Auto-segmentation of Adult-Type Diffuse Gliomas: Comparison of Transfer Learning-Based Convolutional Neural Network Model vs. Radiologists.

Segmentation of glioma is crucial for quantitative brain tumor assessment, to guide therapeutic research and clinical management, but very time-consuming. Fully automated tools for the segmentation of multi-sequence MRI are needed. We developed and pretrained a deep learning (DL) model using publicly available datasets A (n = 210) and B (n = 369) containing FLAIR, T2WI, and contrast-enhanced (CE)-T1WI. This was then fine-tuned with our institutional dataset (n = 197) containing ADC, T2WI, and CE-T1WI, manually annotated by radiologists, and split into training (n = 100) and testing (n = 97) sets. The Dice similarity coefficient (DSC) was used to compare model outputs and manual labels. A third independent radiologist assessed segmentation quality on a semi-quantitative 5-scale score. Differences in DSC between new and recurrent gliomas, and between uni or multifocal gliomas were analyzed using the Mann-Whitney test. Semi-quantitative analyses were compared using the chi-square test. We found that there was good agreement between segmentations from the fine-tuned DL model and ground truth manual segmentations (median DSC: 0.729, std-dev: 0.134). DSC was higher for newly diagnosed (0.807) than recurrent (0.698) (p < 0.001), and higher for unifocal (0.747) than multi-focal (0.613) cases (p = 0.001). Semi-quantitative scores of DL and manual segmentation were not significantly different (mean: 3.567 vs. 3.639; 93.8% vs. 97.9% scoring ≥ 3, p = 0.107). In conclusion, the proposed transfer learning DL performed similarly to human radiologists in glioma segmentation on both structural and ADC sequences. Further improvement in segmenting challenging postoperative and multifocal glioma cases is needed.

Neuroimaging of COVID-19.

We review the wide variety of common neuroimaging manifestations related to coronavirus disease 2019 (COVID-19) and COVID therapies, grouping the entities by likely pathophysiology, recognizing that the etiology of many entities remains uncertain. Direct viral invasion likely contributes to olfactory bulb abnormalities. COVID meningoencephalitis may represent direct viral infection and/or autoimmune inflammation. Para-infectious inflammation and inflammatory demyelination at the time of infection are likely primary contributors to acute necrotizing encephalopathy, cytotoxic lesion of the corpus callosum, and diffuse white matter abnormality. Later postinfectious inflammation and demyelination may manifest as acute demyelinating encephalomyelitis, Guillain-Barré syndrome, or transverse myelitis. The hallmark vascular inflammation and coagulopathy of COVID-19 may produce acute ischemic infarction, microinfarction contributing to white matter abnormality, space-occupying hemorrhage or microhemorrhage, venous thrombosis, and posterior reversible encephalopathy syndrome. Adverse effects of therapies including zinc, chloroquine/hydroxychloroquine, antivirals, and vaccines, and current evidence regarding "long COVID" is briefly reviewed. Finally, we present a case of bacterial and fungal superinfection related to immune dysregulation from COVID.

Toward More Accessible Fully Automated 3D Volumetric MRI Decision Trees for the Differential Diagnosis of Multiple System Atrophy, Related Disorders, and Age-Matched Healthy Subjects.

Differentiating multiple system atrophy (MSA) from related neurodegenerative movement disorders (NMD) is challenging. MRI is widely available and automated decision-tree analysis is simple, transparent, and resistant to overfitting. Using a retrospective cohort of heterogeneous clinical MRIs broadly sourced from a tertiary hospital system, we aimed to develop readily translatable and fully automated volumetric diagnostic decision-trees to facilitate early and accurate differential diagnosis of NMDs. 3DT1 MRI from 171 NMD patients (72 MSA, 49 PSP, 50 PD) and 171 matched healthy subjects were automatically segmented using Freesurfer6.0 with brainstem module. Decision trees employing substructure volumes and a novel volumetric pons-to-midbrain ratio (3D-PMR) were produced and tenfold cross-validation performed. The optimal tree separating NMD from healthy subjects selected cerebellar white matter, thalamus, putamen, striatum, and midbrain volumes as nodes. Its sensitivity was 84%, specificity 94%, accuracy 84%, and kappa 0.69 in cross-validation. The optimal tree restricted to NMD patients selected 3D-PMR, thalamus, superior cerebellar peduncle (SCP), midbrain, pons, and putamen as nodes. It yielded sensitivities/specificities of 94/84% for MSA, 72/96% for PSP, and 73/92% PD, with 79% accuracy and 0.62 kappa. There was correct classification of 16/17 MSA, 5/8 PSP, 6/8 PD autopsy-confirmed patients, and 6/8 MRIs that preceded motor symptom onset. Fully automated decision trees utilizing volumetric MRI data distinguished NMD patients from healthy subjects and MSA from other NMDs with promising accuracy, including autopsy-confirmed and pre-symptomatic subsets. Our open-source methodology is well-suited for widespread clinical translation. Assessment in even more heterogeneous retrospective and prospective cohorts is indicated.

