Gilad Liberman

Differentiation between vasogenic-edema versus tumor-infiltrative area in patients with glioblastoma during bevacizumab therapy: A longitudinal MRI study

BACKGROUND Treatment with bevacizumab is associated with substantial radiologic response in patients with glioblastoma (GB). However, following this initial response, changes in T2-weighted MRI signal may develop, suggesting an infiltrative pattern of tumor progression. The aim of this study was to differentiate between vasogenic-edema versus tumor-infiltrative area in GB patients. METHODS AND MATERIALS Fourteen patients with GB were longitudinally scanned, before and during intravenous bevacizumab therapy (5/10mg/kg every 2-weeks). A total of 40 MR scans including conventional, diffusion, dynamic susceptibility contrast, dynamic contrast enhancement imaging, and MR-spectroscopy (MRS) were analyzed. Classification of non-enhancing fluid-attenuation-inversion-recovery (FLAIR) area was performed based on mean diffusivity, cerebral blood volume and flow maps, and further characterized using multiple MRI parameters. RESULTS The non-enhancing FLAIR lesion area was classified into: vasogenic-edema, characterized by reduced perfusion and increased FLAIR values; or tumor-infiltrative area, characterized by increased perfusion. Tumor-infiltrative area demonstrated a higher malignant pattern on MRS compared to areas of vasogenic-edema. Substantial reductions of the enhanced T1-weighted (58 ± 10%) and hyperintense FLAIR (53 ± 9%) lesion volumes were detected mainly during the first weeks of therapy, with a shift to an infiltrative pattern of tumor progression thereafter, as detected by an increase in tumor-infiltrative area in the majority of patients, which correlated with progression-free survival (week 8: r=-0.86, p=0.003, week 16: r=-0.99, p=0.001). CONCLUSION Characterization of non-enhancing hyperintense FLAIR lesion area in GB patients can provide an MR-based biomarker, indicating a shift to an infiltrative progression pattern, and may improve therapy response assessment in patients following bevacizumab therapy.

T1 Mapping using variable flip angle SPGR data with flip angle correction

To improve the calculation of T1 relaxation time from a set of variable flip‐angle (FA) spoiled gradient recalled echo images.

Multi step selection in Ig H chains is initially focused on CDR3 and then on other CDR regions

Affinity maturation occurs through two selection processes: the choice of appropriate clones (clonal selection), and the internal evolution within clones, induced by somatic hyper-mutations, where high affinity mutants are selected for. When a final population of immunoglobulin sequences is observed, the genetic composition of this population is affected by a combination of these two processes. Different immune induced diseases can result from the failure of regulation of clonal selection or of the regulation of the within clone affinity maturation. In order to understand each of these processes separately, we propose a mixed lineage tree/sequence based method to detect within clone selection as defined by the effect of mutations on the average number of offspring. Specifically, we measure the imbalance in the number of leaves in lineage trees branches following synonymous and non-synonymous (NS) mutations. If a mutation is positively selected, we expect the number of leaves in the sub-tree below this mutation to be larger than in the parallel sub-tree without the mutation. The ratio between the number of leaves in such branches following NS mutations can be used to measure selection within a clone. We apply this method to the sampled Ig repertoire from multiple healthy volunteers and show that within clone selection is positive in the CDR2 region and either positive or negative in the CDR3 and FWR3 regions. Selection occurs already at the IgM isotype level mainly in the DH gene region, with a strong negative selection in the join region. This is followed in the later memory stages in the CDR2 region. We have not studied here the FWR1 and CDR1 regions. An important advantage of this method is that it is very weakly affected by the baseline mutation model or by sampling biases, as are most synonymous to NS mutations ratio based methods.

Automatic multi-modal MR tissue classification for the assessment of response to bevacizumab in patients with glioblastoma

BACKGROUND Current methods for evaluation of treatment response in glioblastoma are inaccurate, limited and time-consuming. This study aimed to develop a multi-modal MRI automatic classification method to improve accuracy and efficiency of treatment response assessment in patients with recurrent glioblastoma (GB). MATERIALS AND METHODS A modification of the k-Nearest-Neighbors (kNN) classification method was developed and applied to 59 longitudinal MR data sets of 13 patients with recurrent GB undergoing bevacizumab (anti-angiogenic) therapy. Changes in the enhancing tumor volume were assessed using the proposed method and compared with Macdonalds criteria and with manual volumetric measurements. The edema-like area was further subclassified into peri- and non-peri-tumoral edema, using both the kNN method and an unsupervised method, to monitor longitudinal changes. RESULTS Automatic classification using the modified kNN method was applicable in all scans, even when the tumors were infiltrative with unclear borders. The enhancing tumor volume obtained using the automatic method was highly correlated with manual measurements (N=33, r=0.96, p<0.0001), while standard radiographic assessment based on Macdonalds criteria matched manual delineation and automatic results in only 68% of cases. A graded pattern of tumor infiltration within the edema-like area was revealed by both automatic methods, showing high agreement. All classification results were confirmed by a senior neuro-radiologist and validated using MR spectroscopy. CONCLUSION This study emphasizes the important role of automatic tools based on a multi-modal view of the tissue in monitoring therapy response in patients with high grade gliomas specifically under anti-angiogenic therapy.

