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Background Cardiac MRI is limited by long acquisition times, yet faster acquisition of smaller-matrix images reduces spatial detail. Deep learning (DL) might enable both faster acquisition and higher spatial detail via super-resolution. Purpose To explore the feasibility of using DL to enhance spatial detail from small-matrix MRI acquisitions and evaluate its performance against that of conventional image upscaling methods. Materials and Methods Short-axis cine cardiac MRI examinations performed between January 2012 and December 2018 at one institution were retrospectively collected for algorithm development and testing. Convolutional neural networks (CNNs), a form of DL, were trained to perform super resolution in image space by using synthetically generated low-resolution data. There were 70%, 20%, and 10% of examinations allocated to training, validation, and test sets, respectively. CNNs were compared against bicubic interpolation and Fourier-based zero padding by calculating the structural similarity index (SSIM) between high-resolution ground truth and each upscaling method. Means and standard deviations of the SSIM were reported, and statistical significance was determined by using the Wilcoxon signed-rank test. For evaluation of clinical performance, left ventricular volumes were measured, and statistical significance was determined by using the paired Student t test. Results For CNN training and retrospective analysis, 400 MRI scans from 367 patients (mean age, 48 years ± 18; 214 men) were included. All CNNs outperformed zero padding and bicubic interpolation at upsampling factors from two to 64 (P < .001). CNNs outperformed zero padding on more than 99.2% of slices (9828 of 9907). In addition, 10 patients (mean age, 51 years ± 22; seven men) were prospectively recruited for super-resolution MRI. Super-resolved low-resolution images yielded left ventricular volumes comparable to those from full-resolution images (P > .05), and super-resolved full-resolution images appeared to further enhance anatomic detail. Conclusion Deep learning outperformed conventional upscaling methods and recovered high-frequency spatial information. Although training was performed only on short-axis cardiac MRI examinations, the proposed strategy appeared to improve quality in other imaging planes. © RSNA, 2020 Online supplemental material is available for this article.

Immunotherapy is increasingly used in the treatment of glioblastoma (GBM), with immune checkpoint therapy gaining in popularity given favorable outcomes achieved for other tumors. However, immune-mediated (IM)-pseudoprogression is common, remains poorly characterized, and renders conventional imaging of little utility when evaluating for treatment response. We present the case of a 64-year-old man with GBM who developed pathologically proven IM-pseudoprogression after initiation of a checkpoint inhibitor, and who subsequently developed true tumor progression at a distant location. Based on both qualitative and quantitative analysis, we demonstrate that an advanced diffusion-weighted imaging (DWI) technique called restriction spectrum imaging (RSI) can differentiate IM-pseudoprogression from true progression even when conventional imaging, including standard DWI/apparent diffusion coefficient (ADC), is not informative. These data complement existing literature supporting the ability of RSI to estimate tumor cellularity, which may help to resolve complex diagnostic challenges such as the identification of IM-pseudoprogression.

IntroductionWe investigated multi-domain baseline neurocognition of primary brain tumor patients prior to radiotherapy (RT), including clinical predictors of function and association between pre-RT and post-RT impairment on a prospective trial.MethodsA multi-domain neuropsychological battery (memory, executive functioning, language, attention, processing) was performed on 37 patients, pre-RT and 3-(n = 21), 6-(n = 22) and 12-(n = 14) months post-RT. Impairment rate was the proportion of patients with standardized T-scores ≤ 1.5 standard deviations below normative means. Per-patient impairment across all domains was calculated using a global deficit score (GDS; higher value indicates more impairment). Associations between baseline GDS and clinical variables were tested. Global GDS impairment rate at each time point was the fraction of patients with GDS scores > 0.5.ResultsStatistically significant baseline neurocognitive impairments were identified on 4 memory (all p ≤ 0.03) and 2 out of 3 (p = 0.01, p = 0.027) executive functioning tests. Per-patient baseline GDS was significantly associated with tumor volume (p = 0.048), tumor type (p = 0.043), seizure history (p = 0.007), and use of anti-epileptics (p = 0.009). The percentage of patients with the same impairment status at 3-, 6-, and 12-months as at baseline were 88%, 85%, and 85% respectively.ConclusionsMemory and executive functioning impairment were the most common cognitive deficits prior to RT. Patients with larger tumors, more aggressive histology, and use of anti-epileptics had higher baseline GDS values. GDS is a promising tool to encompass multi-domain neurocognitive function, and baseline GDS can identify those at risk of cognitive impairment.

