OBJECTIVE:Image-guided laser interstitial thermal therapy (LITT) is a minimally invasive cytoreductive treatment for recurrent gliomas and tumors in eloquent regions. This technique was adapted to develop an image-guided glioblastoma (GBM) ablation model of recurrence. The efficacy of imaging biomarkers for evaluating tumor ablation and recurrence was evaluated by comparing the cytopathology and molecular signatures of primary and recurrent tumors. METHODS:Immune-compromised female rats were implanted with U251N tumor cells in 1 brain hemisphere (n = 20). Tumor growth was monitored using MRI and dynamic contrast-enhanced (DCE)-MRI. When tumors reached about 3.5-4.5 mm in diameter at their largest extent, they were ablated using a clinical LITT system (Visualase), guided by diffusion-weighted MRI. Five rats implanted with U251N tumors were used as unablated controls. Postablation DCE-MRI data were acquired at 24 hours and 2 and 4 weeks. Rats were euthanized at 2 and 4 weeks, and brain sections were stained for hematoxylin and eosin (H&E), human major histocompatibility complex (MHC), Ki-67, SOX2, OLIG2, and mesenchymal marker CD44. Additional rats with primary (n = 4) and postablation recurrent (n = 4) U251N tumors were used to compare molecular compositions using RNA sequencing (RNA-Seq). RESULTS:All rats survived the LITT procedure. Sham controls showed increased tumor burden by 2 weeks and were euthanized. In the ablated groups, MRI revealed little tumor tissue at 24 hours after ablation, signs of recurrence at 2 weeks, and significant tumor tissue at 4 weeks. Tumor DCE-MRI parameters showed elevated intratumoral vascular permeability values (i.e., blood-to-tissue forward volumetric transfer constant [Ktrans]) at pre-LITT imaging that shifted to the ablation site periphery at 24 hours after ablation. A progressive decrease in Ktrans was seen until 1 week after LITT. Increasing Ktrans values at 2 weeks and after 4 weeks coincided with histological evidence of tumor recurrence. RNA-Seq showed that cell cycle, cellular movement, and inflammatory disease genes were the most differentially expressed genes (DEGs) in recurrent tumors compared to primary tumors. CONCLUSIONS:These results suggest that DCE-MRI can serve as an efficient imaging biomarker for evaluating glioma cytoablation and for tracking post-LITT recurrence. Functions that influence cell cycle, cell motility, and inflammatory processes were upregulated in the recurrent tumor. This represents a new preclinical, image-guided tumor ablation model of recurrent GBM that can be used to test potential therapies.

The uniform lethality of glioblastoma (GBM) with a survival of less than 2 years despite best available therapy is attributed to treatment resistance due to DNA repair mechanisms that drive disease relapse and tumor heterogeneity. One prognostic factor identified as a reliable biomarker for GBM sensitivity to temozolomide (TMZ) and radiotherapy (RT) is the overexpression of O6-methylguanine-methyl-transferase (MGMT) enzyme. Patients with active MGMT were found to receive little benefit from TMZ and RT. They represent a group of great unmet need with no treatment options that significantly improve survival. Recently, several preclinical and clinical studies suggest that the alcohol aversion drug, disulfiram (DSF), inhibited MGMT and improved the efficacy of TMZ in GBM when combined with copper (Cu). However, phase II trial showed that there was no significant survival benefit from oral Cu/DSF. Nevertheless, the major limitation of oral Cu/DSF has been delivery of fragile DSF to the in vivo system. To address this limitation, we developed a novel delivery system using 2-hydroxypropyl beta cyclodextrin (HPβCD) encapsulating the Cu complex of DSF’s active metabolite, diethyldithiocarbamic acid (DDC). It was determined that HPβCD stabilized Cu(DDC)2. In vitro cell culture study revealed that HPβCD-Cu(DDC)2 inhibited MGMT through the ubiquitin-proteasome pathway. Inhibition of MGMT activity in cell cultures vastly increased the alkylation-induced DNA double-strand breaks, cytotoxicity, and the levels of apoptotic markers like histone family member X (γ-H2AX), JNK-P and cleavage of Poly [ADP-ribose] polymerase 1 (PARP-1). Preliminary intravenous delivery of HPβCD-Cu(DDC)2 in combination with TMZ in an MGMT-positive patient derived orthotopic xenograft (PDOX) model demonstrated tumor size regression. HPβCD-Cu(DDC)2 targets MGMT-145-cysteine and its unique cytotoxic mechanism circumvents MGMT-mediated TMZ resistance. This novel delivery system shows promise for overcoming MGMT-mediated resistance in GBM, offering a potential new therapeutic strategy.