Clinical Trial-Ready Patient Cohorts for Multiple System Atrophy: Coupling Biospecimen and iPSC Banking to Longitudinal Deep-Phenotyping.

Multiple system atrophy (MSA) is a fatal neurodegenerative disease of unknown etiology characterized by widespread aggregation of the protein alpha-synuclein in neurons and glia. Its orphan status, biological relationship to Parkinson's disease (PD), and rapid progression have sparked interest in drug development. One significant obstacle to therapeutics is disease heterogeneity. Here, we share our process of developing a clinical trial-ready cohort of MSA patients (69 patients in 2 years) within an outpatient clinical setting, and recruiting 20 of these patients into a longitudinal "n-of-few" clinical trial paradigm. First, we deeply phenotype our patients with clinical scales (UMSARS, BARS, MoCA, NMSS, and UPSIT) and tests designed to establish early differential diagnosis (including volumetric MRI, FDG-PET, MIBG scan, polysomnography, genetic testing, autonomic function tests, skin biopsy) or disease activity (PBR06-TSPO). Second, we longitudinally collect biospecimens (blood, CSF, stool) and clinical, biometric, and imaging data to generate antecedent disease-progression scores. Third, in our Mass General Brigham SCiN study (stem cells in neurodegeneration), we generate induced pluripotent stem cell (iPSC) models from our patients, matched to biospecimens, including postmortem brain. We present 38 iPSC lines derived from MSA patients and relevant disease controls (spinocerebellar ataxia and PD, including alpha-synuclein triplication cases), 22 matched to whole-genome sequenced postmortem brain. iPSC models may facilitate matching patients to appropriate therapies, particularly in heterogeneous diseases for which patient-specific biology may elude animal models. We anticipate that deeply phenotyped and genotyped patient cohorts matched to cellular models will increase the likelihood of success in clinical trials for MSA.

Clinical 7T MRI for epilepsy care: Value, patient selection, technical issues, and outlook.

Ultra-high-field 7.0 Tesla (T) MRI offers substantial gains in signal-to-noise ratio (SNR) over 3T and 1.5T, but for over two decades has remained a research tool, while 3T scanners have achieved widespread clinical use. This much slower translation of 7T relates to daunting technical challenges encountered in ultra-high-field human MR imaging. The recent introduction of United States Food and Drug Administration (FDA)-approved clinical 7T scanners promises to be a watershed for many 7T neuroimaging applications, including epilepsy imaging. The high SNR of 7T allows clinical imaging of fine neuroanatomic detail at unprecedented spatial resolution, helping with detection and differentiation of subtle, potentially treatable lesions undetectable or suboptimally assessed at 3T. The accompanying research paper reports our group's analysis of 7T MRI efficacy in epilepsy treatment planning. Here, we introduce the technical background and clinical approach we currently use, in order to assist clinical epileptologists and neuroimagers contemplating, creating, or referring patients to a clinical 7T epilepsy imaging service. We describe a tiered epilepsy imaging strategy and protocols designed to optimize 7T value and work around signal intensity variation and signal loss artifacts, which remain significant challenges to full exploitation of 7T clinical value. We describe FDA-approved techniques for mitigating these artifacts and briefly outline techniques currently under development, but not yet FDA approved. Finally, we discuss the major issues in 7T patient safety and toleration, outlining their physical causes and effects on workflow, and provide references to more comprehensive technical reviews for readers seeking greater technical detail.

7T versus 3T MRI in the presurgical evaluation of patients with drug-resistant epilepsy.

