A 3D-printed phantom for quality-controlled reproducibility measurements of arterial spin labeled perfusion.

PURPOSE: To develop a portable MR perfusion phantom for quality-controlled assessment and reproducibility of arterial spin labeled (ASL) perfusion measurement. METHODS: A 3D-printed perfusion phantom was developed that mimics the branching of arterial vessels, capillaries, and a chamber containing cellulose sponge representing tissue characteristics. A peristaltic pump circulated distilled water through the phantom, and was first evaluated at 300, 400, and 500 mL/min. Longitudinal reproducibility of perfusion was performed using 2D pseudo-continuous ASL at 20 post-label delays (PLDs, ranging between 0.2 and 7.8 s at 0.4-s intervals) over a period of 16 weeks, with three repetitions each week. Multi-PLD data were fitted into a general kinetic model for perfusion quantification (f) and arterial transit time (ATT). Intraclass correlation coefficient was used to assess intersession reproducibility. RESULTS: MR perfusion signals acquired in the 3D-printed perfusion phantom agreed well with the experimental conditions, with progressively increasing signal intensities and decreasing ATT for pump flow rates from 300 to 500 mL/min. The perfusion signal at 400 mL/min and the general kinetic model-derived f and ATT maps were similar across all PLDs for both intrasession and intersession reproducibility. Across all 48 experimental time points, the average f was 75.55 ± 3.83 × 10-3 mL/mL/s, the corresponding ATT was 2.10 ± 0.20 s, and the T1 was 1.84 ± 0.102 s. Intraclass correlation coefficient was 0.92 (95% confidence interval 0.83-0.97) for f, 0.96 (0.91-0.99) for ATT, and 0.94 (0.88-0.98) for T1 , demonstrating excellent reproducibility. CONCLUSION: A simple, portable 3D-printed perfusion phantom with excellent reproducibility of 2D pseudo-continuous ASL measurements was demonstrated that can serve for quality-controlled and reliable measurements of ASL perfusion.

Multi-Site Concordance of Diffusion-Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness.

BACKGROUND: Diffusion-weighted imaging (DWI) is commonly used to detect prostate cancer, and a major clinical challenge is differentiating aggressive from indolent disease. PURPOSE: To compare 14 site-specific parametric fitting implementations applied to the same dataset of whole-mount pathologically validated DWI to test the hypothesis that cancer differentiation varies with different fitting algorithms. STUDY TYPE: Prospective. POPULATION: Thirty-three patients prospectively imaged prior to prostatectomy. FIELD STRENGTH/SEQUENCE: 3 T, field-of-view optimized and constrained undistorted single-shot DWI sequence. ASSESSMENT: Datasets, including a noise-free digital reference object (DRO), were distributed to the 14 teams, where locally implemented DWI parameter maps were calculated, including mono-exponential apparent diffusion coefficient (MEADC), kurtosis (K), diffusion kurtosis (DK), bi-exponential diffusion (BID), pseudo-diffusion (BID*), and perfusion fraction (F). The resulting parametric maps were centrally analyzed, where differentiation of benign from cancerous tissue was compared between DWI parameters and the fitting algorithms with a receiver operating characteristic area under the curve (ROC AUC). STATISTICAL TEST: Levene's test, P < 0.05 corrected for multiple comparisons was considered statistically significant. RESULTS: The DRO results indicated minimal discordance between sites. Comparison across sites indicated that K, DK, and MEADC had significantly higher prostate cancer detection capability (AUC range = 0.72-0.76, 0.76-0.81, and 0.76-0.80 respectively) as compared to bi-exponential parameters (BID, BID*, F) which had lower AUC and greater between site variation (AUC range = 0.53-0.80, 0.51-0.81, and 0.52-0.80 respectively). Post-processing parameters also affected the resulting AUC, moving from, for example, 0.75 to 0.87 for MEADC varying cluster size. DATA CONCLUSION: We found that conventional diffusion models had consistent performance at differentiating prostate cancer from benign tissue. Our results also indicated that post-processing decisions on DWI data can affect sensitivity and specificity when applied to radiological-pathological studies in prostate cancer. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 3.

