Background and ObjectivesRepeated impacts in high-contact sports such as American football can affect the brain's microstructure, which can be studied using diffusion MRI. Most imaging studies are cross-sectional, do not include low-contact players as controls, or lack advanced tract-specific microstructural metrics. We aimed to investigate longitudinal changes in high-contact collegiate athletes compared with low-contact controls using advanced diffusion MRI and automated fiber quantification.MethodsWe examined brain microstructure in high-contact (football) and low-contact (volleyball) collegiate athletes with up to 4 years of follow-up. Inclusion criteria included university and team enrollment. Exclusion criteria included history of neurosurgery, severe brain injury, and major neurologic or substance abuse disorder. We investigated diffusion metrics along the length of tracts using nested linear mixed-effects models to ascertain the acute and chronic effects of subconcussive and concussive impacts, and associations between diffusion changes with clinical, behavioral, and sports-related measures.ResultsForty-nine football and 24 volleyball players (271 total scans) were included. Football players had significantly divergent trajectories in multiple microstructural metrics and tracts. Longitudinal increases in fractional anisotropy and axonal water fraction, and decreases in radial/mean diffusivity and orientation dispersion index, were present in volleyball but absent in football players (all findings |T-statistic|> 3.5, p value <0.0001). This pattern was present in the callosum forceps minor, superior longitudinal fasciculus, thalamic radiation, and cingulum hippocampus. Longitudinal differences were more prominent and observed in more tracts in concussed football players (n = 24, |T|> 3.6, p < 0.0001). An analysis of immediate postconcussion scans (n = 12) demonstrated a transient localized increase in axial diffusivity and mean/radial kurtosis in the uncinate and cingulum hippocampus (|T| > 3.7, p < 0.0001). Finally, within football players, those with high position-based impact risk demonstrated increased intracellular volume fraction longitudinally (T = 3.6, p < 0.0001).DiscussionThe observed longitudinal changes seen in football, and especially concussed athletes, could reveal diminished myelination, altered axonal calibers, or depressed pruning processes leading to a static, nondecreasing axonal dispersion. This prospective longitudinal study demonstrates divergent tract-specific trajectories of brain microstructure, possibly reflecting a concussive and repeated subconcussive impact-related alteration of white matter development in football athletes.

Background and Purpose: Athletes participating in high-contact sports experience repeated head trauma. Anatomical findings, such as a cavum septum pellucidum, prominent CSF spaces, and hippocampal volume reductions, have been observed in cases of mild traumatic brain injury. The extent to which these neuroanatomical findings are associated with high-contact sports is unknown. The purpose of this study was to determine whether there are subtle neuroanatomic differences between athletes participating in high-contact sports compared to low-contact athletic controls. Materials and Methods: We performed longitudinal structural brain MRI scans in 63 football (high-contact) and 34 volleyball (low-contact control) male collegiate athletes with up to 4 years of follow-up, evaluating a total of 315 MRI scans. Board-certified neuroradiologists performed semi-quantitative visual analysis of neuroanatomic findings, including: cavum septum pellucidum type and size, extent of perivascular spaces, prominence of CSF spaces, white matter hyperintensities, arterial spin labeling perfusion asymmetries, fractional anisotropy holes, and hippocampal size. Results: At baseline, cavum septum pellucidum length was greater in football compared to volleyball controls (p = 0.02). All other comparisons were statistically equivalent after multiple comparison correction. Within football at baseline, the following trends that did not survive multiple comparison correction were observed: more years of prior football exposure exhibited a trend toward more perivascular spaces (p = 0.03 uncorrected), and lower baseline Standardized Concussion Assessment Tool scores toward more perivascular spaces (p = 0.02 uncorrected) and a smaller right hippocampal size (p = 0.02 uncorrected). Conclusion: Head impacts in high-contact sport (football) athletes may be associated with increased cavum septum pellucidum length compared to low-contact sport (volleyball) athletic controls. Other investigated neuroradiology metrics were generally equivalent between sports.