BACKGROUND AND PURPOSE: MRI has a crucial role in presurgical evaluation of drug-resistant focal epilepsy patients. Whether and how much 7T MRI further improves presurgical diagnosis compared to standard of care 3T MRI remains to be established. We investigate the added value 7T MRI offers in surgical candidates with remaining clinical uncertainty after 3T MRI. METHODS: 7T brain MRI was obtained on sequential patients with drug-resistant focal epilepsy undergoing presurgical evaluation at a comprehensive epilepsy center, including patients with and without suspected lesions on standard 3T MRI. Clinical information and 3T images informed the interpretation of 7T images. Detection of a new lesion on 7T or better characterization of a suspected lesion was considered to add value to the presurgical workup. RESULTS: Interpretable 7T MRI was acquired in 19 patients. 7T MRI identified a lesion relevant to the seizures in three of eight patients (38%) without a lesion on 3T MRI; no lesion in 7/11 patients (64%) with at least one suspected lesion on 3T MRI, contributing to the final classification of all seven as nonlesional; and confirmed and better characterized the lesion suspected at 3T MR in the remaining 4/11 patients. CONCLUSIONS: 7T MRI detected new lesions in over a third of 3T MRI nonlesional patients, confirmed and better characterized a 3T suspected lesion in one third of patients, and helped exclude a 3T suspected lesion in the remainder. Our initial experience suggests that 7T MRI adds value to surgical planning by improving detection and characterization of suspected brain lesions in drug-resistant focal epilepsy patients.

A New Framework of Designing Iterative Techniques for Image Deblurring.

In this work we present a framework of designing iterative techniques for image deblurring in inverse problem. The new framework is based on two observations about existing methods. We used Landweber method as the basis to develop and present the new framework but note that the framework is applicable to other iterative techniques. First, we observed that the iterative steps of Landweber method consist of a constant term, which is a low-pass filtered version of the already blurry observation. We proposed a modification to use the observed image directly. Second, we observed that Landweber method uses an estimate of the true image as the starting point. This estimate, however, does not get updated over iterations. We proposed a modification that updates this estimate as the iterative process progresses. We integrated the two modifications into one framework of iteratively deblurring images. Finally, we tested the new method and compared its performance with several existing techniques, including Landweber method, Van Cittert method, GMRES (generalized minimal residual method), and LSQR (least square), to demonstrate its superior performance in image deblurring.

A Comparative Retrospective Study of Immunotherapy RANO Versus Standard RANO Criteria in Glioblastoma Patients Receiving Immune Checkpoint Inhibitor Therapy.

OBJECTIVES: Real-time assessment of treatment response in glioblastoma (GBM) patients on immune checkpoint blockade (ICB) remains challenging because inflammatory effects of therapy may mimic progressive disease, and the temporal evolution of these inflammatory findings is poorly understood. We compare GBM patient response during ICB as assessed with the Immunotherapy Response Assessment in Neuro-Oncology (iRANO) and the standard Response Assessment in Neuro-Oncology (RANO) radiological criteria. METHODS: 49 GBM patients (seven newly diagnosed and 42 recurrent) treated with ICBs at a single institution were identified. Tumor burden was quantified on serial MR scans according to RANO criteria during ICB. Radiographic response assessment by iRANO and RANO were compared. RESULTS: 82% (40/49) of patients received anti-PD-1, 16% (8/49) received anti-PD-L1, and 2% (1/49) received anti-PD-1 and anti-CTLA4 treatment. Change in tumor burden and best overall response ranged from -100 to +557% (median: +48%). 12% (6/49) of patients were classified as concordant non-progressors by both RANO and iRANO (best response: one CR, one PR, and four SD). Another12% (6/49) had discordant assessments: 15% (6/41) of RANO grade progressive disease (PD) patients had iRANO grade of progressive disease unconfirmed (PDU). The final classification of these discordant patients was pseudoprogression (PsP) in three of six, PD in two of six, and PDU in one of six who went off study before the iRANO assessment of PDU. iRANO delayed diagnosis of PD by 42 and 93 days in the two PD patients. 76% (37/49) patients were classified as concordant PD by both RANO and iRANO. 12% (6/49) of all patients were classified as PsP, starting at a median of 12 weeks (range, 4-30 weeks) after ICB initiation. CONCLUSIONS: Standard RANO and iRANO have high concordance for assessing PD in patients within 6 months of ICB initiation. iRANO was beneficial in 6% (3/49) cases later proven to be PsP, but delayed confirmation of PD by <3 months in 4% (2/49). PsP occurred in 12% of patients, starting at up to 7 months after initiation of ICB. Further study to define the utility of modified RANO compared with iRANO in ICB GBM patients is needed.