In vivo MRS measurement of 2-hydroxyglutarate in patient-derived IDH-mutant xenograft mouse models versus glioma patients.

PURPOSE: To generate a preclinical model of isocitrate dehydrogenase (IDH) mutant gliomas from glioma patients and design a MRS method to test the compatibility of 2-hydroxyglutarate (2HG) production between the preclinical model and patients. METHODS: Five patient-derived xenograft (PDX) mice were generated from two glioma patients with IDH1 R132H mutation. A PRESS sequence was tailored at 9.4 T, with computer simulation and phantom analyses, for improving 2HG detection in mice. 2HG and other metabolites in the PDX mice were measured using the optimized MRS at 9.4 T and compared with 3 T MRS measurements of the metabolites in the parental-tumor patients. Spectral fitting was performed with LCModel using in-house basis spectra. Metabolite levels were quantified with reference to water. RESULTS: The PRESS TE was optimized to be 96 ms, at which the 2HG 2.25 ppm signal was narrow and inverted, thereby leading to unequivocal separation of the 2HG resonance from adjacent signals from other metabolites. The optimized MRS provided precise detection of 2HG in mice compared to short-TE MRS at 9.4 T. The 2HG estimates in PDX mice were in excellent agreement with the 2HG measurements in the patients. CONCLUSION: The similarity of 2HG production between PDX models and parental-tumor patients indicates that PDX tumors retain the parental IDH metabolic fingerprint and can serve as a preclinical model for improving our understanding of the IDH-mutation associated metabolic reprogramming.

Distortion correction in diffusion-weighted imaging of the breast: Performance assessment of prospective, retrospective, and combined (prospective + retrospective) approaches.

PURPOSE: To compare the effectiveness of prospective, retrospective, and combined (prospective + retrospective) EPI distortion correction methods in bilateral breast diffusion-weighted imaging (DWI) scans. METHODS: Five healthy female subjects underwent an axial bilateral breast DWI exam with and without prospective B0 inhomogeneity correction using slice-by-slice linear shimming. In each case, an additional b=0 DWI scan was performed with the polarity of the phase-encoding gradient reversed, to generate an estimated B0 map; this map or a separately acquired B0 map was used for retrospective correction, either alone or in combination with the prospective correction. The alignment between an undistorted, anatomical reference scan with similar contrast and the corrected b=0 DWI images with different correction schemes was assessed. RESULTS: The average cross-correlation coefficient between the DWI images and the anatomical reference scan was increased from 0.82 to 0.92 over the five volunteers when combined prospective and retrospective distortion correction was applied. Furthermore, such correction substantially reduced patient-to-patient variation of the image alignment and the variability of the average apparent diffusion coefficient in normal glandular tissue. CONCLUSION: Combined prospective and retrospective distortion correction can provide an efficient way to reduce susceptibility-induced image distortions and enhance the reliability of breast DWI exams. Magn Reson Med 78:247-253, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

¹H MRS characterization of neurochemical profiles in orthotopic mouse models of human brain tumors.

Glioblastoma (GBM), the most common primary brain tumor, is resistant to currently available treatments. The development of mouse models of human GBM has provided a tool for studying mechanisms involved in tumor initiation and growth as well as a platform for preclinical investigation of new drugs. In this study we used (1) H MR spectroscopy to study the neurochemical profile of a human orthotopic tumor (HOT) mouse model of human GBM. The goal of this study was to evaluate differences in metabolite concentrations in the GBM HOT mice when compared with normal mouse brain in order to determine if MRS could reliably differentiate tumor from normal brain. A TE =19 ms PRESS sequence at 9.4 T was used for measuring metabolite levels in 12 GBM mice and 8 healthy mice. Levels for 12 metabolites and for lipids/macromolecules at 0.9 ppm and at 1.3 ppm were reliably detected in all mouse spectra. The tumors had significantly lower concentrations of total creatine, GABA, glutamate, total N-acetylaspartate, aspartate, lipids/macromolecules at 0.9 ppm, and lipids/macromolecules at 1.3 ppm than did the brains of normal mice. The concentrations of glycine and lactate, however, were significantly higher in tumors than in normal brain.

