Chad A. Holder

Alterations in Cortical Thickness and White Matter Integrity in Mild Cognitive Impairment Measured by Whole Brain Cortical Thickness Mapping and Diffusion Tensor Imaging

BACKGROUND AND PURPOSE: Mild cognitive impairment (MCI) is a risk factor for Alzheimer disease and can be difficult to diagnose because of the subtlety of symptoms. This study attempted to examine gray matter (GM) and white matter (WM) changes with cortical thickness analysis and diffusion tensor imaging (DTI) in patients with MCI and demographically matched comparison subjects to test these measurements as possible imaging markers for diagnosis. MATERIALS AND METHODS: Subjects with amnestic MCI (n = 10; age, 72.2 ± 7.1 years) and normal cognition (n = 10; age, 70.1 ± 7.7 years) underwent DTI and T1-weighted MR imaging at 3T. Fractional anisotropy (FA), apparent diffusion coefficient (ADC), and cortical thickness were measured and compared between the MCI and control groups. We evaluated the diagnostic accuracy of 2 methods, either in combination or separately, using binary logistic regression and nonparametric statistical analyses for sensitivity, specificity, and accuracy. RESULTS: Decreased FA and increased ADC in WM regions of the frontal and temporal lobes and corpus callosum (CC) were observed in patients with MCI. Cortical thickness was decreased in GM regions of the frontal, temporal, and parietal lobes in patients with MCI. Changes in WM and cortical thickness seemed to be more pronounced in the left hemisphere compared with the right hemisphere. Furthermore, the combination of cortical thickness and DTI measurements in the left temporal areas improved the accuracy of differentiating MCI patients from control subjects compared with either measure alone. CONCLUSIONS: DTI and cortical thickness analyses may both serve as imaging markers to differentiate MCI from normal aging. Combined use of these 2 methods may improve the accuracy of MCI diagnosis.

Genomic Mapping and Survival Prediction in Glioblastoma: Molecular Subclassification Strengthened by Hemodynamic Imaging Biomarkers

PURPOSE To correlate tumor blood volume, measured by using dynamic susceptibility contrast material-enhanced T2*-weighted magnetic resonance (MR) perfusion studies, with patient survival and determine its association with molecular subclasses of glioblastoma (GBM). MATERIALS AND METHODS This HIPAA-compliant retrospective study was approved by institutional review board. Fifty patients underwent dynamic susceptibility contrast-enhanced T2*-weighted MR perfusion studies and had gene expression data available from the Cancer Genome Atlas. Relative cerebral blood volume (rCBV) (maximum rCBV [rCBV(max)] and mean rCBV [rCBV(mean)]) of the contrast-enhanced lesion as well as rCBV of the nonenhanced lesion (rCBV(NEL)) were measured. Patients were subclassified according to the Verhaak and Phillips classification schemas, which are based on similarity to defined genomic expression signature. We correlated rCBV measures with the molecular subclasses as well as with patient overall survival by using Cox regression analysis. RESULTS No statistically significant differences were noted for rCBV(max), rCBV(mean) of contrast-enhanced lesion or rCBV(NEL) between the four Verhaak classes or the three Phillips classes. However, increased rCBV measures are associated with poor overall survival in GBM. The rCBV(max) (P = .0131) is the strongest predictor of overall survival regardless of potential confounders or molecular classification. Interestingly, including the Verhaak molecular GBM classification in the survival model clarifies the association of rCBV(mean) with patient overall survival (hazard ratio: 1.46, P = .0212) compared with rCBV(mean) alone (hazard ratio: 1.25, P = .1918). Phillips subclasses are not predictive of overall survival nor do they affect the predictive ability of rCBV measures on overall survival. CONCLUSION The rCBV(max) measurements could be used to predict patient overall survival independent of the molecular subclasses of GBM; however, Verhaak classifiers provided additional information, suggesting that molecular markers could be used in combination with hemodynamic imaging biomarkers in the future.