Collegiate football athletes are subject to repeated head impacts. The purpose of this study was to determine whether this exposure can lead to changes in brain structure. This prospective cohort study was conducted with up to 4 years of follow-up on 63 football (high-impact) and 34 volleyball (control) male collegiate athletes with a total of 315 MRI scans (after exclusions: football n ​= ​50, volleyball n ​= ​24, total scans ​= ​273) using high-resolution structural imaging. Volumetric and cortical thickness estimates were derived using FreeSurfer 5.3's longitudinal pipeline. A linear mixed-effects model assessed the effect of group (football vs. volleyball), time from baseline MRI, and the interaction between group and time. We confirmed an expected developmental decrement in cortical thickness and volume in our cohort (p ​< ​.001). Superimposed on this, total cortical gray matter volume (p ​= ​.03) and cortical thickness within the left hemisphere (p ​= ​.04) showed a group by time interaction, indicating less age-related volume reduction and thinning in football compared to volleyball athletes. At the regional level, sport by time interactions on thickness and volume were identified in the left orbitofrontal (p ​= ​.001), superior temporal (p ​= ​.001), and postcentral regions (p ​< ​.001). Additional cortical thickness interactions were found in the left temporal pole (p ​= ​.003) and cuneus (p ​= ​.005). At the regional level, we also found main effects of sport in football athletes characterized by reduced volume in the right hippocampus (p ​= ​.003), right superior parietal cortical gray (p ​< ​.001) and white matter (p ​< ​.001), and increased volume of the left pallidum (p ​= ​.002). Within football, cortical thickness was higher with greater years of prior play (left hemisphere p ​= ​.013, right hemisphere p ​= ​.005), and any history of concussion was associated with less cortical thinning (left hemisphere p ​= ​.010, right hemisphere p ​= ​.011). Additionally, both position-associated concussion risk (p ​= ​.002) and SCAT scores (p ​= ​.023) were associated with less of the expected volume decrement of deep gray structures. This prospective longitudinal study comparing football and volleyball athletes shows divergent age-related trajectories of cortical thinning, possibly reflecting an impact-related alteration of normal cortical development. This warrants future research into the underlying mechanisms of impacts to the head on cortical maturation.

Collegiate football athletes are subject to repeated traumatic brain injuriesthat may cause brain injury. The hippocampus is composed of several distinct subfields with possible differential susceptibility to injury. The aim of this study is to determine whether there are longitudinal changes in hippocampal subfield volume in collegiate football. A prospective cohort study was conducted over a 5-year period tracking 63 football and 34 volleyball male collegiate athletes. Athletes underwent high-resolution structural magnetic resonance imaging, and automated segmentation provided hippocampal subfield volumes. At baseline, football (n = 59) athletes demonstrated a smaller subiculum volume than volleyball (n = 32) athletes (-67.77 mm3; p = 0.012). A regression analysis performed within football athletes similarly demonstrated a smaller subiculum volume among those at increased concussion risk based on athlete position (p = 0.001). For the longitudinal analysis, a linear mixed-effects model assessed the interaction between sport and time, revealing a significant decrease in cornu ammonis area 1 (CA1) volume in football (n = 36) athletes without an in-study concussion compared to volleyball (n = 23) athletes (volume difference per year = -35.22 mm3; p = 0.005). This decrease in CA1 volume over time was significant when football athletes were examined in isolation from volleyball athletes (p = 0.011). Thus, this prospective, longitudinal study showed a decrease in CA1 volume over time in football athletes, in addition to baseline differences that were identified in the downstream subiculum. Hippocampal changes may be important to study in high-contact sports.