Effect of normal aging versus hypertension, abnormal body mass index, and diabetes mellitus on white matter hyperintensity volume.

BACKGROUND AND PURPOSE: The natural history of white matter hyperintensity (WMH) progression resulting from normal aging versus comorbid vascular insults remains unclear. Therefore we investigated age-related differences in WMH volumes among a group with comorbid hypertension, abnormal body mass index, and diabetes mellitus to a normal aging group drawn from the same population lacking any of these comorbidities. METHODS: WMH volumes were acquired using 3T MRI for 2011 Dallas Heart Study participants. The slope of the WMH versus age regression was compared between normal and comorbidity groups<50 and ≥50 years of age where a change in slope was demonstrated. RESULTS: Aging was linearly associated with greater log WMH volume for both normal (P=0.02) and comorbidity (P<0.0001) groups. Beyond 50 years of age, more rapid increases in WMH volumes for age were seen in the group with comorbidities (P<0.0001) but not in the normal group (P=0.173). The between-group difference in slope of expected WMH for age was significantly greater in the comorbidity groups≥50 years of age (P=0.0008) but not <50 years of age (P=0.752). CONCLUSIONS: After 50 years of age, but not before, comorbid hypertension, obesity, and diabetes mellitus were associated with significantly larger WMH volumes for age compared with a normal aging group lacking these conditions. These results support the assertion that age-related differences in WMH volumes are significantly increased in the presence of comorbidities, but the effect is only detectable after 50 years of age.

In vivo detection of citrate in brain tumors by 1H magnetic resonance spectroscopy at 3T.

PURPOSE: To test whether citrate is elevated in adult patients with gliomas using (1)H magnetic resonance spectroscopy (MRS) at 3T in vivo. METHODS: Thirty-four adult patients were enrolled in the study, including six subjects with glioblastomas, eight subjects with astrocytomas (World Health Organization grade 3, n = 5; grade 2, n = 3), and 20 subjects with oligodendrogliomas (grade 3, n = 5; grade 2, n = 15). Five healthy volunteers were studied for baseline citrate data. Single-voxel localized spectra were collected with point-resolved spectroscopy (PRESS) echo times of 35 and 97 ms and were analyzed with LCModel software using numerically calculated basis spectra that included the effects of the PRESS radiofrequency and gradient pulses. RESULTS: Citrate was not measurable by MRS in healthy brain but was detected in tumor patients at both echo times. The citrate concentration was estimated to be as high as 1.8 mM with reference to water at 42 M, with Cramér-Rao lower bounds (CRLB) as low as 5%. The mean citrate level was 0.7 ± 0.4 mM (mean ± SD, n = 32) with a median CRLB of ∼12%. No correlation was identified between citrate concentration and tumor grade or histological type. CONCLUSION: Citrate was increased in the majority of gliomas in adult patients. The elevated citrate in our data indicates an altered metabolic state of tumor relative to healthy brain.

Measurement of regional variation of GABA in the human brain by optimized point-resolved spectroscopy at 7 T in vivo.

The (1)H resonances of γ-aminobutyric acid (GABA) in the human brain in vivo are extensively overlapped with the neighboring abundant resonances of other metabolites and remain indiscernible in short-TE MRS at 7 T. Here we report that the GABA resonance at 2.28 ppm can be fully resolved by means of echo time optimization of a point-resolved spectroscopy (PRESS) scheme. Following numerical simulations and phantom validation, the subecho times of PRESS were optimized at (TE, TE2) = (31, 61) ms for detection of GABA, glutamate (Glu), glutamine (Gln), and glutathione (GSH). The in vivo feasibility of the method was tested in several brain regions in nine healthy subjects. Spectra were acquired from the medial prefrontal, left frontal, medial occipital, and left occipital brain and analyzed with LCModel. Following the gray and white matter (GM and WM) segmentation of T1 -weighted images, linear regression of metabolite estimates was performed against the fractional GM contents. The GABA concentration was estimated to be about seven times higher in GM than in WM. GABA was overall higher in frontal than in occipital brain. Glu was about twice as high in GM as in WM in both frontal and occipital brain. Gln was significantly different between frontal GM and WM while being similar between occipital GM and WM. GSH did not show significant dependence on tissue content. The signals from N-acetylaspartylglutamate were clearly resolved, giving the concentration more than 10 times higher in WM than in GM. Our data indicate that the PRESS TE = 92 ms method provides an effective means for measuring GABA and several challenging J-coupled spin metabolites in human brain at 7 T.