ObjectiveThe distributed white matter network underlying language leads to difficulties in extracting clinically meaningful summaries of neural alterations leading to language impairment. Here we determine the predictive ability of the structural connectome (SC), compared with global measures of white matter tract microstructure and clinical data, to discriminate language impaired patients with temporal lobe epilepsy (TLE) from TLE patients without language impairment.MethodsT1- and diffusion-MRI, clinical variables (CVs), and neuropsychological measures of naming and verbal fluency were available for 82 TLE patients. Prediction of language impairment was performed using a robust tree-based classifier (XGBoost) for three models: (1) a CV-model which included demographic and epilepsy-related clinical features, (2) an atlas-based tract-model, including four frontotemporal white matter association tracts implicated in language (i.e., the bilateral arcuate fasciculus, inferior frontal occipital fasciculus, inferior longitudinal fasciculus, and uncinate fasciculus), and (3) a SC-model based on diffusion MRI. For the association tracts, mean fractional anisotropy was calculated as a measure of white matter microstructure for each tract using a diffusion tensor atlas (i.e., AtlasTrack). The SC-model used measurement of cortical-cortical connections arising from a temporal lobe subnetwork derived using probabilistic tractography. Dimensionality reduction of the SC was performed with principal components analysis (PCA). Each model was trained on 49 patients from one epilepsy center and tested on 33 patients from a different center (i.e., an independent dataset). Randomization was performed to test the stability of the results.ResultsThe SC-model yielded a greater area under the curve (AUC; .73) and accuracy (79%) compared to both the tract-model (AUC: .54, p

PurposeTo develop and evaluate a system to prescribe imaging planes for cardiac MRI based on deep learning (DL)-based localization of key anatomic landmarks.Materials and methodsAnnotated landmarks on 892 long-axis (LAX) and 493 short-axis (SAX) cine steady-state free precession series from cardiac MR images were retrospectively collected between February 2012 and June 2017. U-Net-based heatmap regression was used for localization of cardiac landmarks, which were used to compute cardiac MRI planes. Performance was evaluated by comparing localization distances and plane angle differences between DL predictions and ground truth. The plane angulations from DL were compared with those prescribed by the technologist at the original time of acquisition. Data were split into 80% for training and 20% for testing, and results confirmed with fivefold cross-validation.ResultsOn LAX images, DL localized the apex within mean 12.56 mm ± 19.11 (standard deviation) and the mitral valve (MV) within 7.68 mm ± 6.91. On SAX images, DL localized the aortic valve within 5.78 mm ± 5.68, MV within 5.90 mm ± 5.24, pulmonary valve within 6.55 mm ± 6.39, and tricuspid valve within 6.39 mm ± 5.89. On the basis of these localizations, average angle bias and mean error of DL-predicted imaging planes relative to ground truth annotations were as follows: SAX, -1.27° ± 6.81 and 4.93° ± 4.86; four chambers, 0.38° ± 6.45 and 5.16° ± 3.80; three chambers, 0.13° ± 12.70 and 9.02° ± 8.83; and two chamber, 0.25° ± 9.08 and 6.53° ± 6.28, respectively.ConclusionDL-based anatomic localization is a feasible strategy for planning cardiac MRI planes. This approach can produce imaging planes comparable to those defined by ground truth landmarks.© RSNA, 2019 Supplemental material is available for this article.