Deep-Learning Detection of Cancer Metastases to the Brain on MRI.

BACKGROUND: Approximately one-fourth of all cancer metastases are found in the brain. MRI is the primary technique for detection of brain metastasis, planning of radiotherapy, and the monitoring of treatment response. Progress in tumor treatment now requires detection of new or growing metastases at the small subcentimeter size, when these therapies are most effective. PURPOSE: To develop a deep-learning-based approach for finding brain metastasis on MRI. STUDY TYPE: Retrospective. SEQUENCE: Axial postcontrast 3D T1 -weighted imaging. FIELD STRENGTH: 1.5T and 3T. POPULATION: A total of 361 scans of 121 patients were used to train and test the Faster region-based convolutional neural network (Faster R-CNN): 1565 lesions in 270 scans of 73 patients for training; 488 lesions in 91 scans of 48 patients for testing. From the 48 outputs of Faster R-CNN, 212 lesions in 46 scans of 18 patients were used for training the RUSBoost algorithm (MatLab) and 276 lesions in 45 scans of 30 patients for testing. ASSESSMENT: Two radiologists diagnosed and supervised annotation of metastases on brain MRI as ground truth. This data were used to produce a 2-step pipeline consisting of a Faster R-CNN for detecting abnormal hyperintensity that may represent brain metastasis and a RUSBoost classifier to reduce the number of false-positive foci detected. STATISTICAL TESTS: The performance of the algorithm was evaluated by using sensitivity, false-positive rate, and receiver's operating characteristic (ROC) curves. The detection performance was assessed both per-metastases and per-slice. RESULTS: Testing on held-out brain MRI data demonstrated 96% sensitivity and 20 false-positive metastases per scan. The results showed an 87.1% sensitivity and 0.24 false-positive metastases per slice. The area under the ROC curve was 0.79. CONCLUSION: Our results showed that deep-learning-based computer-aided detection (CAD) had the potential of detecting brain metastases with high sensitivity and reasonable specificity. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:1227-1236.

Reversibility of impaired brain structures after transsphenoidal surgery in Cushing's disease: a longitudinal study based on an artificial intelligence-assisted tool.

OBJECTIVE: Cushing's disease (CD) involves brain impairments caused by excessive cortisol. Whether these impairments are reversible in remitted CD after surgery has long been controversial due to a lack of high-quality longitudinal studies. In this study the authors aimed to assess the reversibility of whole-brain changes in remitted CD after transsphenoidal surgery (TSS), and its correlations with clinical and hormonal parameters, in the largest longitudinal study cohort to date for CD patient brain analysis. METHODS: Fifty patients with pathologically diagnosed CD and 36 matched healthy controls (HCs) were enrolled in a tertiary comprehensive hospital and national pituitary disease registry center in China. 3-T MRI studies were analyzed using an artificial intelligence-assisted web-based autosegmentation tool to quantify 3D brain volumes. Clinical parameters as well as levels of serum cortisol, adrenocorticotrophic hormone (ACTH), and 24-hour urinary free cortisol were collected for the correlation analysis. All CD patients underwent TSS and 46 patients achieved remission. All clinical, hormonal, and MRI parameters were reevaluated at the 3-month follow-up after surgery. RESULTS: Widespread brain volume loss was observed in active CD patients compared with HCs, including total gray matter (p = 0.003, with false discovery rate [FDR] correction) and the frontal, parietal, occipital, and temporal lobes; insula; cingulate lobe; and enlargement of lateral and third ventricles (p < 0.05, corrected with FDR). All affected brain regions improved significantly after TSS (p < 0.05, corrected with FDR). In patients with remitted CD, total gray matter and most brain regions (except the frontal and temporal lobes) showed full recovery of volume, with volumes that did not differ from those of HCs (p > 0.05, corrected with FDR). ACTH and serum cortisol changes were negatively correlated with brain volume changes during recovery (p < 0.05). CONCLUSIONS: This study demonstrates the rapid reversal of total gray matter loss in remitted CD. The combination of full recovery areas and partial recovery areas after TSS is consistent with the incomplete recovery of memory and cognitive function observed in CD patients in clinical practice. Correlation analyses suggest that ACTH and serum cortisol levels are reliable serum biomarkers of brain recovery for clinical use after surgery.