BACKGROUND:In previous clinical trials, antiangiogenic therapies such as bevacizumab did not show efficacy in patients with newly diagnosed glioblastoma (GBM). This may be a result of the heterogeneity of GBM, which has a variety of imaging-based phenotypes and gene expression patterns. In this study, we sought to identify a phenotypic subtype of GBM patients who have distinct tumor-image features and molecular activities and who may benefit from antiangiogenic therapies. METHODS:Quantitative image features characterizing subregions of tumors and the whole tumor were extracted from preoperative and pretherapy perfusion magnetic resonance (MR) images of 117 GBM patients in 2 independent cohorts. Unsupervised consensus clustering was performed to identify robust clusters of GBM in each cohort. Cox survival and gene set enrichment analyses were conducted to characterize the clinical significance and molecular pathway activities of the clusters. The differential treatment efficacy of antiangiogenic therapy between the clusters was evaluated. RESULTS:A subgroup of patients with elevated perfusion features was identified and was significantly associated with poor patient survival after accounting for other clinical covariates (P values <.01; hazard ratios > 3) consistently found in both cohorts. Angiogenesis and hypoxia pathways were enriched in this subgroup of patients, suggesting the potential efficacy of antiangiogenic therapy. Patients of the angiogenic subgroups pooled from both cohorts, who had chemotherapy information available, had significantly longer survival when treated with antiangiogenic therapy (log-rank P=.022). CONCLUSIONS:Our findings suggest that an angiogenic subtype of GBM patients may benefit from antiangiogenic therapy with improved overall survival.

BACKGROUND AND PURPOSE:Tumor location has been shown to be a significant prognostic factor in patients with glioblastoma. The purpose of this study was to characterize glioblastoma lesions by identifying MR imaging voxel-based tumor location features that are associated with tumor molecular profiles, patient characteristics, and clinical outcomes. MATERIALS AND METHODS:Preoperative T1 anatomic MR images of 384 patients with glioblastomas were obtained from 2 independent cohorts (n = 253 from the Stanford University Medical Center for training and n = 131 from The Cancer Genome Atlas for validation). An automated computational image-analysis pipeline was developed to determine the anatomic locations of tumor in each patient. Voxel-based differences in tumor location between good (overall survival of >17 months) and poor (overall survival of <11 months) survival groups identified in the training cohort were used to classify patients in The Cancer Genome Atlas cohort into 2 brain-location groups, for which clinical features, messenger RNA expression, and copy number changes were compared to elucidate the biologic basis of tumors located in different brain regions. RESULTS:Tumors in the right occipitotemporal periventricular white matter were significantly associated with poor survival in both training and test cohorts (both, log-rank P < .05) and had larger tumor volume compared with tumors in other locations. Tumors in the right periatrial location were associated with hypoxia pathway enrichment and PDGFRA amplification, making them potential targets for subgroup-specific therapies. CONCLUSIONS:Voxel-based location in glioblastoma is associated with patient outcome and may have a potential role for guiding personalized treatment.

Glioblastoma (GBM) is the most common and highly lethal primary malignant brain tumor in adults. There is a dire need for easily accessible, noninvasive biomarkers that can delineate underlying molecular activities and predict response to therapy. To this end, we sought to identify subtypes of GBM, differentiated solely by quantitative magnetic resonance (MR) imaging features, that could be used for better management of GBM patients. Quantitative image features capturing the shape, texture, and edge sharpness of each lesion were extracted from MR images of 121 single-institution patients with de novo, solitary, unilateral GBM. Three distinct phenotypic "clusters" emerged in the development cohort using consensus clustering with 10,000 iterations on these image features. These three clusters--pre-multifocal, spherical, and rim-enhancing, names reflecting their image features--were validated in an independent cohort consisting of 144 multi-institution patients with similar tumor characteristics from The Cancer Genome Atlas (TCGA). Each cluster mapped to a unique set of molecular signaling pathways using pathway activity estimates derived from the analysis of TCGA tumor copy number and gene expression data with the PARADIGM (Pathway Recognition Algorithm Using Data Integration on Genomic Models) algorithm. Distinct pathways, such as c-Kit and FOXA, were enriched in each cluster, indicating differential molecular activities as determined by the image features. Each cluster also demonstrated differential probabilities of survival, indicating prognostic importance. Our imaging method offers a noninvasive approach to stratify GBM patients and also provides unique sets of molecular signatures to inform targeted therapy and personalized treatment of GBM.