Radiomics Analysis of Contrast-Enhanced Breast MRI for Optimized Modelling of Virtual Prognostic Biomarkers in Breast Cancer.

Objective: Breast cancer clinical stage and nodal status are the most clinically significant drivers of patient management, in combination with other pathological biomarkers, such as estrogen receptor (ER), progesterone receptor or human epidermal growth factor receptor 2 (HER2) receptor status and tumor grade. Accurate prediction of such parameters can help avoid unnecessary intervention, including unnecessary surgery. The objective was to investigate the role of magnetic resonance imaging (MRI) radiomics for yielding virtual prognostic biomarkers (ER, HER2 expression, tumor grade, molecular subtype, and T-stage). Materials and Methods: Patients with primary invasive breast cancer who underwent dynamic contrast-enhanced (DCE) breast MRI between July 2013 and July 2016 in a single center were retrospectively reviewed. Age, N-stage, grade, ER and HER2 status, and Ki-67 (%) were recorded. DCE images were segmented and Haralick texture features were extracted. The Bootstrap Lasso feature selection method was used to select a small subset of optimal texture features. Classification of the performance of the final model was assessed with the area under the receiver operating characteristic curve (AUC). Results: Median age of patients (n = 209) was 49 (21-79) years. Sensitivity, specificity, positive predictive value, negative predictive value and accuracy of the model for differentiating N0 vs N1-N3 was: 71%, 79%, 76%, 74%, 75% [AUC = 0.78 (95% confidence interval (CI) 0.72-0.85)], N0-N1 vs N2-N3 was 81%, 59%, 24%, 95%, 62% [AUC = 0.74 (95% CI 0.63-0.85)], distinguishing HER2(+) from HER2(-) was 79%, 48%, 34%, 87%, 56% [AUC = 0.64 (95% CI 0.54-0.73)], high nuclear grade (grade 2-3) vs low grade (grades 1) was 56%, 88%, 96%, 29%, 61% [AUC = 0.71 (95% CI 0.63-0.80)]; and for ER (+) vs ER(-) status the [AUC=0.67 (95% CI 0.59-0.76)]. Radiomics performance in distinguishing triple-negative vs other molecular subtypes was [0.60 (95% CI 0.49-0.71)], and Luminal A [0.66 (95% CI 0.56-0.76)]. Conclusion: Quantitative radiomics using MRI contrast texture shows promise in identifying aggressive high grade, node positive triple negative breast cancer, and correlated well with higher nuclear grades, higher T-stages, and N-positive stages.

Machine Learning Prediction of Lymph Node Metastasis in Breast Cancer: Performance of a Multi-institutional MRI-based 4D Convolutional Neural Network.