Abstract Best current practice in the analysis of dynamic contrast enhanced (DCE)-MRI is to employ a voxel-by-voxel model selection from a hierarchy of nested models. This nested model selection (NMS) assumes that the observed time-trace of contrast-agent (CA) concentration within a voxel, corresponds to a singular physiologically nested model. However, admixtures of different models may exist within a voxel’s CA time-trace. This study introduces an unsupervised feature engineering technique (Kohonen-Self-Organizing-Map (K-SOM)) to estimate the voxel-wise probability of each nested model. Sixty-six immune-compromised-RNU rats were implanted with human U-251 N cancer cells, and DCE-MRI data were acquired from all the rat brains. The time-trace of change in the longitudinal-relaxivity (ΔR1) for all animals’ brain voxels was calculated. DCE-MRI pharmacokinetic (PK) analysis was performed using NMS to estimate three model regions: Model-1: normal vasculature without leakage, Model-2: tumor tissues with leakage without back-flux to the vasculature, Model-3: tumor vessels with leakage and back-flux. Approximately two hundred thirty thousand (229,314) normalized ΔR1 profiles of animals’ brain voxels along with their NMS results were used to build a K-SOM (topology-size: 8 × 8, with competitive-learning algorithm) and probability map of each model. K-fold nested-cross-validation (NCV, k = 10) was used to evaluate the performance of the K-SOM probabilistic-NMS (PNMS) technique against the NMS technique. The K-SOM PNMS’s estimation for the leaky tumor regions were strongly similar (Dice-Similarity-Coefficient, DSC = 0.774 [CI: 0.731–0.823], and 0.866 [CI: 0.828–0.912] for Models 2 and 3, respectively) to their respective NMS regions. The mean-percent-differences (MPDs, NCV, k = 10) for the estimated permeability parameters by the two techniques were: -28%, + 18%, and + 24%, for vp, Ktrans, and ve, respectively. The KSOM-PNMS technique produced microvasculature parameters and NMS regions less impacted by the arterial-input-function dispersion effect. This study introduces an unsupervised model-averaging technique (K-SOM) to estimate the contribution of different nested-models in PK analysis and provides a faster estimate of permeability parameters.

Two preclinical patient-derived orthotopic xenograft (PDOX) models of glioblastoma (GBM) were characterized using measures of tumor physiology. Plasma volume fraction (vp), blood-to-tissue forward volumetric transfer constant (Ktrans), and interstitial volume fraction (ve) were estimated via dynamic contrast-enhanced (DCE) MRI. Tumor blood flow (TBF) was estimated via continuous arterial spin-labeling and apparent diffusion coefficient of water (ADC) via spin-echo diffusion-weighted imaging. Tumor distribution volume at the tumor rim (VD) and peritumoral flux (Flux) were also estimated. Two neurosphere cell lines, taken from a primary human GBM (HF3016) and its recurrence (HF3177), were used in 15 immune-compromised athymic rats (n = 7 for HF3016; n = 8 for HF3177). When the tumors grew to about 3-4 mm in diameter, DCE-MRI data were acquired in a 7T magnet using a low molecular weight gadolinium-chelate contrast agent. DCE data were analyzed voxel-by-voxel using Patlak, extended Patlak, and Logan graphical methods. A data-driven model selection approach was applied to segment the tumor region, and regions of interest (ROIs) based on that segmentation were selected in the imaging slice having the largest tumor cross section. Summary ROI statistics of vascular measures were produced. The parameter estimates Ktrans, ve, vp, VD, ADC, TBF, and growth rates between the two models varied slightly, but the differences were not statistically significant (p > 0.05; t-tests). Flux estimates were found to be strongly correlated with VD values at the tumor rim in both tumor models (R2 = 0.84 and 0.91 for HF3016 and HF3177, respectively). These data report physiological properties of untreated GBM models that are representative of human disease both geno- and pheno-typically. Imaging biomarkers of vascular function in GBMs may aid in testing novel antiglioma therapies using these and other similar PDOX models for longitudinal, minimally invasive evaluations of treatment effects.

