The relationship between radiation dose and bevacizumab-related imaging abnormality in patients with brain tumors: A voxel-wise normal tissue complication probability (NTCP) analysis.

PURPOSE: Bevacizumab-related imaging abnormality (BRIA), appearing as areas of restricted diffusion on magnetic resonance imaging (MRI) and representing atypical coagulative necrosis pathologically, has been observed in patients with brain tumors receiving radiotherapy and bevacizumab. We investigated the role of cumulative radiation dose in BRIA development in a voxel-wise analysis. METHODS: Patients (n = 18) with BRIA were identified. All had high-grade gliomas or brain metastases treated with radiotherapy and bevacizumab. Areas of BRIA were segmented semi-automatically on diffusion-weighted MRI with apparent diffusion coefficient (ADC) images. To avoid confounding by possible tumor, hypoperfusion was confirmed with perfusion imaging. ADC images and radiation dose maps were co-registered to a high-resolution T1-weighted MRI and registration accuracy was verified. Voxel-wise normal tissue complication probability analyses were performed using a logistic model analyzing the relationship between cumulative voxel equivalent total dose in 2 Gy fractions (EQD2) and BRIA development at each voxel. Confidence intervals for regression model predictions were estimated with bootstrapping. RESULTS: Among 18 patients, 39 brain tumors were treated. Patients received a median of 4.5 cycles of bevacizumab and 1-4 radiation courses prior to BRIA appearance. Most (64%) treated tumors overlapped with areas of BRIA. The median proportion of each BRIA region of interest volume overlapping with tumor was 98%. We found a dose-dependent association between cumulative voxel EQD2 and the relative probability of BRIA (ß0 = -5.1, ß1 = 0.03 Gy-1, γ = 1.3). CONCLUSIONS: BRIA is likely a radiation dose-dependent phenomenon in patients with brain tumors receiving bevacizumab and radiotherapy. The combination of radiation effects and tumor microenvironmental factors in potentiating BRIA in this population should be further investigated.

Clinical utility of structural connectomics in predicting memory in temporal lobe epilepsy.

OBJECTIVE: To determine the predictive power of white matter neuronal networks (i.e., structural connectomes [SCs]) in discriminating memory-impaired patients with temporal lobe epilepsy (TLE) from those with normal memory. METHODS: T1- and diffusion MRI (dMRI), clinical variables, and neuropsychological measures of verbal memory were available for 81 patients with TLE. Prediction of memory impairment was performed with a tree-based classifier (XGBoost) for 4 models: (1) a clinical model including demographic and clinical features, (2) a hippocampal volume (HCV) model, (3) a tract model including 5 temporal lobe white matter association tracts derived from a dMRI atlas, and (4) an SC model based on dMRI. SCs were derived by extracting cortical-cortical connections from a temporal lobe subnetwork with probabilistic tractography. Principal component (PC) analysis was then applied to reduce the dimensionality of the SC, yielding 10 PCs. Multimodal models were also tested combining SCs and tracts with HCV. Each model was trained on 48 patients from 1 epilepsy center and tested on 33 patients from a different center. RESULTS: Multimodal models that included the SC + HCV model yielded the highest classification accuracy (81%; 0.90 sensitivity; 0.67 specificity), outperforming the clinical model (61%; p < 0.001) and HCV model (66%; p < 0.001). In addition, the unimodal SC model (76% accuracy) and tract model (73% accuracy) outperformed the clinical model (p < 0.001) and HCV model (p < 0.001) for classifying patients with TLE with and without memory impairment. Furthermore, the SC identified that short-range temporal-temporal connections were important contributors to memory performance. CONCLUSION: SCs and tract-based models are stronger predictors of memory impairment in TLE than HCVs and clinical variables. However, SCs may provide additional information about local cortical-cortical connectivity contributing to memory that is not captured in large association tracts.

Deep Learning Single-Frame and Multiframe Super-Resolution for Cardiac MRI.

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.

Differentiation of progressive disease from pseudoprogression using 3D PCASL and DSC perfusion MRI in patients with glioblastoma.