Purpose To develop a custom deep convolutional neural network (CNN) for noninvasive prediction of breast cancer nodal metastasis. Materials and Methods This retrospective study included patients with newly diagnosed primary invasive breast cancer with known pathologic (pN) and clinical nodal (cN) status who underwent dynamic contrast-enhanced (DCE) breast MRI at the authors' institution between July 2013 and July 2016. Clinicopathologic data (age, estrogen receptor and human epidermal growth factor 2 status, Ki-67 index, and tumor grade) and cN and pN status were collected. A four-dimensional (4D) CNN model integrating temporal information from dynamic image sets was developed. The convolutional layers learned prognostic image features, which were combined with clinicopathologic measures to predict cN0 versus cN+ and pN0 versus pN+ disease. Performance was assessed with the area under the receiver operating characteristic curve (AUC), with fivefold nested cross-validation. Results Data from 350 female patients (mean age, 51.7 years ± 11.9 [SD]) were analyzed. AUC, sensitivity, and specificity values of the 4D hybrid model were 0.87 (95% CI: 0.83, 0.91), 89% (95% CI: 79%, 93%), and 76% (95% CI: 68%, 88%) for differentiating pN0 versus pN+ and 0.79 (95% CI: 0.76, 0.82), 80% (95% CI: 77%, 84%), and 62% (95% CI: 58%, 67%), respectively, for differentiating cN0 versus cN+. Conclusion The proposed deep learning model using tumor DCE MR images demonstrated high sensitivity in identifying breast cancer lymph node metastasis and shows promise for potential use as a clinical decision support tool. Keywords: MR Imaging, Breast, Breast Cancer, Breast MRI, Machine Learning, Metastasis, Prognostic Prediction Supplemental material is available for this article. Published under a CC BY 4.0 license.

White matter hyperintensities: use of aortic arch pulse wave velocity to predict volume independent of other cardiovascular risk factors.

PURPOSE: To evaluate the relationship between pulse wave velocity (PWV) from the aortic arch and subsequent cerebral microvascular disease independent of other baseline cardiovascular risk factors among the participants in the multiethnic Dallas Heart Study. MATERIALS AND METHODS: Each subject gave written consent to participate in this HIPAA-compliant, institutional review board-approved prospective study. Aortic arch PWV was measured with phase-contrast magnetic resonance (MR) imaging in a population sample (n = 1270) drawn from the probability-based Dallas Heart Study. Seven years later, the volume of white matter hyperintensities (WMHs) was determined from brain MR images. Linear regression was conducted with aortic arch PWV, 15 other cardiovascular risk factors, and age, sex, and ethnicity included as predictors of WMH. The authors implemented a smoothly clipped absolute deviation-penalized variable selection method to evaluate an optimal predictive risk factor model. RESULTS: Aortic arch PWV helped predict WMH volume independent of the other demographic and cardiovascular risk factors (regression coefficient: 0.29; standard error: 0.06; 95% confidence interval: 0.17, 0.42; P < .0001). The optimal predictor variables of subsequent WMH volume adjusted for sex and ethnicity included aortic arch PWV, age, systolic blood pressure, hypertension treatment, and congestive heart failure. The authors estimated that a 1% increase in aortic arch PWV (in meters per second) is related to a 0.3% increase in subsequent WMH volume (in milliliters) when all other variables in the model are held constant. CONCLUSION: Aortic arch PWV measured with phase-contrast MR imaging is a highly significant independent predictor of subsequent WMH volume, with a higher standardized effect than any other cardiovascular risk factor assessed except for age. In an optimal predictive model of subsequent WMH burden, aortic arch PWV provides a distinct contribution along with systolic blood pressure, hypertension treatment, congestive heart failure, and age.

A comparative study of short- and long-TE ¹H MRS at 3 T for in vivo detection of 2-hydroxyglutarate in brain tumors.

2-Hydroxyglutarate (2HG) is produced in gliomas with mutations of isocitrate dehydrogenase (IDH) 1 and 2. The (1) H resonances of the J-coupled spins of 2HG are extensively overlapped with signals from other metabolites. Here, we report a comparative study at 3 T of the utility of the point-resolved spectroscopy sequence with a standard short TE (35 ms) and a long TE (97 ms), which had been theoretically designed for the detection of the 2HG 2.25-ppm resonance. The performance of the methods is evaluated using data from phantoms, seven healthy volunteers and 22 subjects with IDH-mutated gliomas. The results indicate that TE = 97 ms provides higher detectability of 2HG than TE = 35 ms, and that this improved capability is gained when data are analyzed with basis spectra that include the effects of the volume localizing radiofrequency and gradient pulses.

Measurement of glycine in gray and white matter in the human brain in vivo by 1H MRS at 7.0 T.