A neural network approach to segment brain blood vessels in digital subtraction angiography.

BACKGROUND AND OBJECTIVE: Cerebrovascular diseases (CVDs) affect a large number of patients and often have devastating outcomes. The hallmarks of CVDs are the abnormalities formed on brain blood vessels, including protrusions, narrows, widening, and bifurcation of the blood vessels. CVDs are often diagnosed by digital subtraction angiography (DSA) yet the interpretation of DSA is challenging as one must carefully examine each brain blood vessel. The objective of this work is to develop a computerized analysis approach for automated segmentation of brain blood vessels. METHODS: In this work, we present a U-net based deep learning approach, combined with pre-processing, to track and segment brain blood vessels in DSA images. We compared the results given by the deep learning approach with manually marked ground truth using accuracy, sensitivity, specificity, and Dice coefficient. RESULTS: Our results showed that the proposed approach achieved an accuracy of 0.978, with a standard deviation of 0.00796, a sensitivity of 0.76 with a standard deviation of 0.096, a specificity of 0.994 with a standard deviation of 0.0036, and an average Dice coefficient was 0.8268 with a standard deviation of 0.052. CONCLUSIONS: Our findings show that the deep learning approach can achieve satisfactory performance as a computer-aided analysis tool to assist clinicians in diagnosing CVDs.

CerebroVis: Designing an Abstract yet Spatially Contextualized Cerebral Artery Network Visualization.

Blood circulation in the human brain is supplied through a network of cerebral arteries. If a clinician suspects a patient has a stroke or other cerebrovascular condition, they order imaging tests. Neuroradiologists visually search the resulting scans for abnormalities. Their visual search tasks correspond to the abstract network analysis tasks of browsing and path following. To assist neuroradiologists in identifying cerebral artery abnormalities, we designed CerebroVis, a novel abstract-yet spatially contextualized-cerebral artery network visualization. In this design study, we contribute a novel framing and definition of the cerebral artery system in terms of network theory and characterize neuroradiologist domain goals as abstract visualization and network analysis tasks. Through an iterative, user-centered design process we developed an abstract network layout technique which incorporates cerebral artery spatial context. The abstract visualization enables increased domain task performance over 3D geometry representations, while including spatial context helps preserve the user's mental map of the underlying geometry. We provide open source implementations of our network layout technique and prototype cerebral artery visualization tool. We demonstrate the robustness of our technique by successfully laying out 61 open source brain scans. We evaluate the effectiveness of our layout through a mixed methods study with three neuroradiologists. In a formative controlled experiment our study participants used CerebroVis and a conventional 3D visualization to examine real cerebral artery imaging data to identify a simulated intracranial artery stenosis. Participants were more accurate at identifying stenoses using CerebroVis (absolute risk difference 13%). A free copy of this paper, the evaluation stimuli and data, and source code are available at osf.io/e5sxt.

Frequency and Evolution of New Postoperative Enhancement on 3 Tesla Intraoperative and Early Postoperative Magnetic Resonance Imaging.

BACKGROUND: Intraoperative magnetic resonance imaging (IO-MRI) provides real-time assessment of extent of resection of brain tumor. Development of new enhancement during IO-MRI can confound interpretation of residual enhancing tumor, although the incidence of this finding is unknown. OBJECTIVE: To determine the frequency of new enhancement during brain tumor resection on intraoperative 3 Tesla (3T) MRI. To optimize the postoperative imaging window after brain tumor resection using 1.5 and 3T MRI. METHODS: We retrospectively evaluated 64 IO-MRI performed for patients with enhancing brain lesions referred for biopsy or resection as well as a subset with an early postoperative MRI (EP-MRI) within 72 h of surgery (N = 42), and a subset with a late postoperative MRI (LP-MRI) performed between 120 h and 8 wk postsurgery (N = 34). Three radiologists assessed for new enhancement on IO-MRI, and change in enhancement on available EP-MRI and LP-MRI. Consensus was determined by majority response. Inter-rater agreement was assessed using percentage agreement. RESULTS: A total of 10 out of 64 (16%) of the IO-MRI demonstrated new enhancement. Seven of 10 patients with available EP-MRI demonstrated decreased/resolved enhancement. One out of 42 (2%) of the EP-MRI demonstrated new enhancement, which decreased on LP-MRI. Agreement was 74% for the assessment of new enhancement on IO-MRI and 81% for the assessment of new enhancement on the EP-MRI. CONCLUSION: New enhancement occurs in intraoperative 3T MRI in 16% of patients after brain tumor resection, which decreases or resolves on subsequent MRI within 72 h of surgery. Our findings indicate the opportunity for further study to optimize the postoperative imaging window.

