Recommended Resting-State fMRI Acquisition and Preprocessing Steps for Preoperative Mapping of Language and Motor and Visual Areas in Adult and Pediatric Patients with Brain Tumors and Epilepsy.

Resting-state (rs) fMRI has been shown to be useful for preoperative mapping of functional areas in patients with brain tumors and epilepsy. However, its lack of standardization limits its widespread use and hinders multicenter collaboration. The American Society of Functional Neuroradiology, American Society of Pediatric Neuroradiology, and the American Society of Neuroradiology Functional and Diffusion MR Imaging Study Group recommend specific rs-fMRI acquisition approaches and preprocessing steps that will further support rs-fMRI for future clinical use. A task force with expertise in fMRI from multiple institutions provided recommendations on the rs-fMRI steps needed for mapping of language, motor, and visual areas in adult and pediatric patients with brain tumor and epilepsy. These were based on an extensive literature review and expert consensus.Following rs-fMRI acquisition parameters are recommended: minimum 6-minute acquisition time; scan with eyes open with fixation; obtain rs-fMRI before both task-based fMRI and contrast administration; temporal resolution of ≤2 seconds; scanner field strength of 3T or higher. The following rs-fMRI preprocessing steps and parameters are recommended: motion correction (seed-based correlation analysis [SBC], independent component analysis [ICA]); despiking (SBC); volume censoring (SBC, ICA); nuisance regression of CSF and white matter signals (SBC); head motion regression (SBC, ICA); bandpass filtering (SBC, ICA); and spatial smoothing with a kernel size that is twice the effective voxel size (SBC, ICA).The consensus recommendations put forth for rs-fMRI acquisition and preprocessing steps will aid in standardization of practice and guide rs-fMRI program development across institutions. Standardized rs-fMRI protocols and processing pipelines are essential for multicenter trials and to implement rs-fMRI as part of standard clinical practice.

Severity of intervertebral disc herniation regulates cytokine and chemokine levels in patients with chronic radicular back pain.

OBJECTIVE: The contributions of intervertebral disc disease and subject-specific covariates to systemic inflammation in low back pain are unknown. We examined the effects of symptomatic disc herniation (DH) and MRI herniation severity on serum cytokine levels in clinical subjects. DESIGN: Cytokine levels from lumbar DH subjects (N = 78) were compared to control subjects (N = 57) accounting for effects of DH, age, body mass index (BMI) and gender. Effect of DH severity on cytokine levels was analyzed on subsets of subjects with acute or chronic pain. Serum cytokines were also analyzed in a subset of patients between pre- and 3 months post-surgery. RESULTS: Cytokine levels were elevated in the serum of patients with symptomatic DH, and the covariates age, BMI and gender significantly contributed to levels of some cytokines. Severity of herniation was a significant contributor to pain intensity (VAS), serum levels of HMGB1, PDGFbb, and IL-9. The relationship between DH severity and cytokine levels was confirmed in subjects with chronic, but not acute symptoms. Serum levels of macrophage migration inhibitory factor (MIF) decreased, whereas levels of CCL3, CCL11, CXCL1, and CXCL10 were significantly elevated post surgery. CONCLUSIONS: This study is the first to show that DH severity is coordinately associated with changes in serum levels of inflammatory cytokines in chronic pain subjects. HMGB1, PDGFbb and IL-9 are novel mediators of increasing DH severity, indicative of cellular damage, neuro-inflammation and angiogenesis. Resolution of inflammation was observed with decrease in MIF post surgery. However, elevated chemokine levels indicate ongoing remodeling and wound healing at 3-month time point.

Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas.

BACKGROUND AND PURPOSE: The World Health Organization has recently placed new emphasis on the integration of genetic information for gliomas. While tissue sampling remains the criterion standard, noninvasive imaging techniques may provide complimentary insight into clinically relevant genetic mutations. Our aim was to train a convolutional neural network to independently predict underlying molecular genetic mutation status in gliomas with high accuracy and identify the most predictive imaging features for each mutation. MATERIALS AND METHODS: MR imaging data and molecular information were retrospectively obtained from The Cancer Imaging Archives for 259 patients with either low- or high-grade gliomas. A convolutional neural network was trained to classify isocitrate dehydrogenase 1 (IDH1) mutation status, 1p/19q codeletion, and O6-methylguanine-DNA methyltransferase (MGMT) promotor methylation status. Principal component analysis of the final convolutional neural network layer was used to extract the key imaging features critical for successful classification. RESULTS: Classification had high accuracy: IDH1 mutation status, 94%; 1p/19q codeletion, 92%; and MGMT promotor methylation status, 83%. Each genetic category was also associated with distinctive imaging features such as definition of tumor margins, T1 and FLAIR suppression, extent of edema, extent of necrosis, and textural features. CONCLUSIONS: Our results indicate that for The Cancer Imaging Archives dataset, machine-learning approaches allow classification of individual genetic mutations of both low- and high-grade gliomas. We show that relevant MR imaging features acquired from an added dimensionality-reduction technique demonstrate that neural networks are capable of learning key imaging components without prior feature selection or human-directed training.

