ± 14 in WM, CBV (mL/100 mL) was 2.6 ± 0.8 in GM and 1.3 ± 0.4 in WM. EF (mL/100 mL/min) was close to zero in GM (−0.009 ± 0.05) and WM (−0.03 ± 0.08). Simulations show an overlap between CBF values of WM and GM, which is eliminated when Contrast‐to‐Noise (CNR) is improved. The model provides a consistent description of tracer kinetics in all brain tissues, and an accurate assessment of perfusion and permeability in reference tissues. The measurement sequence requires optimization to improve CNR and the precision in the perfusion parameters. With this approach, DCE‐MRI presents a promising alternative to DSC‐MRI for quantitative bolus‐tracking in the brain. Magn Reson Med, 2009.

Detailed pathophysiological manifestations of early disease in the context of prediabetes are poorly understood. This study aimed to evaluate the extent of early signs of metabolic and cardio-cerebrovascular complications affecting multiple organs in individuals with prediabetes. Subjects without a history of stroke, coronary artery disease, or peripheral artery disease were enrolled in a case-control study nested within the Cooperative Health Research in the Region of Augsburg (KORA) FF4 cohort and underwent comprehensive MRI assessment to characterize cerebral parameters (white matter lesions, microbleeds), cardiovascular parameters (carotid plaque, left ventricular function, and myocardial late gadolinium enhancement [LGE]), and metabolic parameters (hepatic proton-density fat fraction [PDFF] and subcutaneous and visceral abdominal fat). Among 400 subjects who underwent MRI, 103 subjects had prediabetes and 54 had established diabetes. Subjects with prediabetes had an increased risk for carotid plaque and adverse functional cardiac parameters, including reduced early diastolic filling rates as well as a higher prevalence of LGE compared with healthy control subjects. In addition, people with prediabetes had significantly elevated levels of PDFF and total and visceral fat. Thus, subjects with prediabetes show early signs of subclinical disease that include vascular, cardiac, and metabolic changes, as measured by whole-body MRI after adjusting for cardiometabolic risk factors.

Objectives: The aim of this study was to investigate whether radiomic analysis with random survival forests (RSFs) can predict overall survival from T1-weighted contrast-enhanced baseline magnetic resonance imaging (MRI) scans in a cohort of glioblastoma multiforme (GBM) patients with uniform treatment. Materials and Methods: This retrospective study was approved by the institutional review board and informed consent was waived. The MRI scans from 66 patients with newly diagnosed GBM from a previous prospective study were analyzed. Tumors were segmented manually on contrast-enhanced 3-dimensional T1-weighted images. Using these segmentations, P = 208 quantitative image features characterizing tumor shape, signal intensity, and texture were calculated in an automated fashion. On this data set, an RSF was trained using 10-fold cross validation to establish a link between image features and overall survival, and the individual risk for each patient was predicted. The mean concordance index was assessed as a measure of prediction accuracy. Association of individual risk with overall survival was assessed using Kaplan-Meier analysis and a univariate proportional hazards model. Results: Mean overall survival was 14 months (range, 0.8–85 months). Mean concordance index of the 10-fold cross-validated RSF was 0.67. Kaplan-Meier analysis clearly distinguished 2 patient groups with high and low predicted individual risk (P = 5.5 × 10⁻⁵). Low predicted individual mortality was found to be a favorable prognostic factor for overall survival in a univariate Cox proportional hazards model (hazards ratio, 1.038; 95% confidence interval, 1.015–1.062; P = 0.0059). Conclusions: This study demonstrates that baseline MRI in GBM patients contains prognostic information, which can be accessed by radiomic analysis using RSFs.