Widespread correction of brain pathology in feline alpha-mannosidosis by dose escalation of intracisternal AAV vector injection.

Alpha-mannosidosis is caused by a genetic deficiency of lysosomal alpha-mannosidase, leading to the widespread presence of storage lesions in the brain and other tissues. Enzyme replacement therapy is available but is not approved for treating the CNS, since the enzyme does not penetrate the blood-brain barrier. However, intellectual disability is a major manifestation of the disease; thus, a complimentary treatment is needed. While enzyme replacement therapy into the brain is technically feasible, it requires ports and frequent administration over time that are difficult to manage medically. Infusion of adeno-associated viral vectors into the cerebrospinal fluid is an attractive route for broadly targeting brain cells. We demonstrate here the widespread post-symptomatic correction of the globally distributed storage lesions by infusion of a high dose of AAV1-feline alpha-mannosidase (fMANB) into the CSF via the cisterna magna in the gyrencephalic alpha-mannosidosis cat brain. Significant improvements in clinical parameters occurred, and widespread global correction was documented pre-mortem by non-invasive magnetic resonance imaging. Postmortem analysis demonstrated high levels of MANB activity and reversal of lysosomal storage lesions throughout the brain. Thus, CSF treatment by adeno-associated viral vector gene therapy appears to be a suitable complement to systemic enzyme replacement therapy to potentially treat the whole patient.

Non-Invasive Assessment of Isocitrate Dehydrogenase-Mutant Gliomas Using Optimized Proton Magnetic Resonance Spectroscopy on a Routine Clinical 3-Tesla MRI.

PURPOSE: The isocitrate dehydrogenase (IDH) mutation has become one of the most important prognostic biomarkers in glioma management, indicating better treatment response and prognosis. IDH mutations confer neomorphic activity leading to the conversion of alpha-ketoglutarate (α-KG) to 2-hydroxyglutarate (2HG). The purpose of this study was to investigate the clinical potential of proton MR spectroscopy (1H-MRS) in identifying IDH-mutant gliomas by detecting characteristic resonances of 2HG and its complex interplay with other clinically relevant metabolites. MATERIALS AND METHODS: Thirty-two patients with suspected infiltrative glioma underwent a single-voxel (SVS, n = 17) and/or single-slice-multivoxel (1H-MRSI, n = 15) proton MR spectroscopy (1H-MRS) sequence with an optimized echo-time (97 ms) on 3T-MRI. Spectroscopy data were analyzed using the linear combination (LC) model. Cramér-Rao lower bound (CRLB) values of <40% were considered acceptable for detecting 2HG and <20% for other metabolites. Immunohistochemical analyses for determining IDH mutational status were subsequently performed from resected tumor specimens and findings were compared with the results from spectral data. Mann-Whitney and chi-squared tests were performed to ascertain differences in metabolite levels between IDH-mutant and IDH-wild-type gliomas. Receiver operating characteristic (ROC) curve analyses were also performed. RESULTS: Data from eight cases were excluded due to poor spectral quality or non-tumor-related etiology, and final data analyses were performed from 24 cases. Of these cases, 9/12 (75%) were correctly identified as IDH-mutant or IDH-wildtype gliomas through SVS and 10/12 (83%) through 1H-MRSI with an overall concordance rate of 79% (19/24). The sensitivity, specificity, positive predictive value, and negative predictive value were 80%, 77%, 86%, and 70%, respectively. The metabolite 2HG was found to be significant in predicting IDH-mutant gliomas through the chi-squared test (p < 0.01). The IDH-mutant gliomas also had a significantly higher NAA/Cr ratio (1.20 ± 0.09 vs. 0.75 ± 0.12 p = 0.016) and lower Glx/Cr ratio (0.86 ± 0.078 vs. 1.88 ± 0.66; p = 0.029) than those with IDH wild-type gliomas. The areas under the ROC curves for NAA/Cr and Glx/Cr were 0.808 and 0.786, respectively. CONCLUSIONS: Noninvasive optimized 1H-MRS may be useful in predicting IDH mutational status and 2HG may serve as a valuable diagnostic and prognostic biomarker in patients with gliomas.

Assessment of treatment response to dendritic cell vaccine in patients with glioblastoma using a multiparametric MRI-based prediction model.