The concentration of glycine (Gly) was measured in gray matter (GM) and white matter (WM) in the human brain using single-voxel localized (1)H MRS at 7 T. A point-resolved spectroscopy sequence with echo time = 150 ms was used for measuring Gly levels in various regions of the frontal and occipital lobes in 11 healthy volunteers and one subject with a glioblastoma. The point-resolved spectroscopy spectra were analyzed with LCModel using basis functions generated from density matrix simulations that included the effects of volume localized radio-frequency and gradient pulses. The fraction of GM and white matter within the voxels was obtained from T(1)-weighted image segmentation. The metabolite concentrations within the voxels, estimated with respect to the GM + WM water concentrations, were fitted to a linear function of fractional GM content. The Gly concentrations in pure GM and white matter were estimated to be 1.1 and 0.1 mM, with 95% confidence intervals 1.0-1.2 and 0.0-0.2, respectively.

Automated quantification of white matter disease extent at 3 T: comparison with volumetric readings.

PURPOSE: To develop and validate an algorithm to automatically quantify white matter hyperintensity (WMH) volume. MATERIALS AND METHODS: Images acquired as part of the Dallas Heart Study, a multiethnic, population-based study of cardiovascular health, were used to develop and validate the algorithm. 3D magnetization prepared rapid acquisition gradient echo (MP-RAGE) and 2D fluid-attenuated inversion recovery (FLAIR) images were acquired from 2082 participants. Images from 161 participants (7.7% of the cohort) were used to set an intensity threshold to maximize the agreement between the algorithm and a qualitative rating made by a radiologist. The resulting algorithm was run on the entire cohort and outlier analyses were used to refine the WMH volume measurement. The refined, automatic WMH burden estimate was then compared to manual quantitative measurements of WMH volume in 28 participants distributed across the range of volumes seen in the entire cohort. RESULTS: The algorithm showed good agreement with the volumetric readings of a trained analyst: the Spearman's Rank Order Correlation coefficient was r = 0.87. Linear regression analysis showed a good correlation WMHml[automated] = 1.02 × WMHml[manual] - 0.48. Bland-Altman analysis showed a bias of 0.34 mL and a standard deviation of 2.8 mL over a range of 0.13 to 41 mL. CONCLUSION: We have developed an algorithm that automatically estimates the volume of WMH burden using an MP-RAGE and a FLAIR image. This provides a tool for evaluating the WMH burden of large populations to investigate the relationship between WMH burden and other health factors.

Preoperative Prediction of Lymph Node Metastasis from Clinical DCE MRI of the Primary Breast Tumor Using a 4D CNN.

In breast cancer, undetected lymph node metastases can spread to distal parts of the body for which the 5-year survival rate is only 27%, making accurate nodal metastases diagnosis fundamental to reducing the burden of breast cancer, when it is still early enough to intervene with surgery and adjuvant therapies. Currently, breast cancer management entails a time consuming and costly sequence of steps to clinically diagnose axillary nodal metastases status. The purpose of this study is to determine whether preoperative, clinical DCE MRI of the primary tumor alone may be used to predict clinical node status with a deep learning model. If possible then many costly steps could be eliminated or reserved for only those with uncertain or probable nodal metastases. This research develops a data-driven approach that predicts lymph node metastasis through the judicious integration of clinical and imaging features from preoperative 4D dynamic contrast enhanced (DCE) MRI of 357 patients from 2 hospitals. Innovative deep learning classifiers are trained from scratch, including 2D, 3D, 4D and 4D deep convolutional neural networks (CNNs) that integrate multiple data types and predict the nodal metastasis differentiating nodal stage N0 (non metastatic) against stages N1, N2 and N3. Appropriate methodologies for data preprocessing and network interpretation are presented, the later of which bolster radiologist confidence that the model has learned relevant features from the primary tumor. Rigorous nested 10-fold cross-validation provides an unbiased estimate of model performance. The best model achieves a high sensitivity of 72% and an AUROC of 71% on held out test data. Results are strongly supportive of the potential of the combination of DCE MRI and machine learning to inform diagnostics that could substantially reduce breast cancer burden.

Mechanisms of Action of Liraglutide in Patients With Type 2 Diabetes Treated With High-Dose Insulin.