Outcome Prediction in Patients with Glioblastoma by Using Imaging, Clinical, and Genomic Biomarkers: Focus on the Nonenhancing Component of the Tumor

PURPOSE To correlate patient survival with morphologic imaging features and hemodynamic parameters obtained from the nonenhancing region (NER) of glioblastoma (GBM), along with clinical and genomic markers. MATERIALS AND METHODS An institutional review board waiver was obtained for this HIPAA-compliant retrospective study. Forty-five patients with GBM underwent baseline imaging with contrast material-enhanced magnetic resonance (MR) imaging and dynamic susceptibility contrast-enhanced T2*-weighted perfusion MR imaging. Molecular and clinical predictors of survival were obtained. Single and multivariable models of overall survival (OS) and progression-free survival (PFS) were explored with Kaplan-Meier estimates, Cox regression, and random survival forests. RESULTS Worsening OS (log-rank test, P = .0103) and PFS (log-rank test, P = .0223) were associated with increasing relative cerebral blood volume of NER (rCBVNER), which was higher with deep white matter involvement (t test, P = .0482) and poor NER margin definition (t test, P = .0147). NER crossing the midline was the only morphologic feature of NER associated with poor survival (log-rank test, P = .0125). Preoperative Karnofsky performance score (KPS) and resection extent (n = 30) were clinically significant OS predictors (log-rank test, P = .0176 and P = .0038, respectively). No genomic alterations were associated with survival, except patients with high rCBVNER and wild-type epidermal growth factor receptor (EGFR) mutation had significantly poor survival (log-rank test, P = .0306; area under the receiver operating characteristic curve = 0.62). Combining resection extent with rCBVNER marginally improved prognostic ability (permutation, P = .084). Random forest models of presurgical predictors indicated rCBVNER as the top predictor; also important were KPS, age at diagnosis, and NER crossing the midline. A multivariable model containing rCBVNER, age at diagnosis, and KPS can be used to group patients with more than 1 year of difference in observed median survival (0.49-1.79 years). CONCLUSION Patients with high rCBVNER and NER crossing the midline and those with high rCBVNER and wild-type EGFR mutation showed poor survival. In multivariable survival models, however, rCBVNER provided unique prognostic information that went above and beyond the assessment of all NER imaging features, as well as clinical and genomic features.

Somatic mutations associated with MRI-derived volumetric features in glioblastoma

IntroductionMR imaging can noninvasively visualize tumor phenotype characteristics at the macroscopic level. Here, we investigated whether somatic mutations are associated with and can be predicted by MRI-derived tumor imaging features of glioblastoma (GBM).MethodsSeventy-six GBM patients were identified from The Cancer Imaging Archive for whom preoperative T1-contrast (T1C) and T2-FLAIR MR images were available. For each tumor, a set of volumetric imaging features and their ratios were measured, including necrosis, contrast enhancing, and edema volumes. Imaging genomics analysis assessed the association of these features with mutation status of nine genes frequently altered in adult GBM. Finally, area under the curve (AUC) analysis was conducted to evaluate the predictive performance of imaging features for mutational status.ResultsOur results demonstrate that MR imaging features are strongly associated with mutation status. For example, TP53-mutated tumors had significantly smaller contrast enhancing and necrosis volumes (p = 0.012 and 0.017, respectively) and RB1-mutated tumors had significantly smaller edema volumes (p = 0.015) compared to wild-type tumors. MRI volumetric features were also found to significantly predict mutational status. For example, AUC analysis results indicated that TP53, RB1, NF1, EGFR, and PDGFRA mutations could each be significantly predicted by at least one imaging feature.ConclusionMRI-derived volumetric features are significantly associated with and predictive of several cancer-relevant, drug-targetable DNA mutations in glioblastoma. These results may shed insight into unique growth characteristics of individual tumors at the macroscopic level resulting from molecular events as well as increase the use of noninvasive imaging in personalized medicine.