PURPOSE: Autologous tumor lysate-loaded dendritic cell vaccine (DCVax-L) is a promising treatment modality for glioblastomas. The purpose of this study was to investigate the potential utility of multiparametric MRI-based prediction model in evaluating treatment response in glioblastoma patients treated with DCVax-L. METHODS: Seventeen glioblastoma patients treated with standard-of-care therapy + DCVax-L were included. When tumor progression (TP) was suspected and repeat surgery was being contemplated, we sought to ascertain the number of cases correctly classified as TP + mixed response or pseudoprogression (PsP) from multiparametric MRI-based prediction model using histopathology/mRANO criteria as ground truth. Multiparametric MRI model consisted of predictive probabilities (PP) of tumor progression computed from diffusion and perfusion MRI-derived parameters. A comparison of overall survival (OS) was performed between patients treated with standard-of-care therapy + DCVax-L and standard-of-care therapy alone (external controls). Additionally, Kaplan-Meier analyses were performed to compare OS between two groups of patients using PsP, Ki-67, and MGMT promoter methylation status as stratification variables. RESULTS: Multiparametric MRI model correctly predicted TP + mixed response in 72.7% of cases (8/11) and PsP in 83.3% (5/6) with an overall concordance rate of 76.5% with final diagnosis as determined by histopathology/mRANO criteria. There was a significant concordant correlation coefficient between PP values and histopathology/mRANO criteria (r = 0.54; p = 0.026). DCVax-L-treated patients had significantly prolonged OS than those treated with standard-of-care therapy (22.38 ± 12.8 vs. 13.8 ± 9.5 months, p = 0.040). Additionally, glioblastomas with PsP, MGMT promoter methylation status, and Ki-67 values below median had longer OS than their counterparts. CONCLUSION: Multiparametric MRI-based prediction model can assess treatment response to DCVax-L in patients with glioblastoma.

Taming Glioblastoma in "Real Time": Integrating Multimodal Advanced Neuroimaging/AI Tools Towards Creating a Robust and Therapy Agnostic Model for Response Assessment in Neuro-Oncology.

The highly aggressive nature of glioblastoma carries a dismal prognosis despite aggressive multimodal therapy. Alternative treatment regimens, such as immunotherapies, are known to intensify the inflammatory response in the treatment field. Follow-up imaging in these scenarios often mimics disease progression on conventional MRI, making accurate evaluation extremely challenging. To this end, revised criteria for assessment of treatment response in high-grade gliomas were successfully proposed by the RANO Working Group to distinguish pseudoprogression from true progression, with intrinsic constraints related to the postcontrast T1-weighted MRI sequence. To address these existing limitations, our group proposes a more objective and quantifiable "treatment agnostic" model, integrating into the RANO criteria advanced multimodal neuroimaging techniques, such as diffusion tensor imaging (DTI), dynamic susceptibility contrast-perfusion weighted imaging (DSC-PWI), dynamic contrast enhanced (DCE)-MRI, MR spectroscopy, and amino acid-based positron emission tomography (PET) imaging tracers, along with artificial intelligence (AI) tools (radiomics, radiogenomics, and radiopathomics) and molecular information to address this complex issue of treatment-related changes versus tumor progression in "real-time", particularly in the early posttreatment window. Our perspective delineates the potential of incorporating multimodal neuroimaging techniques to improve consistency and automation for the assessment of early treatment response in neuro-oncology.

Validation of multiparametric MRI based prediction model in identification of pseudoprogression in glioblastomas.