BACKGROUND:Recent studies have confirmed the effects of whole-brain radiation therapy (RT) on the blood-brain-barrier and vasculature permeability. Optimal therapeutic targeting of cancer depends on ability to distinguish tumor from normal tissue. PURPOSE:This study recruits nested model selection (NMS) and time-frequency analyses of the time-trace of contrast agent from dynamic-contrast-enhanced MRI information to characterize the acute (i.e., within hours) RT response of tumor and normal brain tissues in an animal model of brain tumors. METHODS:Twenty immune-compromised-RNU rats were implanted orthotopically with human U251N glioma cells. Twenty-eight days after the brain implantation, two DCE-MRI studies were performed 24 h apart. 20 Gy stereotactic radiation was delivered 1-6.5 h before the second MRI. NMS-based DCE-MRI analysis was performed to distinguish three different brain regions by model selection using a nested paradigm. Model 1 was characterized by non-leaky vasculature and considered as normal brain tissue. Model 2 was characterized by contrast agent (CA) movement predominantly in one direction, out of the vasculature, and was primarily associated with the tumor boundary. In contrast, Model 3 exhibited contrast agent movement in both directions, into and out of the vasculature, and corresponded to the tumor core. Time-traces of CA concentration from pre- and post-RT DCE-MRI data for the different models were analyzed using wavelet-based coherence and wavelet cross-spectrum phase analyses to characterize and rank the magnitude of RT-induced effects. Four distinct time-direction classes (in-phase/anti-phase with lead/lag time) were introduced to describe the impact of RT on CA concentration profiles, allowing for comparison of RT effects across different model-based zones of rat brains. RESULTS:The time-frequency analyses revealed both average lag and lead times between the pre- and post-RT CA concentration profiles for the three model regions. The average lag times were 2.882 s (95% CI: 2.606-3.157) for Model 1, 1.546 s (95% CI: 1.401-1.691) for Model 2, and 2.515 s (95% CI: 2.319-2.711) for Model 3, all exhibiting anti-phase oscillation. The average lead times were 1.892 s (95% CI: 1.757-2.028) for Model 1, 2.632 s (95% CI: 2.366-2.898) for Model 2, and 2.160 s (95% CI: 2.021-2.299) for Model 3, also with anti-phase oscillation. Results imply that compared to pre-RT, Model 1, 2, and 3 regions that correspond to normal tissue, periphery, and core of the tumor, show lag-time (2.882 [2.606 3.157] s), lead-time (2.632 [2.366 2.898] s), and lag-time (2.515 [2.319 2.711] s), in their post-RT time-trace of CA concentration, respectively. RT-induced lead/lag time changes were found to be more significant for the lower frequency components of the CA concentration profiles of all the three models. The analysis further revealed that Model 2 (tumor periphery) exhibited the most significant lead time, implying a shorter retainage-time of CA after radiation. Conversely, Model 1, normal tissue, showed the most pronounced lag-time, suggesting longer retainage-time of CA. CONCLUSIONS:This study demonstrates a novel approach to analyze the time-frequency information of DCE-MRI CA concentration profiles of the animal brain to detect acute changes in tumor and normal tissue physiology in response to RT that has clinical translatability and has potential to improve treatment planning and RT efficacy.