Multicenter imaging outcomes study of The Cancer Genome Atlas glioblastoma patient cohort: imaging predictors of overall and progression-free survival

BACKGROUND Despite an aggressive therapeutic approach, the prognosis for most patients with glioblastoma (GBM) remains poor. The aim of this study was to determine the significance of preoperative MRI variables, both quantitative and qualitative, with regard to overall and progression-free survival in GBM. METHODS We retrospectively identified 94 untreated GBM patients from the Cancer Imaging Archive who had pretreatment MRI and corresponding patient outcomes and clinical information in The Cancer Genome Atlas. Qualitative imaging assessments were based on the Visually Accessible Rembrandt Images feature-set criteria. Volumetric parameters were obtained of the specific tumor components: contrast enhancement, necrosis, and edema/invasion. Cox regression was used to assess prognostic and survival significance of each image. RESULTS Univariable Cox regression analysis demonstrated 10 imaging features and 2 clinical variables to be significantly associated with overall survival. Multivariable Cox regression analysis showed that tumor-enhancing volume (P = .03) and eloquent brain involvement (P < .001) were independent prognostic indicators of overall survival. In the multivariable Cox analysis of the volumetric features, the edema/invasion volume of more than 85 000 mm(3) and the proportion of enhancing tumor were significantly correlated with higher mortality (Ps = .004 and .003, respectively). CONCLUSIONS Preoperative MRI parameters have a significant prognostic role in predicting survival in patients with GBM, thus making them useful for patient stratification and endpoint biomarkers in clinical trials.

Magnetic resonance imaging with lateralized arterial spin labeling

We report the development of a new MRI technique which allows spins from right-sided arteries to be labeled separately from spins from left-sided arteries. This method uses two spatially-selective adiabatic inversion pulses to alternate the labeling of the right carotid and vertebral artery separate from the left carotid and vertebral artery. Normal volunteers were scanned on a clinical 1.5 T system and the resultant brain images correlated with the T2 anatomic images. Arterial anatomy was depicted using the new sequence and corresponded to the labeling scheme employed by the sequence. It was demonstrated that spatially selective inversion pulses permit the encoding of the spins within specific vascular origins and the observation of their run-off territory.

Diffusion tensor and functional magnetic resonance imaging of diffuse axonal injury and resulting language impairment.

Diffuse axonal injury (DAI) is a common aftermath of brain trauma. The diagnosis of DAI is often difficult using conventional magnetic resonance imaging (MRI). We report a diffusion tensor imaging (DTI) study of a patient who sustained DAI presenting with language impairment. Fractional anisotropy (FA) and DTI tractography revealed a reduction of white matter integrity in the left frontal and medial temporal areas. White matter damage identified by DTI was correlated with the patients language impairment as assessed by functional MRI (fMRI) and a neuropsychological exam. The findings demonstrate the utility of DTI for identifying white matter changes secondary to traumatic brain injury (TBI).

Whole-brain spectroscopic MRI biomarkers identify infiltrating margins in glioblastoma patients

BACKGROUND The standard of care for glioblastoma (GBM) is maximal safe resection followed by radiation therapy with chemotherapy. Currently, contrast-enhanced MRI is used to define primary treatment volumes for surgery and radiation therapy. However, enhancement does not identify the tumor entirely, resulting in limited local control. Proton spectroscopic MRI (sMRI), a method reporting endogenous metabolism, may better define the tumor margin. Here, we develop a whole-brain sMRI pipeline and validate sMRI metrics with quantitative measures of tumor infiltration. METHODS Whole-brain sMRI metabolite maps were coregistered with surgical planning MRI and imported into a neuronavigation system to guide tissue sampling in GBM patients receiving 5-aminolevulinic acid fluorescence-guided surgery. Samples were collected from regions with metabolic abnormalities in a biopsy-like fashion before bulk resection. Tissue fluorescence was measured ex vivo using a hand-held spectrometer. Tissue samples were immunostained for Sox2 and analyzed to quantify the density of staining cells using a novel digital pathology image analysis tool. Correlations among sMRI markers, Sox2 density, and ex vivo fluorescence were evaluated. RESULTS Spectroscopic MRI biomarkers exhibit significant correlations with Sox2-positive cell density and ex vivo fluorescence. The choline to N-acetylaspartate ratio showed significant associations with each quantitative marker (Pearsons ρ = 0.82, P < .001 and ρ = 0.36, P < .0001, respectively). Clinically, sMRI metabolic abnormalities predated contrast enhancement at sites of tumor recurrence and exhibited an inverse relationship with progression-free survival. CONCLUSIONS As it identifies tumor infiltration and regions at high risk for recurrence, sMRI could complement conventional MRI to improve local control in GBM patients.