Accelerating compartmental modeling on a graphical processing unit

Compartmental modeling is a widely used tool in neurophysiology but the detail and scope of such models is frequently limited by lack of computational resources. Here we implement compartmental modeling on low cost Graphical Processing Units (GPUs), which significantly increases simulation speed compared to NEURON. Testing two methods for solving the current diffusion equation system revealed which method is more useful for specific neuron morphologies. Regions of applicability were investigated using a range of simulations from a single membrane potential trace simulated in a simple fork morphology to multiple traces on multiple realistic cells. A runtime peak 150-fold faster than the CPU was achieved. This application can be used for statistical analysis and data fitting optimizations of compartmental models and may be used for simultaneously simulating large populations of neurons. Since GPUs are forging ahead and proving to be more cost-effective than CPUs, this may significantly decrease the cost of computation power and open new computational possibilities for laboratories with limited budgets.

Classification of High-Grade Glioma into Tumor and Nontumor Components Using Support Vector Machine

BACKGROUND AND PURPOSE: Current imaging assessment of high-grade brain tumors relies on the Response Assessment in Neuro-Oncology criteria, which measure gross volume of enhancing and nonenhancing lesions from conventional MRI sequences. These assessments may fail to reliably distinguish tumor and nontumor. This study aimed to classify enhancing and nonenhancing lesion areas into tumor-versus-nontumor components. MATERIALS AND METHODS: A total of 140 MRI scans obtained from 32 patients with high-grade gliomas and 6 patients with brain metastases were included. Classification of lesion areas was performed using a support vector machine classifier trained on 4 components: enhancing and nonenhancing, tumor and nontumor, based on T1-weighted, FLAIR, and dynamic-contrast-enhancing MRI parameters. Classification results were evaluated by 2-fold cross-validation analysis of the training set and MR spectroscopy. Longitudinal changes of the component volumes were compared with Response Assessment in Neuro-Oncology criteria. RESULTS: Normalized T1-weighted values, FLAIR, plasma volume, volume transfer constant, and bolus-arrival-time parameters differentiated components. High sensitivity and specificity (100%) were obtained within the enhancing and nonenhancing areas. Longitudinal changes in component volumes correlated with the Response Assessment in Neuro-Oncology criteria in 27 patients; 5 patients (16%) demonstrated an increase in tumor component volumes indicating tumor progression. These changes preceded Response Assessment in Neuro-Oncology assessments by several months. Seven patients treated with bevacizumab showed a shift to an infiltrative pattern of progression. CONCLUSIONS: This study proposes an automatic classification method: segmented Response Assessment in Neuro-Oncology criteria based on advanced imaging that reliably differentiates tumor and nontumor components in high-grade gliomas. The segmented Response Assessment in Neuro-Oncology criteria may improve therapy-response assessment and provide earlier indication of progression.

Human cerebral blood volume measurements using dynamic contrast enhancement in comparison to dynamic susceptibility contrast MRI

IntroductionCerebral blood volume (CBV) is an important parameter for the assessment of brain tumors, usually obtained using dynamic susceptibility contrast (DSC) MRI. However, this method often suffers from low spatial resolution and high sensitivity to susceptibility artifacts and usually does not take into account the effect of tissue permeability. The plasma volume (vp) can also be extracted from dynamic contrast enhancement (DCE) MRI. The aim of this study was to investigate whether DCE can be used for the measurement of cerebral blood volume in place of DSC for the assessment of patients with brain tumors.MethodsTwenty-eight subjects (17 healthy subjects and 11 patients with glioblastoma) were scanned using DCE and DSC. vp and CBV values were measured and compared in different brain components in healthy subjects and in the tumor area in patients.ResultsSignificant high correlations were detected between vp and CBV in healthy subjects in the different brain components; white matter, gray matter, and arteries, correlating with the known increased tissue vascularity, and within the tumor area in patients.ConclusionThis work proposes the use of DCE as an alternative method to DSC for the assessment of blood volume, given the advantages of its higher spatial resolution, its lower sensitivity to susceptibility artifacts, and its ability to provide additional information regarding tissue permeability.

Estimate of within population incremental selection through branch imbalance in lineage trees

Incremental selection within a population, defined as limited fitness changes following mutation, is an important aspect of many evolutionary processes. Strongly advantageous or deleterious mutations are detected using the synonymous to non-synonymous mutations ratio. However, there are currently no precise methods to estimate incremental selection. We here provide for the first time such a detailed method and show its precision in multiple cases of micro-evolution. The proposed method is a novel mixed lineage tree/sequence based method to detect within population selection as defined by the effect of mutations on the average number of offspring. Specifically, we propose to measure the log of the ratio between the number of leaves in lineage trees branches following synonymous and non-synonymous mutations. The method requires a high enough number of sequences, and a large enough number of independent mutations. It assumes that all mutations are independent events. It does not require of a baseline model and is practically not affected by sampling biases. We show the methods wide applicability by testing it on multiple cases of micro-evolution. We show that it can detect genes and inter-genic regions using the selection rate and detect selection pressures in viral proteins and in the immune response to pathogens.

Reducing SAR requirements in multislice volumetric single-shot spatiotemporal MRI by two-dimensional RF pulses

Spatiotemporal encoding (SPEN) can deliver single‐scan MR images without folding complications and with increased robustness to chemical shift and susceptibility artifacts. Yet, it does so at the expense of relatively high specific absorption rates (SAR) owing to its reliance on frequency‐swept pulses. This study describes SPEN implementations aimed at full three‐dimensional (3D) multislice imaging, possessing reduced SAR thanks to an implementation based on new 2D radiofrequency (RF) pulses.

Robust diffusion tensor imaging by spatiotemporal encoding: Principles and in vivo demonstrations.

Evaluate the usefulness of single‐shot and of interleaved spatiotemporally encoded (SPEN) methods to perform diffusion tensor imaging (DTI) under various preclinical and clinical settings.