PurposeDelayed enhancement imaging is an essential component of cardiac MRI, which is used widely for the evaluation of myocardial scar and viability. The selection of an optimal inversion time (TI) or null point (TINP ) to suppress the background myocardial signal is required. The purpose of this study was to assess the feasibility of automated selection of TINP using a convolutional neural network (CNN). We hypothesized that a CNN may use spatial and temporal imaging characteristics from an inversion-recovery scout to select TINP , without the aid of a human observer.MethodsWe retrospectively collected 425 clinically acquired cardiac MRI exams performed at 1.5 T that included inversion-recovery scout acquisitions. We developed a VGG19 classifier ensembled with long short-term memory to identify the TINP . We compared the performance of the ensemble CNN in predicting TINP against ground truth, using linear regression analysis. Ground truth was defined as the expert physician annotation of the optimal TI. In a backtrack approach, saliency maps were generated to interpret the classification outcome and to increase the model's transparency.ResultsPrediction of TINP from our ensemble VGG19 long short-term memory closely matched with expert annotation (ρ = 0.88). Ninety-four percent of the predicted TINP were within ±36 ms, and 83% were at or after expert TI selection.ConclusionIn this study, we show that a CNN is capable of automated prediction of myocardial TI from an inversion-recovery experiment. Merging the spatial and temporal characteristics of the VGG-19 and long short-term-memory CNN structures appears to be sufficient to predict myocardial TI from TI scout.

ObjectivesTemporal lobe epilepsy (TLE) is known to affect large-scale gray and white matter networks, and these network changes likely contribute to the verbal memory impairments observed in many patients. In this study, we investigate multimodal imaging patterns of brain alterations in TLE and evaluate the sensitivity of different imaging measures to verbal memory impairment.MethodsDiffusion tensor imaging (DTI), volumetric magnetic resonance imaging (vMRI), and resting-state functional MRI (rs-fMRI) were evaluated in 46 patients with TLE and 33 healthy controls to measure patterns of microstructural, structural, and functional alterations, respectively. These measurements were obtained within the white matter directly beneath neocortex (ie, superficial white matter [SWM]) for DTI and across neocortex for vMRI and rs-fMRI. The degree to which imaging alterations within left medial temporal lobe/posterior cingulate (LMT/PC) and left lateral temporal regions were associated with verbal memory performance was evaluated.ResultsPatients with left TLE and right TLE both demonstrated pronounced microstructural alterations (ie, decreased fractional anisotropy [FA] and increased mean diffusivity [MD]) spanning the entire frontal and temporolimbic SWM, which were highly lateralized to the ipsilateral hemisphere. Conversely, reductions in cortical thickness in vMRI and alterations in the magnitude of the rs-fMRI response were less pronounced and less lateralized than the microstructural changes. Both stepwise regression and mediation analyses further revealed that FA and MD within SWM in LMT/PC regions were the most robust predictors of verbal memory, and that these associations were independent of left hippocampal volume.SignificanceThese findings suggest that microstructural loss within the SWM is pronounced in patients with TLE, and injury to the SWM within the LMT/PC region plays a critical role in verbal memory impairment.

OBJECTIVE:To identify distinct cognitive phenotypes in temporal lobe epilepsy (TLE) and evaluate patterns of white matter (WM) network alterations associated with each phenotype. METHODS:Seventy patients with TLE were characterized into 4 distinct cognitive phenotypes based on patterns of impairment in language and verbal memory measures (language and memory impaired, memory impaired only, language impaired only, no impairment). Diffusion tensor imaging was obtained in all patients and in 46 healthy controls (HC). Fractional anisotropy (FA) and mean diffusivity (MD) of the WM directly beneath neocortex (i.e., superficial WM [SWM]) and of deep WM tracts associated with memory and language were calculated for each phenotype. Regional and network-based SWM analyses were performed across phenotypes. RESULTS:The language and memory impaired group and the memory impaired group showed distinct patterns of microstructural abnormalities in SWM relative to HC. In addition, the language and memory impaired group showed widespread alterations in WM tracts and altered global SWM network topology. Patients with isolated language impairment exhibited poor network structure within perisylvian cortex, despite relatively intact global SWM network structure, whereas patients with no impairment appeared similar to HC across all measures. CONCLUSIONS:These findings demonstrate a differential pattern of WM microstructural abnormalities across distinct cognitive phenotypes in TLE that can be appreciated at both the regional and network levels. These findings not only help to unravel the underlying neurobiology associated with cognitive impairment in TLE, but they could also aid in establishing cognitive taxonomies or in the prediction of cognitive course in TLE.