Dynamic Susceptibility Contrast-Enhanced MR Perfusion Imaging in Assessing Recurrent Glioblastoma Response to Superselective Intra-Arterial Bevacizumab Therapy.

BACKGROUND AND PURPOSE: Recurrent glioblastoma currently has no established standard of care. We evaluated the response of recurrent glioblastoma to superselective intra-arterial cerebral infusion of bevacizumab by using dynamic susceptibility contrast-enhanced MR perfusion imaging. We hypothesized that treatment response would be associated with decreased relative CBV and relative CBF. MATERIALS AND METHODS: Patients were accrued for this study from larger ongoing serial Phase I/II trials. Twenty-five patients (14 men, 11 women; median age, 55 years) were analyzed. Four distinct ROIs were chosen: 1) normal-appearing white matter on the contralateral side, 2) the location of the highest T1 enhancement in the lesion (maximum enhancing), 3) the location of highest relative CBV in the lesion (maximum relative CBV), and 4) nonenhancing T2 hyperintense signal abnormality surrounding the tumor (nonenhancing T2 hyperintensity). RESULTS: There was a statistically significant median percentage change of -32.34% (P = .001) in relative CBV in areas of maximum relative CBV following intra-arterial bevacizumab therapy. There was also a statistically significant median percentage decrease in relative CBF of -30.67 (P = .001) and -27.25 (P = .037) in areas of maximum relative CBV and maximum tumor enhancement, respectively. Last, a trend toward statistical significance for increasing relative CBV in nonenhancing T2 hyperintense areas (median percent change, 30.04; P = .069) was noted. CONCLUSIONS: Dynamic susceptibility contrast-enhanced MR perfusion imaging demonstrated a significant decrease in tumor perfusion metrics within recurrent glioblastomas in response to superselective intra-arterial cerebral infusion of bevacizumab; however, these changes did not correlate with time to progression or overall survival.

Meta-analysis of diffusion metrics for the prediction of tumor grade in gliomas.

BACKGROUND AND PURPOSE: Diffusion tensor metrics are potential in vivo quantitative neuroimaging biomarkers for the characterization of brain tumor subtype. This meta-analysis analyzes the ability of mean diffusivity and fractional anisotropy to distinguish low-grade from high-grade gliomas in the identifiable tumor core and the region of peripheral edema. MATERIALS AND METHODS: A meta-analysis of articles with mean diffusivity and fractional anisotropy data for World Health Organization low-grade (I, II) and high-grade (III, IV) gliomas, between 2000 and 2013, was performed. Pooled data were analyzed by using the odds ratio and mean difference. Receiver operating characteristic analysis was performed for patient-level data. RESULTS: The minimum mean diffusivity of high-grade gliomas was decreased compared with low-grade gliomas. High-grade gliomas had decreased average mean diffusivity values compared with low-grade gliomas in the tumor core and increased average mean diffusivity values in the peripheral region. High-grade gliomas had increased FA values compared with low-grade gliomas in the tumor core, decreased values in the peripheral region, and a decreased fractional anisotropy difference between the tumor core and peripheral region. CONCLUSIONS: The minimum mean diffusivity differs significantly with respect to the World Health Organization grade of gliomas. Statistically significant effects of tumor grade on average mean diffusivity and fractional anisotropy were observed, supporting the concept that high-grade tumors are more destructive and infiltrative than low-grade tumors. Considerable heterogeneity within the literature may be due to systematic factors in addition to underlying lesion heterogeneity.

Semiautomated volumetric measurement on postcontrast MR imaging for analysis of recurrent and residual disease in glioblastoma multiforme.

