Early onset absence epilepsy of childhood: Epidemiologic data, treatment and outcome in a sample of 56 patients born between 2000 and 2018.

PURPOSE: The aim of our work is to describe the characteristics of Early Onset Absence Epilepsy (EOAE) and to observe whether specific anamnestic, clinical or electroencephalographic characteristics can influence the drug sensitivity of this pathology. METHODS: We carried out a retrospective study of patients affected by absence epilepsy with onset under four years of age, born between January 1st 2000 and December 31st 2018, who were reffered to the Regional Epilepsy Center of Spedali Civili of Brescia. We then divided the sample into three groups based on the age of onset. RESULTS: Our sample is composed of 56 patients. Among the children with epilepsy onset under two years of age (11), all were still on therapy after three and six years of follow-up, and 64 % of them required polytherapy. Among patients with epilepsy onset between two and three years of age (24), 87 % were still on therapy after three years of follow-up and 68 % after six years of follow-up; 46 % of these subjects required polytherapy. Among patients with epilepsy onset between three and four years of age (21), 89 % were still on therapy after three years of follow-up and 38 % after six years of follow-up; 38 % of them required polytherapy. CONCLUSIONS: We observe that patients with an earlier epilepsy onset have a worse outcome and a lower drug sensitivity. This may allow to predict in which cases it would be appropriate to maintain antiseizure therapy for a prolonged period.

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

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

Ethical Considerations and Fairness in the Use of Artificial Intelligence for Neuroradiology.

In this review, concepts of algorithmic bias and fairness are defined qualitatively and mathematically. Illustrative examples are given of what can go wrong when unintended bias or unfairness in algorithmic development occurs. The importance of explainability, accountability, and transparency with respect to artificial intelligence algorithm development and clinical deployment is discussed. These are grounded in the concept of "primum no nocere" (first, do no harm). Steps to mitigate unfairness and bias in task definition, data collection, model definition, training, testing, deployment, and feedback are provided. Discussions on the implementation of fairness criteria that maximize benefit and minimize unfairness and harm to neuroradiology patients will be provided, including suggestions for neuroradiologists to consider as artificial intelligence algorithms gain acceptance into neuroradiology practice and become incorporated into routine clinical workflow.

An objective, markerless videosystem for staging facial palsy.

PURPOSE: To propose a new objective, video recording method for the classification of unilateral peripheral facial palsy (UPFP) that relies on mathematical algorithms allowing the software to recognize numerical points on the two sides of the face surface that would be indicative of facial nerve impairment without positioning of markers on the face. METHODS: Patients with UPFP of different House-Brackmann (HB) degrees ranging from II to V were evaluated after video recording during two selected facial movements (forehead frowning and smiling) using a software trained to recognize the face points as numbers. Numerical parameters in millimeters were obtained as indicative values of the shifting of the face points, of the shift differences of the two face sides and the shifting ratio between the healthy (denominator) and the affected side (numerator), i.e., the asymmetry index for the two movements. RESULTS: For each HB grade, specific asymmetry index ranges were identified with a positive correlation for shift differences and negative correlation for asymmetry indexes. CONCLUSIONS: The use of the present objective system enabled the identification of numerical ranges of asymmetry between the healthy and the affected side that were consistent with the outcome from the subjective methods currently in use.

Impact of SARS-CoV-2 Pandemic on "Stroke Code" Imaging Utilization and Yield.