Purpose/Objective(s) Dynamic Contrast Enhanced (DCE) MRI information has shown promise as a surrogate endpoint for assessing tumor response to radiation therapy (RT) because the functional pharmacokinetic (PK) alterations occur earlier than morphological changes. This study proposes a probabilistic unsupervised mathematical model constructed from DCE-MRI information to predict the probability of RT-induced PK changes in a rat brain tumor model of human cancer. The changes in DCE-MRI collected within hours of RT have the potential to predict long term tumor response. Materials/Methods Twenty-four immune compromised RNU rats were implanted with human U-251N cancer cells to form an orthotopic glioma studied 28 days after implantation when tumors were 3 to 4 mm in diameter. For each rat, two DCE-MRI studies (Dual Gradient Echo) were performed 24h apart using a 7T MRI scanner. A single 20Gy stereotactic conformal radiation exposure, equal to the dose to cure half of a group of tumors, was performed before the second MRI (acquired 1-6.5 hours post RT). DCE-MRI PK analysis was performed using a probabilistic nested model selection (PNMS) technique to distinguish three different pathophysiological states of brain regions. The longitudinal relaxation time change (ΔR1) was calculated for each brain voxel, normalized to a fixed timespan (5min), and used to build three RT-based Kohonen Self Organizing Maps (KSOM, topology:5X5, competitive learning using a “best matching unit” strategy). The PNMS results were used to perform model averaging of the estimations provided by the RT-KSOMs. The trained KSOMs were applied to the pretreatment RT DCE-MRI information to predict the RT-induced alteration probability maps. Average values of the RT induced probability map for different model regions (PNMS maps at a 50% threshold) were calculated. Results Non-leaky and highly permeable tissues showed less RT-induced effect (mean probabilities were: 0.51±0.05 and 0.57±0.04, respectively) compared to the peritumoral regions (mean probability: 0.66±0.04) pertaining to leaky tissues with no back flux to the vasculature compartment. Conclusion This pilot study introduces an unsupervised predictive model combined with a PNMS technique to effectively capture subtle RT-induced alterations in the PK response of brain tissues according to their similarities/dissimilarities. Results of this study strongly agree with our previous findings, indicating that the contrast-enhanced rim of the tumors - the peritumoral zones correspond to infiltrative tumor borders that are preferentially affected by RT. To improve the accuracy of the prediction, an uncertainty analysis is warranted that is designed to estimate the intrinsic variations between the pre and post RT DCE-MRI signal and their influence on the KSOM feature space along with physiological-based signal calibration of the RT-effect.

Mechanical stress and fluid flow influence glioma cell phenotype in vitro, but measuring these quantities in vivo continues to be challenging. The purpose of this study was to predict these quantities in vivo, thus providing insight into glioma physiology and potential mechanical biomarkers that may improve glioma detection, diagnosis, and treatment. Image-based finite element models of human U251N orthotopic glioma in athymic rats were developed to predict structural stress and interstitial flow in and around each animal's tumor. In addition to accounting for structural stress caused by tumor growth, our approach has the advantage of capturing fluid pressure-induced structural stress, which was informed by in vivo interstitial fluid pressure (IFP) measurements. Because gliomas and the brain are soft, elevated IFP contributed substantially to tumor structural stress, even inverting this stress from compressive to tensile in the most compliant cases. The combination of tumor growth and elevated IFP resulted in a concentration of structural stress near the tumor boundary where it has the greatest potential to influence cell proliferation and invasion. MRI-derived anatomical geometries and tissue property distributions resulted in heterogeneous interstitial fluid flow with local maxima near cerebrospinal fluid spaces, which may promote tumor invasion and hinder drug delivery. In addition, predicted structural stress and interstitial flow varied markedly between irradiated and radiation-naïve animals. Our modeling suggests that relative to tumors in stiffer tissues, gliomas experience unusual mechanical conditions with potentially important biological (e.g., proliferation and invasion) and clinical consequences (e.g., drug delivery and treatment monitoring).