BACKGROUND: Accurate differentiation of pseudoprogression (PsP) from tumor progression (TP) in glioblastomas (GBMs) is essential for appropriate clinical management and prognostication of these patients. In the present study, we sought to validate the findings of our previously developed multiparametric MRI model in a new cohort of GBM patients treated with standard therapy in identifying PsP cases. METHODS: Fifty-six GBM patients demonstrating enhancing lesions within 6 months after completion of concurrent chemo-radiotherapy (CCRT) underwent anatomical imaging, diffusion and perfusion MRI on a 3 T magnet. Subsequently, patients were classified as TP + mixed tumor (n = 37) and PsP (n = 19). When tumor specimens were available from repeat surgery, histopathologic findings were used to identify TP + mixed tumor (> 25% malignant features; n = 34) or PsP (< 25% malignant features; n = 16). In case of non-availability of tumor specimens, ≥ 2 consecutive conventional MRIs using mRANO criteria were used to determine TP + mixed tumor (n = 3) or PsP (n = 3). The multiparametric MRI-based prediction model consisted of predictive probabilities (PP) of tumor progression computed from diffusion and perfusion MRI derived parameters from contrast enhancing regions. In the next step, PP values were used to characterize each lesion as PsP or TP+ mixed tumor. The lesions were considered as PsP if the PP value was < 50% and TP+ mixed tumor if the PP value was ≥ 50%. Pearson test was used to determine the concordance correlation coefficient between PP values and histopathology/mRANO criteria. The area under ROC curve (AUC) was used as a quantitative measure for assessing the discriminatory accuracy of the prediction model in identifying PsP and TP+ mixed tumor. RESULTS: Multiparametric MRI model correctly predicted PsP in 95% (18/19) and TP+ mixed tumor in 57% of cases (21/37) with an overall concordance rate of 70% (39/56) with final diagnosis as determined by histopathology/mRANO criteria. There was a significant concordant correlation coefficient between PP values and histopathology/mRANO criteria (r = 0.56; p < 0.001). The ROC analyses revealed an accuracy of 75.7% in distinguishing PsP from TP+ mixed tumor. Leave-one-out cross-validation test revealed that 73.2% of cases were correctly classified as PsP and TP + mixed tumor. CONCLUSIONS: Our multiparametric MRI based prediction model may be helpful in identifying PsP in GBM patients.

MRI-Based Radiomics Combined with Deep Learning for Distinguishing IDH-Mutant WHO Grade 4 Astrocytomas from IDH-Wild-Type Glioblastomas.

This study aimed to investigate the potential of quantitative radiomic data extracted from conventional MR images in discriminating IDH-mutant grade 4 astrocytomas from IDH-wild-type glioblastomas (GBMs). A cohort of 57 treatment-naïve patients with IDH-mutant grade 4 astrocytomas (n = 23) and IDH-wild-type GBMs (n = 34) underwent anatomical imaging on a 3T MR system with standard parameters. Post-contrast T1-weighted and T2-FLAIR images were co-registered. A semi-automatic segmentation approach was used to generate regions of interest (ROIs) from different tissue components of neoplasms. A total of 1050 radiomic features were extracted from each image. The data were split randomly into training and testing sets. A deep learning-based data augmentation method (CTGAN) was implemented to synthesize 200 datasets from the training sets. A total of 18 classifiers were used to distinguish two genotypes of grade 4 astrocytomas. From generated data using 80% training set, the best discriminatory power was obtained from core tumor regions overlaid on post-contrast T1 using the K-best feature selection algorithm and a Gaussian naïve Bayes classifier (AUC = 0.93, accuracy = 0.92, sensitivity = 1, specificity = 0.86, PR_AUC = 0.92). Similarly, high diagnostic performances were obtained from original and generated data using 50% and 30% training sets. Our findings suggest that conventional MR imaging-based radiomic features combined with machine/deep learning methods may be valuable in discriminating IDH-mutant grade 4 astrocytomas from IDH-wild-type GBMs.

Imaging of White Matter Injury Correlates with Plasma and Tissue Biomarkers in Pediatric Porcine Model of Traumatic Brain Injury.

Traumatic brain injury (TBI) causes significant white matter injury, which has been characterized by various rodent and human clinical studies. The exact time course of imaging changes in a pediatric brain after TBI and its relation to biomarkers of injury and cellular function, however, is unknown. To study the changes in major white matter structures using a valid model of TBI that is comparable to a human pediatric brain in terms of size and anatomical features, we utilized a four-week-old pediatric porcine model of injury with controlled cortical impact (CCI). Using diffusion tensor imaging differential tractography, we show progressive anisotropy changes at major white matter tracts such as the corona radiata and inferior fronto-occipital fasciculus between day 1 and day 30 after injury. Moreover, correlational tractography shows a large part of bilateral corona radiata having positive correlation with the markers of cellular respiration. In contrast, bilateral corona radiata has a negative correlation with the plasma biomarkers of injury such as neurofilament light or glial fibrillary acidic protein. These are expected correlational findings given that higher integrity of white matter would be expected to correlate with lower injury biomarkers. We then studied the magnetic resonance spectroscopy findings and report decrease in a N-acetylaspartate/creatinine (NAA/Cr) ratio at the pericontusional cortex, subcortical white matter, corona radiata, thalamus, genu, and splenium of corpus callosum at 30 days indicating injury. There was also an increase in choline/creatinine ratio in these regions indicating rapid membrane turnover. Given the need for a pediatric TBI model that is comparable to human pediatric TBI, these data support the use of a pediatric pig model with CCI in future investigations of therapeutic agents. This model will allow future TBI researchers to rapidly translate our pre-clinical study findings into clinical trials for pediatric TBI.