PURPOSE: To use 3D pseudocontinuous arterial spin labeling (3D PCASL) and dynamic susceptibility contrast-enhanced (DSC) perfusion MRI to differentiate progressive disease from pseudoprogression in patients with glioblastoma (GBM). METHODS: Thirty-two patients with GBM who developed progressively enhancing lesions within the radiation field following resection and chemoradiation were included in this retrospective, single-institution study. The updated modified RANO criteria were used to establish progressive disease or pseudoprogression. Following 3D PCASL and DSC MR imaging, perfusion parameter estimates of cerebral blood flow (ASL-nCBF and DSC-nrCBF) and cerebral blood volume (DSC-nrCBV) were calculated. Additionally, contrast enhanced volumes were measured. Mann-Whitney U tests were used to compare groups. Linear discriminant analysis (LDA) and area under receiver operator characteristic curve (AUC) analyses were used to evaluate performance of each perfusion parameter and to determine optimal cut-off points. RESULTS: All perfusion parameter measurements were higher in patients with progressive disease (mean, 95% CI ASL-nCBF 2.48, [2.03, 2.93]; DSC-nrCBF = 2.27, [1.85, 2.69]; DSC-nrCBV = 3.51, [2.37, 4.66]) compared to pseudoprogression (mean, 95% CI ASL-nCBF 0.99, [0.47, 1.52]; DSC-nrCBF = 1.05, [0.36, 1.74]; DSC-nCBV = 1.19, [0.34, 2.05]), and findings were significant at the p < 0.0125 level (p = 0.001, 0.003, 0.002; effect size: Cohen's d = 1.48, 1.27, and 0.92). Contrast enhanced volumes were not significantly different between groups (p > 0.447). All perfusion parameters demonstrated high AUC (0.954 for ASL-nCBF, 0.867 for DSC-nrCBF, and 0.891 for DSC-nrCBV), however, ASL-nCBF demonstrated the highest AUC and misclassified the fewest cases (N = 6). Lesions correctly classified by ASL but misclassified by DSC were located along the skull base or adjacent to large resection cavities with residual blood products, at areas of increased susceptibility. CONCLUSION: Both 3D PCASL and DSC perfusion MRI techniques have nearly equivalent performance for the differentiation of progressive disease from pseudoprogression in patients with GBM. However, 3D PCASL is less sensitive to susceptibility artifact and may allow for improved classification in select cases.

Restriction Spectrum Imaging Differentiates True Tumor Progression From Immune-Mediated Pseudoprogression: Case Report of a Patient With Glioblastoma.

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.

The white matter connectome as an individualized biomarker of language impairment in temporal lobe epilepsy.

OBJECTIVE: The 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. METHODS: T1- 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. RESULTS: The SC-model yielded a greater area under the curve (AUC; .73) and accuracy (79%) compared to both the tract-model (AUC: .54, p < .001; accuracy: 70%, p < .001) and the CV-model (AUC: .59, p < .001; accuracy: 64%, p < .001). Within the SC-model, lateral temporal connections had the highest importance to model performance, including connections similar to language association tracts such as links between the superior temporal gyrus to pars opercularis. However, in addition to these connections many additional connections that were widely distributed, bilateral and interhemispheric in nature were identified as contributing to SC-model performance. CONCLUSION: The SC revealed a white matter network contributing to language impairment that was widely distributed, bilateral, and lateral temporal in nature. The distributed network underlying language may be why the SC-model has an advantage in identifying sub-components of the complex fiber networks most relevant for aspects of language performance.

Multi-domain neurocognitive classification of primary brain tumor patients prior to radiotherapy on a prospective clinical trial.

INTRODUCTION: We 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. METHODS: A 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. RESULTS: Statistically 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. CONCLUSIONS: Memory 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.

Differential sensitivity of structural, diffusion, and resting-state functional MRI for detecting brain alterations and verbal memory impairment in temporal lobe epilepsy.

OBJECTIVES: Temporal 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. METHODS: Diffusion 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. RESULTS: Patients 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. SIGNIFICANCE: These 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.

Cognitive phenotypes in temporal lobe epilepsy are associated with distinct patterns of white matter network abnormalities.

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.

Identifying early diffusion imaging biomarkers of regional white matter injury as indicators of executive function decline following brain radiotherapy: A prospective clinical trial in primary brain tumor patients.

BACKGROUND AND PURPOSE: Executive 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 METHODS: Diffusion 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. RESULTS: Decreases 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. CONCLUSION: White 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.

Automated selection of myocardial inversion time with a convolutional neural network: Spatial temporal ensemble myocardium inversion network (STEMI-NET).

PURPOSE: Delayed 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. METHODS: We 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. RESULTS: Prediction 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. CONCLUSION: In 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.

Automated CT and MRI Liver Segmentation and Biometry Using a Generalized Convolutional Neural Network.

PURPOSE: To 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. METHODS: We 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. RESULTS: Dice 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%]). CONCLUSIONS: Utilizing 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.

Deep Learning-based Prescription of Cardiac MRI Planes.