BACKGROUND AND PURPOSE: A limitation in postoperative monitoring of patients with glioblastoma is the lack of objective measures to quantify residual and recurrent disease. Automated computer-assisted volumetric analysis of contrast-enhancing tissue represents a potential tool to aid the radiologist in following these patients. In this study, we hypothesize that computer-assisted volumetry will show increased precision and speed over conventional 1D and 2D techniques in assessing residual and/or recurrent tumor. MATERIALS AND METHODS: This retrospective study included patients with native glioblastomas with MR imaging performed at 24-48 hours following resection and 2-4 months postoperatively. 1D and 2D measurements were performed by 2 neuroradiologists with Certificates of Added Qualification. Volumetry was performed by using manual segmentation and computer-assisted volumetry, which combines region-based active contours and a level set approach. Tumor response was assessed by using established 1D, 2D, and volumetric standards. Manual and computer-assisted volumetry segmentation times were compared. Interobserver correlation was determined among 1D, 2D, and volumetric techniques. RESULTS: Twenty-nine patients were analyzed. Discrepancy in disease status between 1D and 2D compared with computer-assisted volumetry was 10.3% (3/29) and 17.2% (5/29), respectively. The mean time for segmentation between manual and computer-assisted volumetry techniques was 9.7 minutes and <1 minute, respectively (P < .01). Interobserver correlation was highest for volumetric measurements (0.995; 95% CI, 0.990-0.997) compared with 1D (0.826; 95% CI, 0.695-0.904) and 2D (0.905; 95% CI, 0.828-0.948) measurements. CONCLUSIONS: Computer-assisted volumetry provides a reproducible and faster volumetric assessment of enhancing tumor burden, which has implications for monitoring disease progression and quantification of tumor burden in treatment trials.

Proton magnetic resonance spectroscopy of choroid plexus tumors in children.

A variety of lesions may present as intraventricular masses in children. We report quantitative proton magnetic resonance spectroscopy (MRS) of two intraventricular tumors of the choroid plexus: choroid plexus carcinoma (CPC) and choroid plexus papilloma (CPP). Both lesions were characterized by high levels of choline-containing compounds and a complete absence of creatine and the neuronal/axonal marker N-acetyl aspartate. The CPC showed higher levels of choline compared to the CPP, and it also had elevated lactate. These preliminary results, if confirmed in a larger cohort of patients, indicate that proton MRS may have a role in the presurgical diagnosis of choroid plexus tumors in children, which may also have important implications for therapy and prognosis.

Appearance of meningiomas on diffusion-weighted images: correlating diffusion constants with histopathologic findings.

BACKGROUND AND PURPOSE: Malignant and atypical meningiomas are prone to recurrence and aggressive growth, which affects treatment planning and prognostication. Investigators have used diffusion-weighted imaging and apparent diffusion coefficient (ADC) maps to compare tumor grade and cellularity with the histopathologic findings of intraaxial primary brain neoplasms. The purpose of this study was to determine whether the signal characteristics of meningiomas on diffusion-weighted images correlate with the average diffusion constant (Dav) from ADC maps and histopathologic findings and whether the Dav can reliably distinguish benign from malignant and atypical meningiomas. METHODS: Seventeen patients (13 women and four men; average age, 55 years) with meningiomas were prospectively studied using routine MR imaging and diffusion-weighted imaging with a single-shot gradient-echo echo-planar pulse sequence (6000/100 [TR/TE]) and b values of 0 and 1000. Signal characteristics on routine MR and diffusion-weighted images were compared with the histopathologic findings after resection by using World Health Organization criteria. Dav values were calculated within the tumor mass from ADC maps before resection. RESULTS: Four meningiomas were malignant or atypical (World Health Organization grades II and III). Dav values were lower than normal brain values (average, 0.52 +/- 0.12 x 10(-5) cm2/s; range, 0.45-0.69 x 10(-5) cm2/s) and were hyperintense on diffusion-weighted images and hypointense on ADC maps. Thirteen meningiomas were benign. Dav values were higher than normal brain values (average, 1.03 +/- 0.29 x 10(-5) cm2/s; range, 0.62-1.8 x 10(-5) cm2/s). On diffusion-weighted images and ADC maps, most were isointense. Five benign meningiomas had very high Dav values, bright signal on ADC maps, and distinct histopathologic findings, including microcysts, necrotic infarct, and organizing intratumoral hemorrhage. The difference in Dav values between malignant and benign meningiomas was statistically significant (P < .00029). CONCLUSION: Albeit a small sample size, meningiomas with low Dav tended to be malignant or highly atypical (P < .00029) whereas meningiomas with the highest Dav had increased water content due to either a specific histologic subtype of meningioma or the presence of associated pathologic abnormality.