INTRODUCTION: The recent characterization of the glymphatic system has revived interest in the interaction of cerebrospinal fluid (CSF) with brain tissue via perivascular spaces. Traditional CSF tracers, such as gadolinium and iohexal, are large molecules that do not cross the blood brain barrier (BBB). The primary component of CSF, however, is water, which is BBB permeable. METHODS: Eight adult male rats underwent cisterna magna catheterization for intrathecal infusion. Brain MR images were obtained in a 7 tesla small-animal MRI system. Temporal T1-weighted images (T1WI) were obtained before, during, and after intrathecal infusion of D2O. Five rats then underwent ventricular kaolin injection for induction of hydrocephalus, after which repeat imaging with intrathecal D2O infusion was performed. RESULTS: Intrathecal deuterium infusion produced consistent changes on MRI imaging. Because conventional MRI does not sense deuterium, the T1WI showed a loss in proton signal with D2O infusion. Rapid D2O uptake of <2.5 minutes was noted in CSF spaces, brain tissue, and saggital and straight venous sinuses. CONCLUSIONS: Use of deuterium as an intrathecal contrast agent provides a physiologically realistic model of CSF behavior. Efforts are ongoing to quantify the relationship between MRI signal loss and deuterium tissue concentration for comparative analysis between healthy and hydrocephalic rats. The loss of MRI signal in the straight sinus after intrathecal infusion implicates the deep venous system in the clearance of CSF, suggesting that CSF is reabsorbed into the venous system via the brain tissue itself. Because traditional CSF tracers do not cross the BBB, the role of the brain tissue in the reabsorption of CSF has been overlooked.

Recurrent glioblastoma (rGBM) is highly aggressive and invasive. A reliable preclinical model that recapitulates these features is not presently available. The objective was to generate a preclinical rGBM model, characterize and compare its imaging, histological and molecular signatures in comparison to the primary tumor. Immune-suppressed, RNU/RNU female rats were implanted with U251N tumor cells in one brain hemisphere (n=33). Tumor progression in all rats was followed by longitudinal dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI). In 24 rats the tumor was ablated under diffusion-weighted imaging (DWI)-guided laser interstitial thermal therapy (LITT) at post-implantation 2-weeks. Cohorts from twenty ablated rats were euthanized at post-LITT 24 h, 2- and 4-weeks and, along with 5 unablated controls, used for hematoxylin and eosin (H&E) and Ki67 staining. Tissues from 4 other unablated and 4 recurrent tumors at post-LITT 2-weeks were used for RNAseq. All the rats survived the LITT procedure. Unablated controls showed increased tumor burden by post-implantation 2 weeks and were euthanized. In the LITT group, MRI showed little tumor tissue at 24 h, evidence of recurrence at 2 weeks and significant tumor tissue at 4 weeks and matched with histological evidence for tumor recurrence. Compared to the primary tumor, H&E staining showed increased vascular hyperplasia, mitotic bodies and hypoxic regions with pseudopalisading necrosis in the recurrent tumor. Increased KI67 staining in recurrent tumors suggested higher rates of proliferation and evidence of infiltration into host tissue. Pathway analyses demonstrated differentially expressed genes in the canonical pathways of hypoxia-inducible factor-alpha (HIF-1α), nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) and OX40 signaling. The genes for the following functions were significantly affected: cell cycle, cellular movement and cell morphology. Reliable preclinical rGBM models are few. These data suggest that this model replicates the features of human rGBM and can be useful in testing putative anti-glioma therapies.

Wavelet-analysis of DCE-MR images was performed to explore the association between radiomics information and relaxivity-change (ΔR1) in human U251n tumors grown in rat brains. Sixteen DCE-MRI experiments (8 rats before- and after- radiation) were studied. Wavelet-decomposition analysis was performed using ΔR1 time trace. Frequency-based localized approximations of ΔR1 with four degrees of regularities were estimated and compared to the volume-transfer-constant (Ktrans, calculated from a modified Tofts-model pharmacokinetic analysis). Results confirm strong associations between wavelet-based radiomic information and contrast uptake/flow/leakage in the tumor vasculature. Results suggest that radiomics has potential as a biomarker of tumor physiology.