INTRODUCTION: Glioblastoma is the most common and aggressive primary human brain cancer. Noninvasive characterization of intratumor blood flow parameters may help guide clinical decision making. Beyond risk stratification and prognostication, tumor perfusion may inform treatment selection and serial monitoring of newer antiangiogenic targeted therapies. In this study, intra- and intertumor variations in blood volume were quantified by using a novel 3-D volumetric, dynamic-susceptibility contrast-enhanced (DSCE), T2*-weighted perfusion magnetic resonance (MR) analysis to determine associations with molecular features and clinical outcomes. METHODS: A total of n = 150 patients underwent preoperative DSCE T2* MR perfusion analysis, including an internal test cohort and external validation cohort. Volumetric quantitative voxel-based data on relative cerebral blood volume (rCBV) were assessed, including mean, median, kurtosis, skewness, and percentage of elevated rCBV (ie, elevated rCBV). Intra- and intertumor heterogeneity in each parameter was characterized by mosaic analysis. Hierarchical clustering was performed to identify subsets of patients with correlated perfusion patterns. Resulting perfusion-based clusters were assessed against molecular features by using integrated PARADIGM genomic pathway-level elastic net logistic regression analyses. Perfusion-based clusters were assessed in univariate Kaplan-Meier (log-rank) and multivariate Cox proportional hazards models for association with overall clinical survival outcomes. Validated MR perfusion parameters discovered in the test cohort were externally validated in the independent validation cohort. RESULTS: Intra- and intertumor heterogeneity was observed in mosaic analyses of quantitative voxel-based MR perfusion data of mean, median, kurtosis, skewness, and elevated rCBV. Hierarchical clustering and random forest analyses identified an elevated rCBV cluster of patients with correlated MR perfusion patterns that demonstrated distinct molecular features of vasculogenesis, gap junction assembly, and endothelial permeability in integrated PARADIGM genomic pathway-level analysis. This elevated rCBV subgroup of patients demonstrated worse overall survival in univariate and multivariate survival analyses (HR 2.9, P = .02), and these findings externally validated in an independent cohort. CONCLUSION: A distinct vasculogenic subtype of glioblastoma identified by quantitative MR perfusion voxel-based analysis was associated with unique molecular features and worse overall survival. Quantitative volumetric MR perfusion holds potential in characterizing intra- and intertumoral heterogeneity, and identifying biologically distinct, clinically relevant subsets of patients for risk stratification and treatment selection.

Reversible cerebral vasoconstriction syndrome (RCVS) is characterized by sudden-onset thunderclap headache and focal neurologic deficits. Once thought to be a rare syndrome, more advanced non-invasive imaging has led to an increase in RCVS diagnosis. Unilateral vertebral artery dissection has been described in fewer than 40% of cases of RCVS. Bilateral vertebral artery dissection has rarely been reported. We describe the case of a patient with RCVS and bilateral vertebral artery dissection presenting with an intramedullary infarct treated successfully with medical management and careful close follow-up. This rare coexistence should be recognized as the treatment differs.