CONTEXT: The mechanisms of action of incretin mimetics in patients with long-standing type 2 diabetes (T2D) and high insulin requirements have not been studied. OBJECTIVE: To evaluate changes in ß-cell function, glucagon secretion, and fat distribution after addition of liraglutide to high-dose insulin. DESIGN: A single-center, randomized, double-blind, placebo-controlled trial. SETTING: University of Texas Southwestern and Parkland Memorial Hospital clinics. PATIENTS: Seventy-one patients with long-standing (median, 17 years) T2D requiring high-dose insulin treatment (>1.5 U/kg/d; average, 2.2 ± 0.9 U/kg/d). INTERVENTION: Patients were randomized to liraglutide 1.8 mg/d or matching placebo for 6 months. MAIN OUTCOME MEASURES: We measured changes in insulin and glucagon secretion using a 4-hour mixed-meal challenge test. Magnetic resonance-based techniques were used to estimate sc and visceral fat in the abdomen and ectopic fat in the liver and pancreas. RESULTS: Glycosylated hemoglobin improved significantly with liraglutide treatment, with an end-of-trial estimated treatment difference between groups of −0.9% (95% confidence interval, −1.5, −0.4%) (P = .002). Insulin secretion improved in the liraglutide group vs placebo, as measured by the area under the curve of C-peptide (P = .002) and the area under the curves ratio of C-peptide to glucose (P = .003). Insulin sensitivity (Matsuda index) and glucagon secretion did not change significantly between groups. Liver fat and sc fat decreased in the liraglutide group vs placebo (P = .0006 and P = .01, respectively), whereas neither visceral nor pancreatic fat changed significantly. CONCLUSIONS: Treatment with liraglutide significantly improved insulin secretion, even in patients with long-standing T2D requiring high-dose insulin treatment. Liraglutide also decreased liver and sc fat, but it did not alter glucagon secretion.

Prospective Longitudinal Analysis of 2-Hydroxyglutarate Magnetic Resonance Spectroscopy Identifies Broad Clinical Utility for the Management of Patients With IDH-Mutant Glioma.

Purpose Proton magnetic resonance spectroscopy (MRS) of the brain can detect 2-hydroxyglutarate (2HG), the oncometabolite produced in neoplasms harboring a mutation in the gene coding for isocitrate dehydrogenase ( IDH). We conducted a prospective longitudinal imaging study to determine whether quantitative assessment of 2HG by MRS could serve as a noninvasive clinical imaging biomarker for IDH-mutated gliomas. Patients and Methods 2HG MRS was performed in 136 patients using point-resolved spectroscopy at 3 T in parallel with standard clinical magnetic resonance imaging and assessment. Data were analyzed in patient cohorts representing the major phases of the glioma clinical course and were further subgrouped by histology and treatment type to evaluate 2HG. Histologic correlations were performed. Results Quantitative 2HG MRS was technically and biologically reproducible. 2HG concentration > 1 mM could be reliably detected with high confidence. During the period of indolent disease, 2HG concentration varied by less than ± 1 mM, and it increased sharply with tumor progression. 2HG concentration was positively correlated with tumor cellularity and significantly differed between high- and lower-grade gliomas. In response to cytotoxic therapy, 2HG concentration decreased rapidly in 1p/19q codeleted oligodendrogliomas and with a slower time course in astrocytomas and mixed gliomas. The magnitude and time course of the decrease in 2HG concentration and magnitude of the decrease in tumor volume did not differ between oligodendrogliomas treated with temozolomide or carmustine. Criteria for 2HG MRS were established to make a presumptive molecular diagnosis of an IDH mutation in gliomas technically unable to undergo a surgical procedure. Conclusion 2HG concentration as measured by MRS was reproducible and reliably reflected the disease state. These data provide a basis for incorporating 2HG MRS into clinical management of IDH-mutated gliomas.

Association of 3.0-T brain magnetic resonance imaging biomarkers with cognitive function in the Dallas Heart Study.