In a study employing MRI-guided stereotactic radiotherapy (SRS) in two orthotopic rodent brain tumor models, the radiation dose yielding 50% survival (the TCD50) was sought. Syngeneic 9L cells, or human U-251N cells, were implanted stereotactically in 136 Fischer 344 rats or 98 RNU athymic rats, respectively. At approximately 7 days after implantation for 9L, and 18 days for U-251N, rats were imaged with contrast-enhanced MRI (CE-MRI) and then irradiated using a Small Animal Radiation Research Platform (SARRP) operating at 220 kV and 13 mA with an effective energy of ∼70 keV and dose rate of ∼2.5 Gy per min. Radiation doses were delivered as single fractions. Cone-beam CT images were acquired before irradiation, and tumor volumes were defined using co-registered CE-MRI images. Treatment planning using MuriPlan software defined four non-coplanar arcs with an identical isocenter, subsequently accomplished by the SARRP. Thus, the treatment workflow emulated that of current clinical practice. The study endpoint was animal survival to 200 days. The TCD50 inferred from Kaplan-Meier survival estimation was approximately 25 Gy for 9L tumors and below 20 Gy, but within the 95% confidence interval in U-251N tumors. Cox proportional-hazards modeling did not suggest an effect of sex, with the caveat of wide confidence intervals. Having identified the radiation dose at which approximately half of a group of animals was cured, the biological parameters that accompany radiation response can be examined.

Purpose Laser interstitial thermal therapy (LITT) is a minimally invasive, image-guided, cytoreductive procedure to treat recurrent glioblastoma. This study implemented dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) methods and employed a model selection paradigm to localize and quantify post-LITT blood-brain barrier (BBB) permeability in the ablation vicinity. Serum levels of neuron-specific enolase (NSE), a peripheral marker of increased BBB permeability, were measured. Methods Seventeen patients were enrolled in the study. Using an enzyme-linked immunosorbent assay, serum NSE was measured preoperatively, 24 hours postoperatively, and at two, eight, 12, and 16 weeks postoperatively, depending on postoperative adjuvant treatment. Of the 17 patients, four had longitudinal DCE-MRI data available, from which blood-to-brain forward volumetric transfer constant (Ktrans) data were assessed. Imaging was performed preoperatively, 24 hours postoperatively, and between two and eight weeks postoperatively. Results Serum NSE increased at 24 hours following ablation (p=0.04), peaked at two weeks, and returned to baseline by eight weeks postoperatively. Ktrans was found to be elevated in the peri-ablation periphery 24 hours after the procedure. This increase persisted for two weeks. Conclusion Following the LITT procedure, serum NSE levels and peri-ablation Ktrans estimated from DCE-MRI demonstrated increases during the first two weeks after ablation, suggesting transiently increased BBB permeability.

Here, we investigate radiomics-based characterization of tumor vascular and microenvironmental properties in an orthotopic rat brain tumor model measured using dynamic-contrast-enhanced (DCE) MRI. Thirty-two immune compromised-RNU rats implanted with human U-251N cancer cells were imaged using DCE-MRI (7Tesla, Dual-Gradient-Echo). The aim was to perform pharmacokinetic analysis using a nested model (NM) selection technique to classify brain regions according to vasculature properties considered as the source of truth. A two-dimensional convolutional-based radiomics analysis was performed on the raw-DCE-MRI of the rat brains to generate dynamic radiomics maps. The raw-DCE-MRI and respective radiomics maps were used to build 28 unsupervised Kohonen self-organizing-maps (K-SOMs). A Silhouette-Coefficient (SC), k-fold Nested-Cross-Validation (k-fold-NCV), and feature engineering analyses were performed on the K-SOMs’ feature spaces to quantify the distinction power of radiomics features compared to raw-DCE-MRI for classification of different Nested Models. Results showed that eight radiomics features outperformed respective raw-DCE-MRI in prediction of the three nested models. The average percent difference in SCs between radiomics features and raw-DCE-MRI was: 29.875% ± 12.922%, p < 0.001. This work establishes an important first step toward spatiotemporal characterization of brain regions using radiomics signatures, which is fundamental toward staging of tumors and evaluation of tumor response to different treatments.