We sought to discover subtypes of glioblastoma (GBM) defined by quantitative imaging features and to identify their canonical signaling pathways. Preoperative MR imaging from 121 Stanford patients with de novo, focal, unilateral GBM were analyzed. Two board-certified neuroradiologists and a neurosurgeon reached consensus in delineating Regions-of-Interest (ROIs) around areas of enhancement in each T1 post-contrast MR. We extracted 138 quantitative image features representing signal intensity and morphology of each lesion. We then used Consensus Clustering to define subtypes in the Stanford group. Next, Prediction Analysis for Microarrays (PAM) and the In-Group Proportion (IGP) statistic were used to validate the reproducibility and robustness of our subtype classification in a second cohort of subjects from The Cancer Genome Atlas (TCGA, n = 145). Finally, to map imaging subtypes to particular molecular signaling pathways, we performed Significance Analysis of Microarrays (SAM) on pathway activity estimates derived from analysis of TCGA tumor copy number and RNA sequencing data with the PARADIGM algorithm. Consensus Clustering analysis of the training set identified three clusters whose validity was confirmed in the TCGA cohort (p < 0.0001, p< 0.0001, p = 0.015). Cluster sizes for the TCGA validation cohort (i.e. 45%, 21% and 35%) were similar to those in the Stanford training set (57%, 17% and 26%). Cluster 1 was associated with 49 overexpressed pathways (FDR < 5%) including Wnt, hypoxia, apoptosis, cell proliferation, and angiogenesis signaling pathways while Cluster 2 was characterized by downregulation of these pathways (FDR < 5%). No pathway was significantly associated with Cluster 3. We identified three distinct, robust clusters of focal, unilateral GBM defined by quantitative image signatures. Subtypes identified in a homogeneous Stanford training set were validated in the heterogeneous multi-institutional TCGA data set. Two of our three clusters had significant associations with canonical signaling pathways.

Clinical, molecular, and MR imaging data for GBMs in 55 patients were obtained from the Cancer Genome Atlas and the Cancer Imaging Archive after local ethics committee and institutional review board approval. Regions of interest (ROIs) corresponding to enhancing necrotic portions of tumor and peritumoral edema were drawn, and were derived from these ROIs. Robust were defined on the quantitative image features quantitative image features basis of an intraclass correlation coefficient of 0.6 for a digital algorithmic modification and a test-retest . The robust were visualized by analysis features using hierarchic clustering and were correlated with survival by using Cox proportional hazards modeling. Next, these robust were image features correlated with manual radiologist annotations from the Visually Accessible Rembrandt Images (VASARI) feature set and GBM molecular subgroups by using nonparametric statistical tests. A bioinformatic algorithm was used to create gene expression modules, defined as a set of coexpressed genes together with a multivariate model of cancer driver genes predictive of the module's expression pattern. Modules were correlated with robust image features by using the Spearman correlation test to create maps and to link robust with molecular pathways. radiogenomic image features

Abstract Objective: To predict mutations of key genes in glioblastoma multiforme (GBM) from MR image features. Methods: We obtained mutational and MR image data from 35 patients in the Cancer Genome Atlas (TCGA) GBM database. T1-weighted axial images pre and post gadolinium contrast MRI were processed as follows: a board certified neuro-radiologist traced a region of interest (ROI) around the enhanced part of the largest lesion in the T1 post-contrast MRI and confirmed by comparing it with the T1 pre-contrast image. This ROI was then used to compute features that characterized the intensity of the enhanced lesion, the sharpness of lesion boundaries and the boundary shape. We used the resulting data set to build a linear regression model with regularization to predict the presence of a mutation in terms of image features. We focused on predicting mutations in EGFR because several drugs target it and because EGFR mutations are prevalent in the TCGA GBM data. We evaluated the performance of predicting EGFR mutations from MR imaging data using 5-fold cross validation (5F-CV) and the area under the ROC Curve (AUC). The optimal ROC operating point was defined as the point with the maximal sum of sensitivity and specificity when the cost of false positives and false negatives are considered equivalent. Results: We found that computationally-derived MR image features can predict the presence of EGFR mutations with an AUC of 0.80. The optimal operating point had a sensitivity and specificity of 83% and 93% respectively. The top ranked features in the image-based EGFR-predictor model suggest that EGFR-mutated tumors have blurrier edges than EGFR-wild-type tumors. Conclusion: Our preliminary radiogenomic analysis of GBM suggests MR images may be used to non-invasively determine the mutation status of EGFR, an important drug target in glioblastoma. The ability to determine important genomic aberrations based on image data suggests a increasingly important role for imaging in personalized medicine and warrants further investigation. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5561. doi:1538-7445.AM2012-5561