PurposeTo assess feasibility of training a convolutional neural network (CNN) to automate liver segmentation across different imaging modalities and techniques used in clinical practice and apply this to enable automation of liver biometry.MethodsWe trained a 2D U-Net CNN for liver segmentation in two stages using 330 abdominal MRI and CT exams acquired at our institution. First, we trained the neural network with non-contrast multi-echo spoiled-gradient-echo (SGPR)images with 300 MRI exams to provide multiple signal-weightings. Then, we used transfer learning to generalize the CNN with additional images from 30 contrast-enhanced MRI and CT exams.We assessed the performance of the CNN using a distinct multi-institutional data set curated from multiple sources (n = 498 subjects). Segmentation accuracy was evaluated by computing Dice scores. Utilizing these segmentations, we computed liver volume from CT and T1-weighted (T1w) MRI exams, and estimated hepatic proton- density-fat-fraction (PDFF) from multi-echo T2*w MRI exams. We compared quantitative volumetry and PDFF estimates between automated and manual segmentation using Pearson correlation and Bland-Altman statistics.ResultsDice scores were 0.94 ± 0.06 for CT (n = 230), 0.95 ± 0.03 (n = 100) for T1w MR, and 0.92 ± 0.05 for T2*w MR (n = 169). Liver volume measured by manual and automated segmentation agreed closely for CT (95% limit-of-agreement (LoA) = [-298 mL, 180 mL]) and T1w MR (LoA = [-358 mL, 180 mL]). Hepatic PDFF measured by the two segmentations also agreed closely (LoA = [-0.62%, 0.80%]).ConclusionsUtilizing a transfer-learning strategy, we have demonstrated the feasibility of a CNN to be generalized to perform liver segmentations across different imaging techniques and modalities. With further refinement and validation, CNNs may have broad applicability for multimodal liver volumetry and hepatic tissue characterization.

Background and purposeExecutive function (EF) decline is common after brain radiation therapy (RT), yet the etiology is unclear. We analyzed the association between longitudinal changes in frontal lobe white matter microstructure and decline in EF following RT in brain tumor patients on a prospective clinical trial.Materials and methodsDiffusion tensor imaging was obtained on 22 patients with brain tumors prior to RT, as well as 3- and 6-months post-RT, in a prospective, observational trial. Fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) were calculated within the superficial white matter (SWM) of the anterior cingulate (AC) and dorsolateral prefrontal cortex. Measures of cognitive flexibility, verbal fluency, and verbal set-shifting were obtained pre- and post-RT. Reliable change indices were calculated to determine significant baseline to 6-month EF changes.ResultsDecreases in FA and increases in MD were observed in the caudal AC (CAC) at 3-months post-RT. CAC changes were characterized by increased RD bilaterally. From baseline to 6-months post-RT, decreased FA and increased MD and RD of the CAC was associated with decline in verbal set-shifting ability, whereas increased MD in the CAC was associated with a decline in cognitive flexibility.ConclusionWhite matter underlying the AC may be particularly vulnerable to radiation effects. Early microstructural loss within AC SWM represents an important biomarker for EF decline, and dose reduction in this region may represent a possibility for cognitive preservation for patients receiving radiotherapy.