Clinical indications and acquisition protocol for the use of dynamic contrast-enhanced MRI in head and neck cancer squamous cell carcinoma: recommendations from an expert panel.

BACKGROUND: The clinical role of perfusion-weighted MRI (PWI) in head and neck squamous cell carcinoma (HNSCC) remains to be defined. The aim of this study was to provide evidence-based recommendations for the use of PWI sequence in HNSCC with regard to clinical indications and acquisition parameters. METHODS: Public databases were searched, and selected papers evaluated applying the Oxford criteria 2011. A questionnaire was prepared including statements on clinical indications of PWI as well as its acquisition technique and submitted to selected panelists who worked in anonymity using a modified Delphi approach. Each panelist was asked to rate each statement using a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree). Statements with scores equal or inferior to 5 assigned by at least two panelists were revised and re-submitted for the subsequent Delphi round to reach a final consensus. RESULTS: Two Delphi rounds were conducted. The final questionnaire consisted of 6 statements on clinical indications of PWI and 9 statements on the acquisition technique of PWI. Four of 19 (21%) statements obtained scores equal or inferior to 5 by two panelists, all dealing with clinical indications. The Delphi process was considered concluded as reasons entered by panelists for lower scores were mainly related to the lack of robust evidence, so that no further modifications were suggested. CONCLUSIONS: Evidence-based recommendations on the use of PWI have been provided by an independent panel of experts worldwide, encouraging a standardized use of PWI across university and research centers to produce more robust evidence.

Prognostication of overall survival in patients with brain metastases using diffusion tensor imaging and dynamic susceptibility contrast-enhanced MRI.

OBJECTIVES: To investigate the prognostic utility of DTI and DSC-PWI perfusion-derived parameters in brain metastases patients. METHODS: Retrospective analyses of DTI-derived parameters (MD, FA, CL, CP, and CS) and DSC-perfusion PWI-derived rCBVmax from 101 patients diagnosed with brain metastases prior to treatment were performed. Using semi-automated segmentation, DTI metrics and rCBVmax were quantified from enhancing areas of the dominant metastatic lesion. For each metric, patients were classified as short- and long-term survivors based on analysis of the best coefficient for each parameter and percentile to separate the groups. Kaplan-Meier analysis was used to compare mOS between these groups. Multivariate survival analysis was subsequently conducted. A correlative histopathologic analysis was performed in a subcohort (n = 10) with DTI metrics and rCBVmax on opposite ends of the spectrum. RESULTS: Significant differences in mOS were observed for MDmin (p < 0.05), FA (p < 0.01), CL (p < 0.05), and CP (p < 0.01) and trend toward significance for rCBVmax (p = 0.07) between the two risk groups, in the univariate analysis. On multivariate analysis, the best predictive survival model was comprised of MDmin (p = 0.05), rCBVmax (p < 0.05), RPA (p < 0.0001), and number of lesions (p = 0.07). On histopathology, metastatic tumors showed significant differences in the amount of stroma depending on the combination of DTI metrics and rCBVmax values. Patients with high stromal content demonstrated poorer mOS. CONCLUSION: Pretreatment DTI-derived parameters, notably MDmin and rCBVmax, are promising imaging markers for prognostication of OS in patients with brain metastases. Stromal cellularity may be a contributing factor to these differences. ADVANCES IN KNOWLEDGE: The correlation of DTI-derived metrics and perfusion MRI with patient outcomes has not been investigated in patients with treatment naïve brain metastasis. DTI and DSC-PWI can aid in therapeutic decision-making by providing additional clinical guidance.