Imaging-genomics reveals driving pathways of MRI derived volumetric tumor phenotype features in Glioblastoma

BackgroundGlioblastoma (GBM) tumors exhibit strong phenotypic differences that can be quantified using magnetic resonance imaging (MRI), but the underlying biological drivers of these imaging phenotypes remain largely unknown. An Imaging-Genomics analysis was performed to reveal the mechanistic associations between MRI derived quantitative volumetric tumor phenotype features and molecular pathways.MethodsOne hundred fourty one patients with presurgery MRI and survival data were included in our analysis. Volumetric features were defined, including the necrotic core (NE), contrast-enhancement (CE), abnormal tumor volume assessed by post-contrast T1w (tumor bulk or TB), tumor-associated edema based on T2-FLAIR (ED), and total tumor volume (TV), as well as ratios of these tumor components. Based on gene expression where available (n = 91), pathway associations were assessed using a preranked gene set enrichment analysis. These results were put into context of molecular subtypes in GBM and prognostication.ResultsVolumetric features were significantly associated with diverse sets of biological processes (FDR < 0.05). While NE and TB were enriched for immune response pathways and apoptosis, CE was associated with signal transduction and protein folding processes. ED was mainly enriched for homeostasis and cell cycling pathways. ED was also the strongest predictor of molecular GBM subtypes (AUC = 0.61). CE was the strongest predictor of overall survival (C-index = 0.6; Noether test, p = 4x10−4).ConclusionGBM volumetric features extracted from MRI are significantly enriched for information about the biological state of a tumor that impacts patient outcomes. Clinical decision-support systems could exploit this information to develop personalized treatment strategies on the basis of noninvasive imaging.

White Matter Hyperintensities and Changes in White Matter Integrity in Patients with Alzheimer’s Disease

IntroductionWhite matter hyperintensities (WMHs) are a risk factor for Alzheimer’s disease (AD). This study investigated the relationship between WMHs and white matter changes in AD using diffusion tensor imaging (DTI) and the sensitivity of each DTI index in distinguishing AD with WMHs.MethodsForty-four subjects with WMHs were included. Subjects were classified into three groups based on the Scheltens rating scale: 15 AD patients with mild WMHs, 12 AD patients with severe WMHs, and 17 controls with mild WMHs. Fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (DR), and axial diffusivity (DA) were analyzed using the region of interest and tract-based spatial statistics methods. Sensitivity and specificity of DTI indices in distinguishing AD groups from the controls were evaluated.ResultsAD patients with mild WMHs exhibited differences from control subjects in most DTI indices in the medial temporal and frontal areas; however, differences in DTI indices from AD patients with mild WMHs and AD patients with severe WMHs were found in the parietal and occipital areas. FA and DR were more sensitive measurements than MD and DA in differentiating AD patients from controls, while MD was a more sensitive measurement in distinguishing AD patients with severe WMHs from those with mild WMHs.ConclusionsWMHs may contribute to the white matter changes in AD brains, specifically in temporal and frontal areas. Changes in parietal and occipital lobes may be related to the severity of WMHs. DR may serve as an imaging marker of myelin deficits associated with AD.