Treatment Of Brain Tumor Patients Research Articles

Effectiveness of VGG19 in deep learning for brain tumor detection

Image processing in the diagnosis of disease is one of the jobs that is currently developing in the world of health. Diagnosis is carried out by utilizing the role of image processing to provide a level of accuracy in diagnosis results and provide efficiency to medical personnel. This research aims to develop a brain tumor object detection process using a deep learning (DL) approach to magnetic resonance images (MRI) images. This development was carried out to optimize the brain tumor diagnosis process by playing the role of the image extraction process. This research dataset was sourced from the M. Djamil Padang Provincial General Hospital with a total of 3370 MRI images. The results of this work report show that DL performance is capable of carrying out the detection process automatically with an accuracy level of 97,83%. The results of the development of the extraction process can work effectively in ensuring brain tumor objects are precise and accurate. Overall, this research can make a major contribution to maximizing the diagnosis process and assisting medical personnel in the early treatment of brain tumor patients.

Improved Image Segmentation using Adaptive Threshold Morphology on CT-Scan Images for Brain Tumor Detection

Diagnosing disease by playing the role of image processing is one form of current medical technology development. The results of image processing performance have been able to provide accurate diagnoses to be used as material for decision-making. This research aims to carry out the process of detecting brain tumor objects in Computed Tomography (CT-Scan) images by developing a segmentation technique using the Adaptive Threshold Morphology (ATM) algorithm. The performance of the ATM algorithm in the segmentation process involves the Extended Adaptive Global Treshold (eAGT) function to produce an optimal threshold value. This research method involves several stages of the process in detecting tumor objects. The preprocessing stage is carried out using the cropping and filtering process which is optimized using the eAGT function. The next stage is the morphological segmentation process involving erosion and dilation operations. The final stage of the segmentation process using the ATM algorithm is labeling objects that have been detected. The research dataset used 187 Computed Tomography-Scan images from 10 brain tumor patients. The results of this study show that the accuracy rate for detecting brain tumor objects in Computed Tomography-Scan images is 93.47%. These results can provide an automatic and effective detection process based on the optimal threshold value that has been generated. Overall, this research contributes to the development of segmentation algorithms in image processing and can be used as an alternative solution in the treatment of brain tumor patients.

Enhancing Brain Tumor Detection on MRI Images Using an Innovative VGG-19 Model-Based Approach

Early detection and diagnosis of brain tumors have a critical impact on the treatment of brain tumor patients. This is because initiating interventions early directly impacts the patient's chances of continuing their life. In the field of medical research, various methods are employed for the detection of brain tumors. Among these methods, magnetic resonance imaging (MRI) is the most popular due to its superior image quality. By leveraging technological advancements, the utilization of deep learning techniques in the identification of brain tumors ensures both high accuracy and simplification of the process. In a conducted study, a new model was developed by utilizing the VGG-19 architecture, a popular convolutional neural network model, to achieve high accuracy in brain tumor detection. In the study, precision, F1 score, accuracy, specificity, Matthews correlation coefficient, and recall metrics were used to evaluate the performance of the developed model. The deep learning model developed for brain tumor detection was trained and evaluated on an open-source dataset consisting of MRI images of gliomas, meningiomas, pituitary tumors, and healthy brains. The results obtained from the study demonstrate the promising potential of using the developed model in clinical applications for brain tumor detection. The high accuracy achieved by the developed model emphasizes its potential as an auxiliary resource for healthcare professionals in brain tumor detection. This research aims to evaluate the model as a valuable tool that can assist physicians in making informed treatment decisions regarding brain tumor diagnosis.