BACKGROUND AND PURPOSE: Indirect consequences of the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) pandemic include those related to failure of patients to seek or receive timely medical attention for seemingly unrelated disease. We report our experience with stroke code imaging during the early pandemic months of 2020. MATERIALS AND METHODS: Retrospective review of stroke codes during the 2020 pandemic and both 2020 and matched 2019 prepandemic months was performed. Patient variables were age, sex, hospital location, and severity of symptoms based on the NIHSS. We reviewed the results of CT of the head, CTA, CTP, and MR imaging examinations and classified a case as imaging-positive if any of the imaging studies yielded a result that related to the clinical indication for the study. Both year-to-year and sequential comparisons were performed between pandemic and prepandemic months. RESULTS: A statistically significant decrease was observed in monthly stroke code volumes accompanied by a statistically significant increased proportion of positive imaging findings during the pandemic compared with the same months in the prior year (P < .001) and prepandemic months in the same year (P < .001). We also observed statistically significant increases in average NIHSS scores (P = .045 and P = .03) and the proportion of inpatient stroke codes (P = .003 and P = .03). CONCLUSIONS: During our pandemic period, there was a significantly decreased number of stroke codes but simultaneous increases in positivity rates, symptom severity, and inpatient codes. We postulate that this finding reflects the documented reluctance of patients to seek medical care during the pandemic, with the shift toward a greater proportion of inpatient stroke codes potentially reflecting the neurologic complications of the virus itself.

Revision of active middle ear implants (AMEI): causes, surgical issues and rehabilitative transition at a single implanting center.

PURPOSE: To report on failures related to active middle ear implants (AMEI) surgery, within a series of subjects treated at a single Implanting Center. METHODS: A retrospective review of 79 cases of implanted AMEI has been performed to report the failure ratio, the causes for the failure and the selected rehabilitative solution. The AMEI included 25 Vibrant Soundbridge® (Medel, Innsbruck, Austria), 20 as round window vibroplasty (RW-VSB) for mixed hearing loss, 5 as incus-vibroplasty for sensorineural hearing loss; 7 MET/Carina® (Cochlear, Melbourne, Australia), 2 MET for mixed and sensorineural hearing loss, 5 Carina for sensorineural hearing loss; 43 Esteem® (EnvoyMedical, St Paul, USA) for sensorineural hearing loss; 3 Maxum® (Ototronics, Texas, USA) for sensorineural hearing loss; 1 Codacs® (Cochlear, Melbourne, Australia) for severe mixed hearing loss. RESULTS: The overall complication rate affected 20% of the implanted devices, individually ranging from 6.9 to 100%. Hardware system failures were recorded in all the AMEI, ranging from 10 to 50%. The alternative auditory rehabilitation included replacement of the same system in 2 cases, use of a conventional hearing aid in 3 cases, Cochlear implantation in 4 cases and implantation in the contralateral ear in 2 cases. CONCLUSION: The present clinical experience showed that, in spite of the successful functional rate displayed by the majority of the AMEI implantees, complications may occur to a certain percentage of cases and should prompt the professionals to select alternative solutions, starting from the (re)adoption of a conventional hearing aid and ending up to Cochlear implantation.

Artificial Intelligence and Acute Stroke Imaging.

Artificial intelligence technology is a rapidly expanding field with many applications in acute stroke imaging, including ischemic and hemorrhage subtypes. Early identification of acute stroke is critical for initiating prompt intervention to reduce morbidity and mortality. Artificial intelligence can help with various aspects of the stroke treatment paradigm, including infarct or hemorrhage detection, segmentation, classification, large vessel occlusion detection, Alberta Stroke Program Early CT Score grading, and prognostication. In particular, emerging artificial intelligence techniques such as convolutional neural networks show promise in performing these imaging-based tasks efficiently and accurately. The purpose of this review is twofold: first, to describe AI methods and available public and commercial platforms in stroke imaging, and second, to summarize the literature of current artificial intelligence-driven applications for acute stroke triage, surveillance, and prediction.

COVID-19 Severity and Stroke: Correlation of Imaging and Laboratory Markers.