Dynamic contrast-enhanced MRI and Doppler sonography in patients with squamous cell carcinoma of head and neck treated with induction chemotherapy.

In view of the inherent limitations associated with performing dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) in clinical settings, current study was designed to provide a proof of principle that Doppler sonography and DCE-MRI derived perfusion parameters yield similar hemodynamic information from metastatic lymph nodes in squamous cell carcinomas of head and neck (HNSCCs). Strong positive correlations between volume fraction of plasma space in tissues (Vp ) and blood volume (r = 0.72, p = 0.02) and between Vp and %area perfused (r = 0.65, p = 0.04) were observed. Additionally, a moderate positive correlation trending towards significance was obtained between volume transfer constant (Ktrans ) and %area perfused (r = 0.49, p = 0.09).

Cerebral hemodynamic and metabolic dysregulation in the postradiation brain.

Technological advances in the delivery of radiation and other novel cancer therapies have significantly improved the 5-year survival rates over the last few decades. Although recent developments have helped to better manage the acute effects of radiation, the late effects such as impairment in cognition continue to remain of concern. Accruing data in the literature have implicated derangements in hemodynamic parameters and metabolic activity of the irradiated normal brain as predictive of cognitive impairment. Multiparametric imaging modalities have allowed us to precisely quantify functional and metabolic information, enhancing the anatomic and morphologic data provided by conventional MRI sequences, thereby contributing as noninvasive imaging-based biomarkers of radiation-induced brain injury. In this review, we have elaborated on the mechanisms of radiation-induced brain injury and discussed several novel imaging modalities, including MR spectroscopy, MR perfusion imaging, functional MR, SPECT, and PET that provide pathophysiological and functional insights into the postradiation brain, and its correlation with radiation dose as well as clinical neurocognitive outcomes. Additionally, we explored some innovative imaging modalities, such as quantitative blood oxygenation level-dependent imaging, susceptibility-based oxygenation measurement, and T2-based oxygenation measurement, that hold promise in delineating the potential mechanisms underlying deleterious neurocognitive changes seen in the postradiation setting. We aim that this comprehensive review of a range of imaging modalities will help elucidate the hemodynamic and metabolic injury mechanisms underlying cognitive impairment in the irradiated normal brain in order to optimize treatment regimens and improve the quality of life for these patients.

In-phase simultaneous spectral editing of lactate and alanine with suppression of J-coupled lipids by the modified selective multiple quantum coherence sequences.

1H magnetic resonance spectroscopy (MRS) with the multiple quantum coherence (MQC) technique allows for the detection of lactate, an end product of glycolysis, in the environment of lipids. The method can also be used to detect alanine, a byproduct of glutaminolysis. An issue is that when both lactate and alanine are detected together by the MQC technique, a phase mismatch arises between lactate and alanine signals due to off-resonance rotations and the difference in double quantum coherence frequencies between the two molecules. Such phase mismatch can cause errors in spectral fitting and metabolite quantification. In this study, we designed two pulse sequences that eliminate such phase differences of lactate and alanine while suppressing lipid signals by modifications of the Selective Multiple Quantum Coherence (Sel-MQC) sequence, a well-known MQC technique. Using the product operator formalism and the off-resonance rotation matrices, the phase evolutions of lactate and alanine during the spectrally selective pulses and the free precession times of the sequence at the single quantum, double quantum and zero quantum coherence states of these molecules were calculated. The multiple quantum (MQ) evolution time t1 that can remove the phase difference of lactate and alanine at the echo was calculated and fine-tuned with experiments. The lactate and alanine signal intensities and the editing efficiencies from the two modified Sel-MQC sequences were theoretically predicted by using the product operator evolutions and compared with the experimental data. The J-coupled lipid signals were successfully suppressed by both sequences. One of the two developed sequences was applied to a human body with a phantom of lactate and alanine, which resulted in successful in-phase editing of lactate and alanine and suppression of the lipid signals from the body. The study sets an important foundation for the noninvasive detection of lactate and alanine from tumors of cancer patients.

Emerging MR Imaging and Spectroscopic Methods to Study Brain Tumor Metabolism.