Automatic Brain Tumor Detection and Volume Estimation in Multimodal MRI Scans via a Symmetry Analysis

In this study, an automated medical decision support system is presented to assist physicians with accurate and immediate brain tumor detection, segmentation, and volume estimation from MRI which is very important in the success of surgical operations and treatment of brain tumor patients. In the proposed approach, first, tumor regions on MR images are labeled by an expert radiologist. Then, an automated medical decision support system is developed to extract brain tumor boundaries and to calculate their volumes by using multimodal MR images. One advantage of this study is that it provides an automated brain tumor detection and volume estimation algorithm that does not require user interactions by determining threshold values adaptively. Another advantage is that, because of the unsupervised approach, the proposed study realized tumor detection, segmentation, and volume estimation without using very large labeled training data. A brain tumor detection and segmentation algorithm is introduced that is based on the fact that the brain consists of two symmetrical hemispheres. Two main analyses, i.e., histogram and symmetry, were performed to automatically estimate tumor volume. The threshold values used for skull stripping were computed adaptively by examining the histogram distances between T1- and T1C-weighted brain MR images. Then, a symmetry analysis between the left and right brain lobes on FLAIR images was performed for whole tumor detection. The experiments were conducted on two brain MRI datasets, i.e., TCIA and BRATS. The experimental results were compared with the labeled expert results, which is known as the gold standard, to demonstrate the efficacy of the presented method. The performance evaluation results achieved accuracy values of 89.7% and 99.0%, and a Dice similarity coefficient value of 93.0% for whole tumor detection, active core detection, and volume estimation, respectively.

IMG-03. HARNESSING COMPUTATIONAL IMAGING, SCALABLE CLOUD-BASED WORKFLOWS, AND MULTI-MODAL DATA ANALYTICS WITH INTEROPERABLE SOFTWARE PLATFORMS IN PEDIATRIC NEURO-ONCOLOGY; THE ROAD AHEAD FOR CHILDREN’S BRAIN TUMOR NETWORK (CBTN)

Abstract Neuroimaging is acquired as part of the clinical standard of care for treatment of brain tumor patients. Capitalizing on this data and integration of imaging with other data types to perform predictive analytics is an increasing need. Here, we review the efforts at CBTN in the past couple of years to establish user-friendly workflows to support the entire imaging data lifecycle and to bridge access to multi-modal datasets with scalable analytics scale, to empower researchers to make breakthrough discoveries that will advance patient care. An automated deep learning-based brain extraction and tumor subregion segmentation model based on a multi-institutional dataset was developed that can reliably segment the MRI scans of children across a variety of brain tumor histologies. This model was subsequently integrated into an end-to-end imaging pipeline for collecting, managing, and analyzing clinically acquired radiology exams of the CBTN consortium with processing of 12,500 exams to date. A public website was also created to allow users to search and explore the dataset based on sequence labels, and clinical and imaging attributes. In addition, we established a scalable workflow and processes for de-identification, ingestion, quality control, and management of digital pathology slides on CBTN (over 8,000 slides to date). Finally, we established interoperability between imaging (Flywheel) and molecular (CAVATICA) platforms deployed in cloud ecosystems which allows streamlined, user-friendly workflows to ingest and harmonize files, perform cohort selection, prepare data using standard processing packages and cloud resources, and conduct analysis on extracted multi-modal feature sets. The described workflow lays the foundation for broad use of imaging studies and access to multi-modal datasets and analytics, with the goal of empowering researchers to make breakthroughs in patient care. The next step is to apply these tools in the context of clinical trials.

Diagnosis of CNS Lesions by CSF

CNS tumors include primary and metastatic neoplasms, which together account for about 1% of human body tumors. The fragile nature of brain and spinal cord parenchyma limits both diagnostic and therapeutic approaches. Fortunately, the recently developed liquid based biopsy (LBB) technique provide highly convenient, fast and less-invasive method to collect and test the potential biomarkers. Biomarkers derived from liquid biopsies can promptly reflect changes on the gene expression profile of tumors. Biomarker derived from tumor cells contain abundant genetic information, which may provide a strong basis for the diagnosis and the individualized treatment of brain tumor patients. CSF can be used as a resource of biomarkers, the sensitivity and specificity of CSF biomarkers of patients with brain tumor is typically higher than those detected in peripheral blood and other sources. This chapter reviews the current different biomarkers in CSF and their significance in brain tumor diagnosis and monitoring.