BackgroundMolecular markers of WHO grade II/III glioma are known to have important prognostic and predictive implications and may be associated with unique imaging phenotypes. The purpose of this study is to determine whether three clinically relevant molecular markers identified in gliomas-IDH, 1p/19q, and MGMT status-show distinct quantitative MRI characteristics on FLAIR imaging.MethodsSixty-one patients with grade II/III gliomas who had molecular data and MRI available prior to radiation were included. Quantitative MRI features were extracted that measured tissue heterogeneity (homogeneity and pixel correlation) and FLAIR border distinctiveness (edge contrast; EC). T-tests were conducted to determine whether patients with different genotypes differ across the features. Logistic regression with LASSO regularization was used to determine the optimal combination of MRI and clinical features for predicting molecular subtypes.ResultsPatients with IDH wildtype tumors showed greater signal heterogeneity (p = 0.001) and lower EC (p = 0.008) within the FLAIR region compared to IDH mutant tumors. Among patients with IDH mutant tumors, 1p/19q co-deleted tumors had greater signal heterogeneity (p = 0.002) and lower EC (p = 0.005) compared to 1p/19q intact tumors. MGMT methylated tumors showed lower EC (p = 0.03) compared to the unmethylated group. The combination of FLAIR border distinctness, heterogeneity, and pixel correlation optimally classified tumors by IDH status.ConclusionQuantitative imaging characteristics of FLAIR heterogeneity and border pattern in grade II/III gliomas may provide unique information for determining molecular status at time of initial diagnostic imaging, which may then guide subsequent surgical and medical management.

In the initial online publication, the values in the last two rows in Table 1 were in the wrong rows. The original article has been corrected.

The 2016 World Health Organization Classification of Tumors of the Central Nervous System incorporates the use of molecular information into the classification of brain tumors, including grade II and III gliomas, providing new prognostic information that cannot be delineated based on histopathology alone. We hypothesized that these genomic subgroups may also have distinct imaging features. A retrospective single institution study was performed on 40 patients with pathologically proven infiltrating WHO grade II/III gliomas with a pre-treatment MRI and molecular data on IDH, chromosomes 1p/19q and ATRX status. Two blinded Neuroradiologists qualitatively assessed MR features. The relationship between each parameter and molecular subgroup (IDH-wildtype; IDH-mutant-1p/19q codeleted-ATRX intact; IDH-mutant-1p/19q intact-ATRX loss) was evaluated with Fisher's exact test. Progression free survival (PFS) was also analyzed. A border that could not be defined on FLAIR was most characteristic of IDH-wildtype tumors, whereas IDH-mutant tumors demonstrated either well-defined or slightly ill-defined borders (p = 0.019). Degree of contrast enhancement and presence of restricted diffusion did not distinguish molecular subgroups. Frontal lobe predominance was associated with IDH-mutant tumors (p = 0.006). The IDH-wildtype subgroup had significantly shorter PFS than the IDH-mutant groups (p

New magnetic resonance (MR) molecular imaging techniques offer the potential for noninvasive, simultaneous quantification of metabolic and perfusion parameters in tumors. This study applied a three-dimensional dynamic dual-agent hyperpolarized 13C magnetic resonance spectroscopic imaging approach with 13C-pyruvate and 13C-urea to investigate differences in perfusion and metabolism between low- and high-grade tumors in the transgenic adenocarcinoma of mouse prostate (TRAMP) transgenic mouse model of prostate cancer. Dynamic MR data were corrected for T1 relaxation and RF excitation and modeled to provide quantitative measures of pyruvate to lactate flux (kPL ) and urea perfusion (urea AUC) that correlated with TRAMP tumor histologic grade. kPL values were relatively higher for high-grade TRAMP tumors. The increase in kPL flux correlated significantly with higher lactate dehydrogenase activity and mRNA expression of Ldha, Mct1, and Mct4 as well as with more proliferative disease. There was a significant reduction in perfusion in high-grade tumors that associated with increased hypoxia and mRNA expression of Hif1α and Vegf and increased ktrans , attributed to increased blood vessel permeability. In 90% of the high-grade TRAMP tumors, a mismatch in perfusion and metabolism measurements was observed, with low perfusion being associated with increased kPL This perfusion-metabolism mismatch was also associated with metastasis. The molecular imaging approach we developed could be translated to investigate these imaging biomarkers for their diagnostic and prognostic power in future prostate cancer clinical trials. Cancer Res; 77(12); 3207-16. ©2017 AACR.