Proton magnetic resonance spectroscopy (1H-MRS) provides a non-invasive biochemical profile of brain tumors. The conventional 1H-MRS methods present a few challenges mainly related to limited spatial coverage and low spatial and spectral resolutions. In the recent past, the advent and development of more sophisticated metabolic imaging and spectroscopic sequences have revolutionized the field of neuro-oncologic metabolomics. In this review article, we will briefly describe the scientific premises of three-dimensional echoplanar spectroscopic imaging (3D-EPSI), two-dimensional correlation spectroscopy (2D-COSY), and chemical exchange saturation technique (CEST) MRI techniques. Several published studies have shown how these emerging techniques can significantly impact the management of patients with glioma by determining histologic grades, molecular profiles, planning treatment strategies, and assessing the therapeutic responses. The purpose of this review article is to summarize the potential clinical applications of these techniques in studying brain tumor metabolism.

Metabolic and physiologic magnetic resonance imaging in distinguishing true progression from pseudoprogression in patients with glioblastoma.

Pseudoprogression (PsP) refers to treatment-related clinico-radiologic changes mimicking true progression (TP) that occurs in patients with glioblastoma (GBM), predominantly within the first 6 months after the completion of surgery and concurrent chemoradiation therapy (CCRT) with temozolomide. Accurate differentiation of TP from PsP is essential for making informed decisions on appropriate therapeutic intervention as well as for prognostication of these patients. Conventional neuroimaging findings are often equivocal in distinguishing between TP and PsP and present a considerable diagnostic dilemma to oncologists and radiologists. These challenges have emphasized the need for developing alternative imaging techniques that may aid in the accurate diagnosis of TP and PsP. In this review, we encapsulate the current state of knowledge in the clinical applications of commonly used metabolic and physiologic magnetic resonance (MR) imaging techniques such as diffusion and perfusion imaging and proton spectroscopy in distinguishing TP from PsP. We also showcase the potential of promising imaging techniques, such as amide proton transfer and amino acid-based positron emission tomography, in providing useful information about the treatment response. Additionally, we highlight the role of "radiomics", which is an emerging field of radiology that has the potential to change the way in which advanced MR techniques are utilized in assessing treatment response in GBM patients. Finally, we present our institutional experiences and discuss future perspectives on the role of multiparametric MR imaging in identifying PsP in GBM patients treated with "standard-of-care" CCRT as well as novel/targeted therapies.

Genetics of glutamate and its receptors in autism spectrum disorder.

Autism spectrum disorder (ASD) is a neurodevelopmental impairment characterized by deficits in social interaction skills, impaired communication, and repetitive and restricted behaviors that are thought to be due to altered neurotransmission processes. The amino acid glutamate is an essential excitatory neurotransmitter in the human brain that regulates cognitive functions such as learning and memory, which are usually impaired in ASD. Over the last several years, increasing evidence from genetics, neuroimaging, protein expression, and animal model studies supporting the notion of altered glutamate metabolism has heightened the interest in evaluating glutamatergic dysfunction in ASD. Numerous pharmacological, behavioral, and imaging studies have demonstrated the imbalance in excitatory and inhibitory neurotransmitters, thus revealing the involvement of the glutamatergic system in ASD pathology. Here, we review the effects of genetic alterations on glutamate and its receptors in ASD and the role of non-invasive imaging modalities in detecting these changes. We also highlight the potential therapeutic targets associated with impaired glutamatergic pathways.

Coherence pathway analysis of J-coupled lipids and lactate and effective suppression of lipids upon the selective multiple quantum coherence lactate editing sequence.