A New Design in Iterative Image Deblurring for Improved Robustness and Performance.

In many applications, image deblurring is a pre-requisite to improve the sharpness of an image before it can be further processed. Iterative methods are widely used for deblurring images but care must be taken to ensure that the iterative process is robust, meaning that the process does not diverge and reaches the solution reasonably fast, two goals that sometimes compete against each other. In practice, it remains challenging to choose parameters for the iterative process to be robust. We propose a new approach consisting of relaxed initialization and pixel-wise updates of the step size for iterative methods to achieve robustness. The first novel design of the approach is to modify the initialization of existing iterative methods to stop a noise term from being propagated throughout the iterative process. The second novel design is the introduction of a vectorized step size that is adaptively determined through the iteration to achieve higher stability and accuracy in the whole iterative process. The vectorized step size aims to update each pixel of an image individually, instead of updating all the pixels by the same factor. In this work, we implemented the above designs based on the Landweber method to test and demonstrate the new approach. Test results showed that the new approach can deblur images from noisy observations and achieve a low mean squared error with a more robust performance.

Buparlisib in Patients With Recurrent Glioblastoma Harboring Phosphatidylinositol 3-Kinase Pathway Activation: An Open-Label, Multicenter, Multi-Arm, Phase II Trial.

PURPOSE: Phosphatidylinositol 3-kinase (PI3K) signaling is highly active in glioblastomas. We assessed pharmacokinetics, pharmacodynamics, and efficacy of the pan-PI3K inhibitor buparlisib in patients with recurrent glioblastoma with PI3K pathway activation. METHODS: This study was a multicenter, open-label, multi-arm, phase II trial in patients with PI3K pathway-activated glioblastoma at first or second recurrence. In cohort 1, patients scheduled for re-operation after progression received buparlisib for 7 to 13 days before surgery to evaluate brain penetration and modulation of the PI3K pathway in resected tumor tissue. In cohort 2, patients not eligible for re-operation received buparlisib until progression or unacceptable toxicity. Once daily oral buparlisib 100 mg was administered on a continuous 28-day schedule. Primary end points were PI3K pathway inhibition in tumor tissue and buparlisib pharmacokinetics in cohort 1 and 6-month progression-free survival (PFS6) in cohort 2. RESULTS: Sixty-five patients were treated (cohort 1, n = 15; cohort 2, n = 50). In cohort 1, reduction of phosphorylated AKTS473 immunohistochemistry score was achieved in six (42.8%) of 14 patients, but effects on phosphoribosomal protein S6S235/236 and proliferation were not significant. Tumor-to-plasma drug level was 1.0. In cohort 2, four (8%) of 50 patients reached 6-month PFS6, and the median PFS was 1.7 months (95% CI, 1.4 to 1.8 months). The most common grade 3 or greater adverse events related to treatment were lipase elevation (n = 7 [10.8%]), fatigue (n = 4 [6.2%]), hyperglycemia (n = 3 [4.6%]), and elevated ALT (n = 3 [4.6%]). CONCLUSION: Buparlisib had minimal single-agent efficacy in patients with PI3K-activated recurrent glioblastoma. Although buparlisib achieved significant brain penetration, the lack of clinical efficacy was explained by incomplete blockade of the PI3K pathway in tumor tissue. Integrative results suggest that additional study of PI3K inhibitors that achieve more-complete pathway inhibition may still be warranted.

Functional MRI Task Comparison for Language Mapping in Neurosurgical Patients.