BACKGROUND AND PURPOSE: Coronavirus disease 2019 (COVID-19) appears to be an independent risk factor for stroke. We hypothesize that patients who develop stroke while hospitalized for severe COVID-19 will have higher inflammatory markers and distinct stroke imaging patterns compared with patients positive for COVID-19 with out-of-hospital stroke onset and milder or no COVID-19 symptoms. MATERIALS AND METHODS: This is a retrospective case series of patients positive for COVID-19 on polymerase chain reaction testing with imaging-confirmed stroke treated within a large health care network in New York City and Long Island between March 14 and April 26, 2020. Clinical and laboratory data collected retrospectively included complete blood counts and creatinine, alanine aminotransferase, lactate dehydrogenase, C-reactive protein, ferritin, and D-dimer levels. All CT and MR imaging studies were independently reviewed by 2 neuroradiologists who recorded stroke subtype and patterns of infarction and intracranial hemorrhage. RESULTS: Compared with patients with COVID-19 with outside-of-hospital stroke onset and milder or no COVID-19 symptoms (n = 45, 52.3%), patients with stroke already hospitalized for severe COVID-19 (n = 41, 47.7%) had significantly more frequent infarctions (95.1% versus 73.3%, P = .006), with multivascular distributions (56.4% versus 33.3%, P = .022) and associated hemorrhage (31.7% versus 4.4%, P = .001). Patients with stroke admitted with more severe COVID-19 had significantly higher C-reactive protein and ferritin levels, elevated D-dimer levels, and more frequent lymphopenia and renal and hepatic injury (all, P < .003). CONCLUSIONS: Patients with stroke hospitalized with severe COVID-19 are characterized by higher inflammatory, coagulopathy, and tissue-damage biomarkers, supporting proposed pathogenic mechanisms of hyperinflammation activating a prothrombotic state. Cautious balancing of thrombosis and the risk of hemorrhagic transformation is warranted when considering anticoagulation.

Artificial Intelligence in Neuroradiology: Current Status and Future Directions.

Fueled by new techniques, computational tools, and broader availability of imaging data, artificial intelligence has the potential to transform the practice of neuroradiology. The recent exponential increase in publications related to artificial intelligence and the central focus on artificial intelligence at recent professional and scientific radiology meetings underscores the importance. There is growing momentum behind leveraging artificial intelligence techniques to improve workflow and diagnosis and treatment and to enhance the value of quantitative imaging techniques. This article explores the reasons why neuroradiologists should care about the investments in new artificial intelligence applications, highlights current activities and the roles neuroradiologists are playing, and renders a few predictions regarding the near future of artificial intelligence in neuroradiology.

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

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

Unveiling the genetic basis of Sclerotinia head rot resistance in sunflower.

BACKGROUND: Sclerotinia sclerotiorum is a necrotrophic fungus that causes Sclerotinia head rot (SHR) in sunflower, with epidemics leading to severe yield losses. In this work, we present an association mapping (AM) approach to investigate the genetic basis of natural resistance to SHR in cultivated sunflower, the fourth most widely grown oilseed crop in the world. RESULTS: Our association mapping population (AMP), which comprises 135 inbred breeding lines (ILs), was genotyped using 27 candidate genes, a panel of 9 Simple Sequence Repeat (SSR) markers previously associated with SHR resistance via bi-parental mapping, and a set of 384 SNPs located in genes with molecular functions related to stress responses. Moreover, given the complexity of the trait, we evaluated four disease descriptors (i.e, disease incidence, disease severity, area under the disease progress curve for disease incidence, and incubation period). As a result, this work constitutes the most exhaustive AM study of disease resistance in sunflower performed to date. Mixed linear models accounting for population structure and kinship relatedness were used for the statistical analysis of phenotype-genotype associations, allowing the identification of 13 markers associated with disease reduction. The number of favourable alleles was negatively correlated to disease incidence, disease severity and area under the disease progress curve for disease incidence, whereas it was positevily correlated to the incubation period. CONCLUSIONS: Four of the markers identified here as associated with SHR resistance (HA1848, HaCOI_1, G33 and G34) validate previous research, while other four novel markers (SNP117, SNP136, SNP44, SNP128) were consistently associated with SHR resistance, emerging as promising candidates for marker-assisted breeding. From the germplasm point of view, the five ILs carrying the largest combination of resistance alleles provide a valuable resource for sunflower breeding programs worldwide.

From the Eye of the Storm: Multi-Institutional Practical Perspectives on Neuroradiology from the COVID-19 Outbreak in New York City.

During the Severe Acute Respiratory Syndrome Coronavirus 2 (COVID-19) pandemic, neuroradiology practices have experienced a paradigm shift in practice, which affected everything from staffing, workflow, work volumes, conferences, resident and fellowship education, and research. This article highlights adaptive strategies that were undertaken at the epicenter of the outbreak in New York City during the past 4-6 weeks, as experienced by 5 large neuroradiology academic departments.