PURPOSE: To 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 METHODS: Annotated 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. RESULTS: On 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. CONCLUSION: DL-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.

Screening for depression in rosacea patients.

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.

Molecular classification of patients with grade II/III glioma using quantitative MRI characteristics.

BACKGROUND: Molecular 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. METHODS: Sixty-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. RESULTS: Patients 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. CONCLUSION: Quantitative 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.

Does bilingualism increase brain or cognitive reserve in patients with temporal lobe epilepsy?

OBJECTIVE: Bilingual healthy adults have been shown to exhibit an advantage in executive functioning (EF) that is associated with microstructural changes in white matter (WM) networks. Patients with temporal lobe epilepsy (TLE) often show EF deficits that are associated with WM compromise. In this study, we investigate whether bilingualism can increase cognitive reserve and/or brain reserve in bilingual patients with TLE, mitigating EF impairment and WM compromise. METHODS: Diffusion tensor imaging was obtained in 19 bilingual and 26 monolingual patients with TLE, 12 bilingual healthy controls (HC), and 21 monolingual HC. Fractional anisotropy (FA) and mean diffusivity (MD) were calculated for the uncinate fasciculus (Unc) and cingulum (Cing), superior frontostriatal tract (SFS), and inferior frontostriatal tract (IFS). Measures of EF included Trail Making Test-B (TMT-B) and Delis-Kaplan Executive Function System Color-Word Inhibition/Switching. Analyses of covariance were conducted to compare FA and MD of the Unc, Cing, SFS, and IFS and EF performance across groups. RESULTS: In bilingual patients, FA was lower in the ipsilateral Cing and Unc compared to all other groups. For both patient groups, MD of the ipsilateral Unc was higher relative to HC. Despite more pronounced reductions in WM integrity, bilingual patients performed similarly to monolingual TLE and both HC groups on EF measures. By contrast, monolingual patients performed worse than HC on TMT-B. In addition, differences in group means between bilingual and monolingual patients on TMT-B approached significance when controlling for the extent of WM damage (P = .071; d = 0.62), suggesting a tendency toward higher performance for bilingual patients. SIGNIFICANCE: Despite poorer integrity of regional frontal lobe WM, bilingual patients performed similarly to monolingual patients and HC on EF measures. These findings align with studies suggesting that bilingualism may provide a protective factor for individuals with neurological disease, potentially through reorganization of EF networks that promote greater cognitive reserve.

Rosacea Triggers: Alcohol and Smoking.

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.

Patient Costs Associated with Rosacea.

The recalcitrance of rosacea to many treatment options may prompt patients to spend exorbitant amounts of money on unsubstantiated treatment regimens in an effort to achieve relief. The authors examine the relationship between disease severity and treatment cost across several demographic and socioeconomic strata. Familiarization of evidence-based clinical recommendations and consensus guidelines may equip physicians to educate patients about the most efficacious and cost-effective treatment options to assist patients in making cost-conscious decisions in the management of their rosacea.

Measurement of Disease Severity in a Population of Rosacea Patients.

Severity of rosacea in populations is not well characterized. A validated self-assessment tool was used to study the relationship between rosacea severity and demographic factors. Subjects were adult patients with a clinical diagnosis of rosacea. Self-assessment severity scores were significantly higher in participants less than 60 years old (mean 3.43 ± 1.07) compared with those greater than or equal to 60 years old (mean 3.09 ± 1.13; P = .04). Self-assessment severity scores were significantly higher in men (3.6 ± 1.3) than women (3.2 ± 1.0; P = .04). The authors conclude that rosacea is more severe in men and younger patients.

Mapping lexical-semantic networks and determining hemispheric language dominance: Do task design, sex, age, and language performance make a difference?

Blocked and event-related fMRI designs are both commonly used to localize language networks and determine hemispheric dominance in research and clinical settings. We compared activation profiles on a semantic monitoring task using one of the two designs in a total of 43 healthy individual to determine whether task design or subject-specific factors (i.e., age, sex, or language performance) influence activation patterns. We found high concordance between the two designs within core language regions, including the inferior frontal, posterior temporal, and basal temporal region. However, differences emerged within inferior parietal cortex. Subject-specific factors did not influence activation patterns, nor did they interact with task design. These results suggest that despite high concordance within perisylvian regions that are robust to subject-specific factors, methodological differences between blocked and event-related designs may contribute to parietal activations. These findings provide important information for researchers incorporating fMRI results into meta-analytic studies, as well as for clinicians using fMRI to guide pre-surgical planning.