Radiation therapy (RT) is a critical treatment modality for patients with brain tumors, although it can cause adverse effects. Recent data suggest that brain RT is associated with dose-dependent cortical atrophy, which could disrupt neocortical networks. This study examines whether brain RT affects structural network properties in brain tumor patients. We applied graph theory to MRI-derived cortical thickness estimates of 54 brain tumor patients before and after RT. Cortical surfaces were parcellated into 68 regions and correlation matrices were created for patients pre- and post-RT. Significant changes in graph network properties were tested using nonparametric permutation tests. Linear regressions were conducted to measure the association between dose and changes in nodal network connectivity. Increases in transitivity, modularity, and global efficiency (n = 54, p

Purpose To examine the effects of subconcussive impacts resulting from a single season of youth (age range, 8-13 years) football on changes in specific white matter (WM) tracts as detected with diffusion-tensor imaging in the absence of clinically diagnosed concussions. Materials and Methods Head impact data were recorded by using the Head Impact Telemetry system and quantified as the combined-probability risk-weighted cumulative exposure (RWECP). Twenty-five male participants were evaluated for seasonal fractional anisotropy (FA) changes in specific WM tracts: the inferior fronto-occipital fasciculus (IFOF), inferior longitudinal fasciculus, and superior longitudinal fasciculus (SLF). Fiber tracts were segmented into a central core and two fiber terminals. The relationship between seasonal FA change in the whole fiber, central core, and the fiber terminals with RWECP was also investigated. Linear regression analysis was conducted to determine the association between RWECP and change in fiber tract FA during the season. Results There were statistically significant linear relationships between RWEcp and decreased FA in the whole (R2 = 0.433; P = .003), core (R2 = 0.3649; P = .007), and terminals (R2 = 0.5666; P < .001) of left IFOF. A trend toward statistical significance (P = .08) in right SLF was observed. A statistically significant correlation between decrease in FA of the right SLF terminal and RWECP was also observed (R2 = 0.2893; P = .028). Conclusion This study found a statistically significant relationship between head impact exposure and change of FA fractional anisotropy value of whole, core, and terminals of left IFOF and right SLF's terminals where WM and gray matter intersect, in the absence of a clinically diagnosed concussion. © RSNA, 2016.

The accurate detection and characterization of cancerous tissue is still a major problem for the clinical management of individual cancer patients and for monitoring their response to therapy. MRI with hyperpolarized agents is a promising technique for cancer characterization because it can non-invasively provide a local assessment of the tissue metabolic profile. In this work, we measured the kinetics of hyperpolarized [1-(13)C] pyruvate and (13)C-urea in prostate and liver tumor models using a compressed sensing dynamic MRSI method. A kinetic model fitting method was developed that incorporated arbitrary RF flip angle excitation and measured a pyruvate to lactate conversion rate, Kpl, of 0.050 and 0.052 (1/s) in prostate and liver tumors, respectively, which was significantly higher than Kpl in healthy tissues [Kpl =0.028 (1/s), P