Measuring Glymphatic Flow in Man Using Quantitative Contrast-Enhanced MRI.

On the basis of animal models, glymphatic flow disruption is hypothesized to be a factor in the development of Alzheimer's disease. We report the first quantitative study of glymphatic flow in man, combining intrathecal administration of gadobutrol with serial T1 mapping to produce contrast concentration maps up to 3 days postinjection, demonstrating performing a quantitative study using the techniques described feasibility and providing data on pharmacokinetics.

Hybrid 3D/2D Convolutional Neural Network for Hemorrhage Evaluation on Head CT.

BACKGROUND AND PURPOSE: Convolutional neural networks are a powerful technology for image recognition. This study evaluates a convolutional neural network optimized for the detection and quantification of intraparenchymal, epidural/subdural, and subarachnoid hemorrhages on noncontrast CT. MATERIALS AND METHODS: This study was performed in 2 phases. First, a training cohort of all NCCTs acquired at a single institution between January 1, 2017, and July 31, 2017, was used to develop and cross-validate a custom hybrid 3D/2D mask ROI-based convolutional neural network architecture for hemorrhage evaluation. Second, the trained network was applied prospectively to all NCCTs ordered from the emergency department between February 1, 2018, and February 28, 2018, in an automated inference pipeline. Hemorrhage-detection accuracy, area under the curve, sensitivity, specificity, positive predictive value, and negative predictive value were assessed for full and balanced datasets and were further stratified by hemorrhage type and size. Quantification was assessed by the Dice score coefficient and the Pearson correlation. RESULTS: A 10,159-examination training cohort (512,598 images; 901/8.1% hemorrhages) and an 862-examination test cohort (23,668 images; 82/12% hemorrhages) were used in this study. Accuracy, area under the curve, sensitivity, specificity, positive predictive value, and negative-predictive value for hemorrhage detection were 0.975, 0.983, 0.971, 0.975, 0.793, and 0.997 on training cohort cross-validation and 0.970, 0.981, 0.951, 0.973, 0.829, and 0.993 for the prospective test set. Dice scores for intraparenchymal hemorrhage, epidural/subdural hemorrhage, and SAH were 0.931, 0.863, and 0.772, respectively. CONCLUSIONS: A customized deep learning tool is accurate in the detection and quantification of hemorrhage on NCCT. Demonstrated high performance on prospective NCCTs ordered from the emergency department suggests the clinical viability of the proposed deep learning tool.

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

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

Human hepatocyte transplantation for liver disease: current status and future perspectives.

Liver transplantation is the accepted treatment for patients with acute liver failure and liver-based metabolic disorders. However, donor organ shortage and lifelong need for immunosuppression are the main limitations to liver transplantation. In addition, loss of the native liver as a target organ for future gene therapy for metabolic disorders limits the futuristic treatment options, resulting in the need for alternative therapeutic strategies. A potential alternative to liver transplantation is allogeneic hepatocyte transplantation. Over the last two decades, hepatocyte transplantation has made the transition from bench to bedside. Standardized techniques have been established for isolation, culture, and cryopreservation of human hepatocytes. Clinical hepatocyte transplantation safety and short-term efficacy have been proven; however, some major hurdles-mainly concerning shortage of donor organs, low cell engraftment, and lack of a long-lasting effect-need to be overcome to widen its clinical applications. Current research is aimed at addressing these problems, with the ultimate goal of increasing hepatocyte transplantation efficacy in clinical applications.

Missed myocardial infarctions in ED patients prospectively categorized as low risk by established risk scores.