BACKGROUND AND PURPOSE: Language task-based functional MRI (fMRI) is increasingly used for presurgical planning in patients with brain lesions. Different paradigms elicit activations of different components of the language network. The aim of this study is to optimize fMRI clinical usage by comparing the effectiveness of three language tasks for language lateralization and localization in a large group of patients with brain lesions. METHODS: We analyzed fMRI data from a sequential retrospective cohort of 51 patients with brain lesions who underwent presurgical fMRI language mapping. We compared the effectiveness of three language tasks (Antonym Generation, Sentence Completion (SC), and Auditory Naming) for lateralizing language function and for activating cortex within patient-specific regions-of-interest representing eloquent language areas, and assessed the degree of spatial overlap of the areas of activation elicited by each task. RESULTS: The tasks were similarly effective for lateralizing language within the anterior language areas. The SC task produced higher laterality indices within the posterior language areas and had a significantly higher agreement with the clinical report. Dice coefficients between the task pairs were in the range of .351-.458, confirming substantial variation in the components of the language network activated by each task. CONCLUSIONS: SC task consistently produced large activations within the dominant hemisphere and was more effective for lateralizing language within the posterior language areas. The low degree of spatial overlap among the tasks strongly supports the practice of using a battery of tasks to help the surgeon to avoid eloquent language areas.

Clinical Validation of Automatable Gaussian Normalized CBV in Brain Tumor Analysis: Superior Reproducibility and Slightly Better Association with Survival than Current Standard Manual Normal Appearing White Matter Normalization.

PURPOSE: To validate Gaussian normalized cerebral blood volume (GN-nCBV) by association with overall survival (OS) in newly diagnosed glioblastoma patients and compare this association with current standard white matter normalized cerebral blood volume (WN-nCBV). METHODS: We retrieved spin-echo echo-planar dynamic susceptibility contrast MRI acquired after maximal resection and prior to radiation therapy between 2006 and 2011 in 51 adult patients (28 male, 23 female; age 23-87 years) with newly diagnosed glioblastoma. Software code was developed in house to perform Gaussian normalization of CBV to the standard deviation of the whole brain CBV. Three expert readers manually selected regions of interest in tumor and normal-appearing white matter on CBV maps. Receiver operating characteristics (ROC) curves associating nCBV with 15-month OS were calculated for both GN-nCBV and WN-nCBV. Reproducibility and interoperator variability were compared using within-subject coefficient of variation (wCV) and intraclass correlation coefficients (ICCs). RESULTS: GN-nCBV ICC (≥0.82) and wCV (≤21%) were superior to WN-nCBV ICC (0.54-0.55) and wCV (≥46%). The area under the ROC curve analysis demonstrated both GN-nCBV and WN-nCBV to be good predictors of OS, but GN-nCBV was consistently superior, although the difference was not statistically significant. CONCLUSION: GN-nCBV has a slightly better association with clinical gold standard OS than conventional WM-nCBV in our glioblastoma patient cohort. This equivalent or superior validity, combined with the advantages of higher reproducibility, lower interoperator variability, and easier automation, makes GN-nCBV superior to WM-nCBV for clinical and research use in glioma patients. We recommend widespread adoption and incorporation of GN-nCBV into commercial dynamic susceptibility contrast processing software.

Normalization of ADC does not improve correlation with overall survival in patients with high-grade glioma (HGG).

Mixed reports leave uncertainty about whether normalization of apparent diffusion coefficient (ADC) to a within-subject white matter reference is necessary for assessment of tumor cellularity. We tested whether normalization improves the previously reported correlation of resection margin ADC with 15-month overall survival (OS) in HGG patients. Spin-echo echo-planar DWI was retrieved from 3 T MRI acquired between maximal resection and radiation in 37 adults with new-onset HGG (25 glioblastoma; 12 anaplastic astrocytoma). ADC maps were produced with the FSL DTIFIT tool (Oxford Centre for Functional MRI). 3 neuroradiologists manually selected regions of interest (ROI) in normal appearing white matter (NAWM) and in non-enhancing tumor (NT) < 2 cm from the margin of residual enhancing tumor or resection cavity. Normalized ADC (nADC) was computed as the ratio of absolute NT ADC to NAWM ADC. Reproducibility of nADC and absolute ADC among the readers' ROI was assessed using intra-class correlation coefficient (ICC) and within-subject coefficient of variation (wCV). Correlations of ADC and nADC with OS were compared using receiver operating characteristics (ROC) analysis. A p value 0.05 was considered statistically significant. Both mean ADC and nADC differed significantly between patients subgrouped by 15-month OS (p = 0.0014 and 0.0073 respectively). wCV and ICC among the readers were similar for absolute and normalized ADC. In ROC analysis of correlation with OS, nADC did not perform significantly better than absolute ADC. Normalization does not significantly improve the correlation of absolute ADC with OS in HGG, suggesting that normalization is not necessary for clinical or research ADC analysis in HGG patients.