Background: Lung cancer is the leading cause of death, related to cancer in both men and women. MicroRNAs (miRNAs) play key roles in regulating the genes that are responsible for apoptosis, and regulation of gene expression. MicroRNA expression is deregulated in cancer. Many efforts have been made to detect cancer biomarkers. Extracellular miRNAs, which are directly released from tumors, can be detected in circulating blood. Objectives: The aim of this study was to examine the expression of miRNA-205 and -21 in lung cancer patients relative to healthy controls. Methods: This cross sectional study was performed in 50 healthy blood sample controls and 50 non-small cell lung cancer (NSCLC) blood samples. The expression of two miRNAs (miR-21 and miR-205) in peripheral blood mononuclear cells (PBMCs) was evaluated, using real-time polymerase chain reaction (real-time PCR) techniques. Statistical tests were carried out via the Mann-Whitney test. Results: The miR-205 levels of expression in the patients with lung cancer significantly increased in comparison to healthy controls (6.8 - 0.42 and 1.2 - 0.19, respectively), (P = 0.014). The expression of miR-21 in lung cancer (5.2 - 0.52) significantly increased compared to the control group (1.6-0.14) (P < 0.001). Conclusions: Based on our findings, lung cancer can enhance the ectopic expression of several miRNAs, which, in turn, play key roles in impaired apoptosis and further cancerous progression. [ABSTRACT FROM AUTHOR]

We assessed the effect of purmorphamine along with collagen/hydroxyapatite scaffold in inducing osteogenesis of human endometrial stem cells (hEnSCs). The adhesion, viability, proliferation, and differentiation of cells on scaffold were assayed with SEM, MTT, real time-PCR, and ALP assay, respectively. The results were shown good integration of cells with scaffold. Also, qRT-PCR of differentiated cells shows that osteoblast cell markers are expressed after 21d in 2D and scaffold groups while in the scaffold group the expression of these markers were higher than the 2D group. Based on our findings, collagen/hydroxyapatite scaffold with PMA has the potential role in osteogenic differentiation of hEnSCs. [ABSTRACT FROM AUTHOR]

Rosacea patients often are burdened with embarrassment, social anxiety, and psychiatric comorbidities. The Patient Health Questionnaire 9 (PHQ-9) is a validated and reliable self-administered tool for diagnosis of depression and designation of depression severity. This study aimed to examine the relationship between rosacea severity scores and level of depression using a validated rosacea self-assessment tool and the PHQ-9, respectively. Our results indicated that there is a direct relationship between rosacea severity and level of depression, and the PHQ-9 could prove useful in screening for depression in rosacea patients given the high incidence of psychiatric comorbidities in this patient population.

A variety of triggers are thought to exacerbate rosacea. A validated self-assessment tool and survey was used to study the relationship between rosacea severity and triggers. Subjects were adult patients with a clinical diagnosis of rosacea. Increased severity of disease was significantly associated with consumption of many alcoholic beverages in 1 day and employment at a job requiring extensive sun exposure. The authors' findings may inform physician counseling practices; patients may be provided with practical measures for managing their rosacea, such as limiting alcohol consumption over short periods of time and increasing sun protection, especially in the summer., (Copyright © 2017 Elsevier Inc. All rights reserved.)

The accurate detection and characterization of cancerous tissue is still a major problem for the clinical management of individual cancer patients and for monitoring their response to therapy. MRI with hyperpolarized agents is a promising technique for cancer characterization because it can non-invasively provide a local assessment of the tissue metabolic profile. In this work, we measured the kinetics of hyperpolarized [1-(13)C] pyruvate and (13)C-urea in prostate and liver tumor models using a compressed sensing dynamic MRSI method. A kinetic model fitting method was developed that incorporated arbitrary RF flip angle excitation and measured a pyruvate to lactate conversion rate, Kpl, of 0.050 and 0.052 (1/s) in prostate and liver tumors, respectively, which was significantly higher than Kpl in healthy tissues [Kpl =0.028 (1/s), P<0.001]. Kpl was highly correlated to the total lactate to total pyruvate signal ratio (correlation coefficient =0.95). We additionally characterized the total pyruvate and urea perfusion, as in cancerous tissue there is both existing vasculature and neovascularization as different kinds of lesions surpass the normal blood supply, including small circulation disturbance in some of the abnormal vessels. A significantly higher perfusion of pyruvate (accounting for conversion to lactate and alanine) relative to urea perfusion was seen in cancerous tissues (liver cancer and prostate cancer) compared to healthy tissues (P<0.001), presumably due to high pyruvate uptake in tumors.