STUDY OBJECTIVES: Few studies have prospectively compared multiple cardiac risk prediction scores. We compared the rate of missed acute myocardial infarction (AMI) in chest pain patients prospectively categorized as low risk by unstructured clinical impression, and by HEART, TIMI, GRACE, and EDACS scores, in combination with two negative contemporary cardiac troponins (cTn) available in the U.S. METHODS: We enrolled 434 patients with chest pain presenting to one of seven emergency departments (ED). Risk scores were prospectively calculated and included the first two cTn. Low risk was defined for each score as HEART≤3, TIMI≤0, GRACE≤50, and EDACS≤15. AMI incidence was calculated for low risk patients and compared across scores using Χ2 tests and C statistics. RESULTS: The patients' median age was 57, 58% were male, 60% white, and 80 (18%) had AMI. The missed AMI rate in low risk patients for each of the scores when combined with 2 cTn were HEART 3.6%, TIMI 0%, GRACE 6.3%, EDACS 0.9%, and unstructured clinical impression 0%. The C-statistic was greatest for the EDACS score, 0.94 (95% CI, 0.92-0.97). CONCLUSIONS: Using their recommended cutpoints and non high sensitivity cTn, TIMI and unstructured clinical impression were the only scores with no missed cases of AMI. Using lower cutpoints (GRACE≤48, TIMI=0, EDACS≤11, HEART≤2) missed no case of AMI, but classified less patients as low-risk.

Phenotyping Sunflower Genetic Resources for Sclerotinia Head Rot Response: Assessing Variability for Disease Resistance Breeding.

Sclerotinia head rot (SHR) is one of the most serious constraints to sunflower (Helianthus annuus L. var. macrocarpus) production worldwide. Here, we evaluated the response to SHR in a sunflower inbred panel from a large INTA germplasm collection, consisting of 137 inbred lines (ILs). Field trials were performed over five consecutive seasons using a twice-replicated randomized complete-block design. Disease incidence, disease severity, incubation period, and area under disease progress curve for disease incidence and severity were determined after controlled inoculation with the pathogen. Statistical analysis using mixed-effect models detected significant differences among ILs for all variables (P < 0.001). In addition, principal component analysis (PCA) and distance-based methods were used to classify the ILs according to their response to SHR, with ILs ALB2/5261 and 5383 emerging as the most resistant. Broad-sense heritability estimates ranged from 20.64% for disease severity to 10.58% for incubation period. The ample phenotypic variability of our collection, along with the moderate heritability estimates, highlight the importance of molecular breeding approaches to gain new insights into the genetic basis of sunflower resistance to SHR. The exhaustive phenotypic characterization presented here provides a reliable set of variables to comprehensively evaluate the disease and identifies two new sources of resistance to SHR.

Brain natriuretic peptide and insulin resistance in older adults.

AIMS: Higher levels of brain natriuretic peptide (BNP) have been associated with a decreased risk of diabetes in adults, but whether BNP is related to insulin resistance in older adults has not been established. METHODS: N-terminal of the pro hormone brain natriuretic peptide (NT-pro BNP) was measured among Cardiovascular Health Study participants at the 1989-1990, 1992-1993 and 1996-1997 examinations. We calculated measures of insulin resistance [homeostatic model assessment of insulin resistance (HOMA-IR), quantitative insulin sensitivity check index (QUICKI), Gutt index, Matsuda index] from fasting and 2-h concentrations of glucose and insulin among 3318 individuals with at least one measure of NT-proBNP and free of heart failure, coronary heart disease and chronic kidney disease, and not taking diabetes medication. We used generalized estimating equations to assess the cross-sectional association of NT-proBNP with measures of insulin resistance. Instrumental variable analysis with an allele score derived from nine genetic variants (single nucleotide polymorphisms) within or near the NPPA and NPPB loci was used to estimate an un-confounded association of NT-proBNP levels on insulin resistance. RESULTS: Lower NT-proBNP levels were associated with higher insulin resistance even after adjustment for BMI, waist circumference and other risk factors (P < 0.001 for all four indices). Although the genetic score was strongly related to measured NT-proBNP levels amongst European Americans (F statistic = 71.08), we observed no association of genetically determined NT-proBNP with insulin resistance (P = 0.38; P = 0.01 for comparison with the association of measured levels of NT-proBNP). CONCLUSIONS: In older adults, lower NT-proBNP is associated with higher insulin resistance, even after adjustment for traditional risk factors. Because related genetic variants were not associated with insulin resistance, the causal nature of this association will require future study.

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

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