Cerebrospinal fluid-based boron delivery system may help increase the uptake boron for boron neutron capture therapy in veterinary medicine: A preliminary study with normal rat brain cells

Boron neutron capture therapy (BNCT) is a non-invasive type of radiation therapy developed for humans and translated to veterinary medicine. However, clinical trials on BNCT for patients with brain tumors are on-going. To improve the therapeutic efficacy of BNCT for brain tumors, we developed a boron delivery system that involves the cerebrospinal fluid (CSF), in contrast to the conventional method that involves intravenous (IV) administration. This study aimed to investigate the time-concentration profile of boron in the CSF as well as the uptake rate of boron by the brain cells after administering L-p‑boronophenylalanine (BPA) into the lateral ventricle of normal rats. Brain cell uptake rates were compared between the CSF-based and IV administration methods. The CSF-based and IV administration methods achieved comparable brain cell uptake levels; however, the former method involved lower BPA doses than the latter method. These findings suggest that the CSF method may reduce the economic and physical burdens associated with this treatment in brain tumor patients. Future studies should validate these findings in rat models of brain tumors.

3D AGSE-VNet: an automatic brain tumor MRI data segmentation framework

BackgroundGlioma is the most common brain malignant tumor, with a high morbidity rate and a mortality rate of more than three percent, which seriously endangers human health. The main method of acquiring brain tumors in the clinic is MRI. Segmentation of brain tumor regions from multi-modal MRI scan images is helpful for treatment inspection, post-diagnosis monitoring, and effect evaluation of patients. However, the common operation in clinical brain tumor segmentation is still manual segmentation, lead to its time-consuming and large performance difference between different operators, a consistent and accurate automatic segmentation method is urgently needed. With the continuous development of deep learning, researchers have designed many automatic segmentation algorithms; however, there are still some problems: (1) The research of segmentation algorithm mostly stays on the 2D plane, this will reduce the accuracy of 3D image feature extraction to a certain extent. (2) MRI images have gray-scale offset fields that make it difficult to divide the contours accurately.MethodsTo meet the above challenges, we propose an automatic brain tumor MRI data segmentation framework which is called AGSE-VNet. In our study, the Squeeze and Excite (SE) module is added to each encoder, the Attention Guide Filter (AG) module is added to each decoder, using the channel relationship to automatically enhance the useful information in the channel to suppress the useless information, and use the attention mechanism to guide the edge information and remove the influence of irrelevant information such as noise.ResultsWe used the BraTS2020 challenge online verification tool to evaluate our approach. The focus of verification is that the Dice scores of the whole tumor, tumor core and enhanced tumor are 0.68, 0.85 and 0.70, respectively.ConclusionAlthough MRI images have different intensities, AGSE-VNet is not affected by the size of the tumor, and can more accurately extract the features of the three regions, it has achieved impressive results and made outstanding contributions to the clinical diagnosis and treatment of brain tumor patients.

A PROSPECTIVE OBSERVATIONAL STUDY TO ASSESS WAITING TIMES TO FIRST TREATMENT IN PATIENTS DIAGNOSED WITH BRAIN TUMOR.

The aim was to investigate and determine waiting intervals from diagnosis to first treatment in brain tumor patients. A prospective observational study was performed at the Department of Neurology, Split University Hospital Center, Split, Croatia, from February 21, 2016 until April 10, 2017. The inclusion criterion was the diagnosis of brain tumor confirmed by standard neuroimaging methods. The diagnosis-to-treatment interval (DTI) was defined as the time interval between the date of confirmed radiological diagnosis and the initiation of definitive treatment. Out of 73 patients diagnosed with brain tumor, 3 of them died, 16 were discharged for symptomatic treatment, and the rest were cured by surgical and/or oncologic procedures. The median DTI for any kind of treatment was 15.5 days. The median DTI for radio-guided and resection surgery was 14 days, while the median DTI for the initial oncologic treatment was 42 days. The median DTI of 15.5 days for brain tumors is still, by a wide margin, beyond the preferable one-digit number. When compared to the available data from other countries, however, it seems that the health system in Croatia provides the same, if not a slightly higher, level of efficiency. The median DTI for primary oncologic treatment did, however, indicate a substantial delay.