We introduce and validate four adaptive models (AMs) to perform a physiologically based Nested-Model-Selection (NMS) estimation of such microvascular parameters as forward volumetric transfer constant, K-trans, plasma volume fraction, v(p), and extravascular, extracellular space, v(e), directly from Dynamic Contrast-Enhanced (DCE) MRI raw information without the need for an Arterial-Input Function (AIF). In sixty-six immune-compromised-RNU rats implanted with human U-251 cancer cells, DCE-MRI studies estimated pharmacokinetic (PK) parameters using a group-averaged radiological AIF and an extended Patlak-based NMS paradigm. One-hundred-ninety features extracted from raw DCE-MRI information were used to construct and validate (nested-cross-validation, NCV) four AMs for estimation of model-based regions and their three PK parameters. An NMS-based a priori knowledge was used to fine-tune the AMs to improve their performance. Compared to the conventional analysis, AMs produced stable maps of vascular parameters and nested-model regions less impacted by AIF-dispersion. The performance (Correlation coefficient and Adjusted R-squared for NCV test cohorts) of the AMs were: 0.914/0.834, 0.825/0.720, 0.938/0.880, and 0.890/0.792 for predictions of nested model regions, v(p), K-trans, and v(e), respectively. This study demonstrates an application of AMs that quickens and improves DCE-MRI based quantification of microvasculature properties of tumors and normal tissues relative to conventional approaches.

INTRODUCTION: Over the past ten years, the role of the glymphatic system has been defined and explored using various cerebrospinal fluid (CSF) tracers to track the movement of CSF through the perivascular spaces and into the brain interstitium. Glymphatic studies most commonly use gadolinium as a CSF tracer, which is not blood-brain-barrier (BBB) soluble. The primary component of CSF is water, however, which is BBB permeable. Cerebrospinal fluid may behave differently than large molecule tracers. Heavy water (D2O) is an isotope of water often used in physiology studies. We hypothesized that D2O would provide a traceable MR signal in the brain, allowing for more physiologically realistic models of CSF flow within the glymphatic system. METHODS: Two adult male rats underwent deuterium enhanced MR imaging. Images were obtained using a custom-made RF transmit/receive coil from Rapid MR International that is dual tuned to both proton (1H, 300.3 MHz) and deuterium (2H, 46.1 MHz) resonant frequencies for use in a 7 Tesla Bruker MRI system. After appropriate system calibration for optimization of deuterium MR signal with intravenous D2O infusion, the rats underwent spinal catheter implantation for intrathecal infusion. Images were taken during and after D2O infusion to evaluate uptake and clearance. RESULTS: Deuterium produced a imageable MR signal in the brain with intravenous and intrathecal infusion. Complete signal washout was noted within 10 minutes of stopping the intrathecal infusion. CONCLUSIONS: Deuterium MRI holds promise as a physiologic CSF tracer. As the technique is refined, it can be used for further CSF physiology studies. The rapid clearance of intrathecal D2O as compared to previous models using gadolinium supports our theory that CSF behaves differently than the large molecule tracers historically used to characterize its flow.