Objective.The selective multiple quantum coherence (Sel-MQC) sequence is a magnetic resonance spectroscopy (MRS) technique used to detect lactate and suppress co-resonant lipid signalsin vivo. The coherence pathways of J-coupled lipids upon the sequence, however, have not been studied, hindering a logical design of the sequence to fully attenuate lipid signals. The objective of this study is to elucidate the coherence pathways of J-coupled lipids upon the Sel-MQC sequence and find a strategy to effectively suppress lipid signals from these pathways while keeping the lactate signal.Approach.The product operator formalism was used to express the evolutions of the J-coupled spins of lipids and lactate. The transformations of the product operators by the spectrally selective pulses of the sequence were calculated by using the off-resonance rotation matrices. The coherence pathways and the conversion rates of the individual pathways were derived from them. Experiments were performed on phantoms and two human subjects at 3 T.Main results.The coherence pathways contributing to the various lipid resonance signals by the Sel-MQC sequence depending on the gradient ratios and RF pulse lengths were identified. Theoretical calculations of the signals from the determined coherence pathways and signal attenuations by gradients matched the experimental data very well. Lipid signals from fatty tissues of the subjects were successfully suppressed to the noise level by using the gradient ratio -0.8:-1:2 or 1:0.8:2. The new gradient ratios kept the lactate signal the same as with the previously used gradient ratio 0:-1:2.Significance.The study has elucidated the coherence pathways of J-coupled lipids upon the Sel-MQC sequence and demonstrated how lipid signals can be effectively suppressed while keeping lactate signals by using information from the coherence pathway analysis. The findings enable applying the Sel-MQC sequence to lactate detection in an environment of high concentrations of lipids.

Advanced magnetic resonance imaging and spectroscopy in a case of neurocysticercosis from North America.

Neurocysticercosis (NCC) is a parasitic infection caused by Cysticercus cellulosae, the metacestode of pork tapeworm (Taenia solium). NCC is one of the most common public health problems worldwide. We present a patient harboring a bilobed ring-enhancing lesion with a presumed diagnosis of brain metastasis, who returned to the USA after traveling to an endemic region. The diagnosis of NCC was established based on a characteristic resonance of succinate on proton magnetic resonance spectroscopy. Also, higher mean diffusivity and lower fractional anisotropy along with relative cerebral blood volume were observed from the lesion compared to contralateral normal brain regions. Multiparametric analysis may improve the differential diagnosis of ring-enhancing intracranial lesions such as NCC.

Applications of Radiomics and Radiogenomics in High-Grade Gliomas in the Era of Precision Medicine.

Machine learning (ML) integrated with medical imaging has introduced new perspectives in precision diagnostics of high-grade gliomas, through radiomics and radiogenomics. This has raised hopes for characterizing noninvasive and in vivo biomarkers for prediction of patient survival, tumor recurrence, and genomics and therefore encouraging treatments tailored to individualized needs. Characterization of tumor infiltration based on pre-operative multi-parametric magnetic resonance imaging (MP-MRI) scans may allow prediction of the loci of future tumor recurrence and thereby aid in planning the course of treatment for the patients, such as optimizing the extent of resection and the dose and target area of radiation. Imaging signatures of tumor genomics can help in identifying the patients who benefit from certain targeted therapies. Specifying molecular properties of gliomas and prediction of their changes over time and with treatment would allow optimization of treatment. In this article, we provide neuro-oncology, neuropathology, and computational perspectives on the promise of radiomics and radiogenomics for allowing personalized treatments of patients with gliomas and discuss the challenges and limitations of these methods in multi-institutional clinical trials and suggestions to mitigate the issues and the future directions.

Multiparametric MRI assessment of response to convection-enhanced intratumoral delivery of MDNA55, an interleukin-4 receptor targeted immunotherapy, for recurrent glioblastoma.

BACKGROUND: Glioblastoma (GBM) is the most common malignant brain tumor and carries a dismal prognosis. Attempts to develop biologically targeted therapies are challenging as the blood-brain barrier can limit drugs from reaching their target when administered through conventional (intravenous or oral) routes. Furthermore, systemic toxicity of drugs often limits their therapeutic potential. To circumvent these problems, convection-enhanced delivery (CED) provides direct, targeted, intralesional therapy with a secondary objective to alter the tumor microenvironment from an immunologically "cold" (nonresponsive) to an "inflamed" (immunoresponsive) tumor. CASE DESCRIPTION: We report a patient with right occipital recurrent GBM harboring poor prognostic genotypes who was treated with MRI-guided CED of a fusion protein MDNA55 (a targeted toxin directed toward the interleukin-4 receptor). The patient underwent serial anatomical, diffusion, and perfusion MRI scans before initiation of targeted therapy and at 1, 3-month posttherapy. Increased mean diffusivity along with decreased fractional anisotropy and maximum relative cerebral blood volume was noted at follow-up periods relative to baseline. CONCLUSION: Our findings suggest that diffusion and perfusion MRI techniques may be useful in evaluating early response to CED of MDNA55 in recurrent GBM patients.