Hope and Distress Are Not Associated With the Brain Tumor Stage.

ObjectiveHopelessness and depression are strongly associated with suicidality. Given that physical and psychological outcomes can be altered with hope, hope is a therapeutic goal of increasing importance in the treatment of brain tumor patients. Moreover, it is not yet understood which factors affect the perception of hope in brain tumor patients. In addition, it remains uncertain whether lower-grade brain tumor patients suffer less from psycho-oncological distress than higher-grade brain tumor patients.MethodsNeuro-oncological patients were examined perioperatively with the Distress Thermometer (DT) and the Herth Hope Index (HHI). In addition, psychological comorbidities (anxiety GAD-2, depression PHQ-2) and an assessment of general psycho-oncological distress were recorded.ResultsSixty-six brain tumor patients were included (median age 53 years, 35% higher-grade brain tumors, i.e., WHO grade III/IV). No differences between higher- and lower-grade brain tumor patients were observed for general psycho-oncological distress and hope. However, higher-grade brain tumor patients showed a significantly higher level of depression (p ≤ 0.001) and more negative expectations regarding therapeutic success (H = 4.873, p ≤ 0.050). The extent of depression correlated negatively with hope.ConclusionUnexpectedly, higher-grade brain tumor patients remained as hopeful as lower-grade brain tumor patients despite the devastating diagnosis, higher levels of depression, and a worse expectation of therapeutic success. Conversely, lower-grade brain tumor patients experience as much psycho-oncological distress as patients with a higher-grade brain tumor, underpinning the imperative need for comprehensive psycho-oncological screening. For all brain tumor patients, considering hope is important to avoid suicides resulting from hopelessness and depression.

Corticosteroid Therapy for Brain Tumor Patients with Adrenal Insufficiency

The use of corticosteroids in cases of brain tumors has become common to reduce brain edema. However, the use can cause adrenal insufficiency (AI) if used long-term and in large doses and with rapid withdrawal. In cases of pituitary macroadenoma that has undergone surgery, AI may also occur. AI also affects the treatment of brain tumor patients. Hence, AI is an important problem in brain tumors because almost all patients with brain tumors receive corticosteroids at some point in the course of their disease. The management is similar to another AI with focus of hydrocortisone treatment. The adjustment of hydrocortisone dosage in patients whom undergo brain surgery is similar with another major surgery, whether the adjustment for pituitary adenoma patients whom undergo excision is more complicated and careful due to the high risk and incidence of AI in these patients.

A Simplified Overview of the Current Non-Surgical Treatment of Brain Tumor Patients with Special Emphasis on Its Complications. A Much Needed Contemporary Review Especially for Neurosurgeons

Brain tumors are complex entities with facts as numerous as stars in our sky, uncountable. They exist and thus every treating physician and surgeon happens to come across it. We try to put major contemporary practices and facts in a single bucket from learned skills of neurology and oncology tradition and things we received as feedback from our patients in clinical settings. Purpose of this work is to refresh your knowledge and preparedness for seeing patients with brain cancer. Brain tumors are molecularly heterogeneous and their features depend upon size, site, and genetic make-up of tumor, aggressive growth, and adjacent structures. Complications and their management are very challenging. This article will comfortably sail you through various aspects of management of brain cancers.

The medical necessity of advanced molecular testing in the diagnosis and treatment of brain tumor patients.