Abstract INTRODUCTION: of polymeric nanoparticles in cancer therapeutics is widely investigated since nanomedicine often enables the intratumoral delivery of drugs with increased efficacy with minimal side effects. In this study magnetic resonance imaging (MRI) monitoring was employed to study the therapeutic effect of nanocombretastatin (G3-CA4) in an orthotopic glioma model. Water insoluble combretastatin (CA4) was conjugated to a small-sized water soluble G3-succinimic acid PAMAM dendrimer. Tumor growth and vascular parameters were assessed non-invasively using MRI. In the brain, intravenously (iv) administered magnetic resonance (MR) contrast agents (CAs) can be used to measure the biomarkers of tumor perfusion - the blood-to-brain transvascular transfer constant (K-trans), tumor plasma volume (Vp), the extravascular extracellular space (Ve) and the blood volume (VD) in cerebral tumors. K-trans, Vp, Ve and VD showed the discrimination of tumorous vasculature responding to CA4 and G3-CA4 therapies from tumorous tissues. The average K-trans, Vp, Ve and VD decreased significantly 24 hours after G3-CA4 treatment. The results showed that G3-CA4 caused a change in tumor vasculature as measured with DCE-MRI. These effects were homogenous throughout the U-251 tumor. However, CA4 did not show any change in tumor vasculature. Imaging, histological and molecular signatures of the primary glioblastoma were compared. Large necrotic area was found together with destroyed blood vessels at the core of the tumor leaving viable tissue at the rim of the tumor. In vivo studies proved that the cytotoxicity of G3-CA4 conjugate was more effective against human U-251 tumor while CA4 alone was ineffective. Therefore, we have accomplished the effective transvascular delivery of G3-CA4 into human U251 glioma tumor with a compromised blood brain tumor barrier. In vivo studies proved that the cytotoxicity of G3-CA4 conjugate is more effective against human U-251 tumor than that of free CA4.

Purpose/Objective(s) Recent studies have shown that vascular parameters of brain tumors derived from DCE-MRI may act as potential biomarkers for radiation-induced acute effects. However, accurate characterization of the spatial regions affected by radiation therapy (RT) remains challenging. Here, we introduce an unsupervised adaptive model for classification and ranking of the RT-affected regions in an animal model of cerebral U-251n tumors. Materials/Methods Twenty-three immune-compromised-RNU rats were implanted with human U251n cancer cells to form an orthotopic glioma (IACUC #1509). For each rat, 28 days after implantation, two DCE-MRI studies (Dual Gradient Echo, DGE, FOV: 32 × 32 mm2, TR/(TE1-TE2) = 24 ms/(2 ms-4 ms), flip angle = 18°, 400 acquisitions, 1.55 sec interval with Magnevist contrast agent, CA injection at ∼ 24 sec) were performed 24h apart using a 7T MRI scanner. A single 20 Gy stereotactic radiation exposure was performed before the second MRI, which was acquired 1-6.5 hrs after RT. DCE-MRI analysis was done using a model selection technique to distinguish three different brain regions as follows: Normal vasculature (Model 1: No leakage, only plasma volume, vp, is estimated), leaky tumor tissues with no back-flux to the vasculature (Model 2: vp and forward volumetric transfer constant, Ktrans, are estimated), and leaky tumor tissues with back-flux (Model 3: vp, Ktrans, and interstitial volume fraction, ve, are estimated). Normalized time traces of DCE-MRI information (24 pre, and 24 post-RT for each rat, total of 64108 training datasets) of tumors and their soft surrounding normal tissues were extracted from the 3 different model regions. To eliminate high-dimensional data similarity, an unsupervised autoencoder (AE) was trained to map out the model-derived data into a feature space (latent variables, N=10). For each model, the pre and post RT latent variables were compared (by appropriate tests of significance: ANOVA/Welch, CI=95%) to reveal RT-discriminant features. Pearson correlation coefficients were used to compare the decoded data to rank the effect of RT on different models. Results The time trace of DCE-MRI information of rat brain in normal (Model 1, non-leaky) and highly permeable (Model 3) regions are less impacted by RT (Higher correlation between pre and post RT: r= 0.8518, p<0.0001 and r= 0.9040, p<0.0001 for Model 1 and Model 3, respectively) compared to the peritumoral regions pertaining to Model 2 (r= 0.8077, p<0.0001). Conclusion This pilot study suggests that among different brain regions, peritumoral zones (infiltrative tumor borders with enhanced rim) are highly affected by RT. Spatial assessment of RT-affected brain regions can play a key role in optimization of treatment planning in cancer patients, but presents a challenging task in conventional DCE-MRI. This study represents an important step toward classification and ranking the RT-affected brain spatial regions according to their vascular response following hypofractionated RT.