IMPORTANCE: Understanding the relationships between age-related changes in brain structure and cognitive function has been limited by inconsistent methods for assessing brain imaging, small sample sizes, and racially/ethnically homogeneous cohorts with biased selection based on risk factors. These limitations have prevented the generalizability of results from brain morphology studies. OBJECTIVE: To determine the association of 3.0-T structural brain magnetic resonance (MR) imaging measurements with cognitive function in the multiracial/multiethnic, population-based Dallas Heart Study. DESIGN, SETTING, AND PARTICIPANTS: Whole-brain, 2-dimensional, fluid-attenuated inversion recovery and 3-dimensional, magnetization-prepared, rapid acquisition with gradient echo MR imaging at 3.0 T was performed in 1645 Dallas Heart Study participants (mean [SD] age, 49.9 [10.5] years; age range, 19-85 years) who received both brain MR imaging and cognitive screening with the Montreal Cognitive Assessment between September 18, 2007, and December 28, 2009. Measurements were obtained for white matter hyperintensity volume, total brain volume, gray matter volume, white matter volume, cerebrospinal fluid volume, and hippocampal volume. Linear regression and a best predictive model were developed to determine the association of MR imaging biomarkers with the Montreal Cognitive Assessment total score and domain-specific questions. MAIN OUTCOMES AND MEASURES: High-resolution anatomical MR imaging was used to quantify brain volumes. Scores on the screening Montreal Cognitive Assessment were used for cognitive assessment in participants. RESULTS: After adjustment for demographic variables, total brain volume (P < .0001, standardized estimate [SE] = .1069), gray matter volume (P < .0001, SE = .1156), white matter volume (P = .008, SE = .0687), cerebrospinal fluid volume (P = .012, SE = -.0667), and hippocampal volume (P < .0001) were significantly associated with cognitive performance. A best predictive model identified gray matter volume (P < .001, SE = .0021), cerebrospinal fluid volume (P = .01, SE = .0024), and hippocampal volume (P = .004, SE = .1017) as 3 brain MR imaging biomarkers significantly associated with the Montreal Cognitive Assessment total score. Questions specific to the visuospatial domain were associated with the most brain MR imaging biomarkers (total brain volume, gray matter volume, white matter volume, cerebrospinal fluid volume, and hippocampal volume), while questions specific to the orientation domain were associated with the least brain MR imaging biomarkers (only hippocampal volume). CONCLUSIONS AND RELEVANCE: Brain MR imaging volumes, including total brain volume, gray matter volume, cerebrospinal fluid volume, and hippocampal volume, were independently associated with cognitive function and may be important early biomarkers of risk for cognitive insult in a young multiracial/multiethnic population. A best predictive model indicated that a combination of multiple neuroimaging biomarkers may be more effective than a single brain MR imaging volume measurement.

Urinary albumin to creatinine ratio as potential biomarker for cerebral microvascular disease.

Elevated urinary albumin to creatinine ratio (ACR) and white matter hyperintensity (WMH) volume seen on brain MRI are measures of microvascular disease which may have shared susceptibility to metabolic and vascular insults. We hypothesized that elevated ACR may be useful as inexpensive biomarker to predict presence of cerebral microvascular disease. We assessed the association between ACR at study entry and subsequent WMH volume. We evaluated pulse pressure, mean arterial pressure, hypertension duration, waist circumference, fasting glucose, glomerular filtration rate (GFR) and C-reactive protein (CRP) as potential mediators and diabetes as a moderator of the association between ACR and WMH. Data were collected at study entry and at follow-up approximately 7 years later in a multiethnic population sample of 1281 participants (mean age = 51, SD = 9.5) from Dallas County. Overall, ACR differences were only marginally (p = 0.05) associated with subsequent WMH. In mediator analysis, however, ACR differences related specifically to arterial pulsatility(ß = 0.010, bootstrap 95% Confidence Interval (CI): 0.002 to 0.021) and waist circumference (ß = -0.004, bootstrap 95% CI: -0.011 to -0.001) were significantly associated with WMH. ACR differences related to serum glucose and CRP were not associated with WMH. ACR evaluated at the same time as WMH had a higher level of significance (p < 0.001) indicating greater utility in predicting current cerebrovascular insults.