Accurate pathologic diagnoses and molecularly informed treatment decisions for a wide variety of cancers depend on robust clinical molecular testing that uses genomic, epigenomic, and transcriptomic-based tools. Nowhere is this more essential than in the workup of brain tumors, as emphasized by the incorporation of molecular criteria into the 2016 World Health Organization classification of central nervous system tumors and the updated official guidelines of the National Comprehensive Cancer Network. Despite the medical necessity of molecular testing in brain tumors, access to and utilization of molecular diagnostics is still highly variable across institutions, and a lack of reimbursement for such testing remains a significant obstacle. The objectives of this review are (i) to identify barriers to adoption of molecular testing in brain tumors, (ii) to describe the current molecular tools recommended for the clinical evaluation of brain tumors, and (iii) to summarize how molecular data are interpreted to guide clinical care, so as to improve understanding and justification for their coverage in the routine workup of adult and pediatric brain tumor cases.

P16.03 Efficacy and tolerability of lacosamide in patients with glioma: final results of a mono-institutional prospective study

AbstractBackground:Lacosamide (LCM) has been suggested in few retrospective studies to improve seizure control as an add-on treatment in brain tumor patients. We present the final results of a prospective study on a cohort of patients with low grade (LGG) and high grade gliomas (HGG) and medically intractable epilepsy who received LCM.Patients and methods:Eligibility criteria were as follows: 1) biopsy-proven grade II or III or IV gliomas according to WHO 2007; 2) persisting seizures (seizure frequency > 1 per month) despite a treatment with 1 or more antiepileptic drugs (AEDs) for at least 3 months and adequate serum concentration; 3) stable steroid dose; 4) available information on tumor response on MRI according to RANO criteria following chemotherapy or radiotherapy; 5) age > 18 years. LCM was given at 50 mg daily for one week with an increase of 50 mg twice daily every week to a target dose of 200–400 mg/day. The endpoints were > 50% decrease of seizure frequency and seizure freedom at 6 and 9 months.Results:Since January 2012 71 patients were evaluable. There were 26 grade II gliomas, 20 grade III and 25 glioblastomas. Eleven patients (15.5%) had generalized seizures and 32 (95.7%) partial seizures. Forty-four patients (61.8%) were or on AED monotherapy while 27 (38.2%) on polytherapy. Reasons for introduction of LCM were lack of efficacy of previous AEDs associated with (46.5%) or without (53.5%) progressive disease on MRI or unacceptable side effects of traditional AEDs in 11 patients (15.5%). Median duration of follow up was 9 months (range 3–24 months).Among LGG at 6 months from start of LCM 11/26 patients (42.3%) had a reduction of >50% of seizure frequency while seizure freedom was obtained in 11/26 (42.3%); at 9 months a reduction of >50% of seizure frequency was observed in 9/24 (37.5%) while seizure freedom was obtained in other 13 (54.2%). Moreover, 11/26 patients (42.3%) at 6 months and 10/26 (38.5%) at 9 months were seizure responders respectively, without concurrent antineoplastic therapies. As for HGG at 6 months 10/39 patients (25.6%) had a reduction of >50% of seizure frequency while seizure freedom was obtained in 18/39 (46.1%); at 9 months a reduction of >50% of seizure frequency was observed in 7/32 (21.8%) and seizure freedom in other 17 (53.1%). A resolution of an epileptic status was obtained in 6 patients of whom one receiving LCM alone. Secondary generalized seizures disappeared in 14 patients. Two responding patients were able to reduce the doses of previous AEDs. Most patients (87.3%) did not report significant toxicities: in 2 cases only LCM was withdrawn due to dizziness and fatigue and resistant epilepsy, respectively.Conclusion:At our knowledge this is the first prospective study on patients with both LGG and HGG with LCM as an add-on treatment showing a significant activity, even regardless of tumor response to antineoplastic treatment. LCM did not have interactions with AEDs.

Lentiviral HSV-Tk.007-mediated suicide gene therapy is not toxic for normal brain cells.

Gene therapeutic strategies with suicide genes are currently investigated in clinical trials for brain tumors. Previously, we have shown that lentiviral vectors delivering the suicide gene HSV-Tk to experimental brain tumors promote a highly significant treatment effect and thus are promising vectors for clinical translation. In the present study, we tested lentiviral vectors delivering the suicide gene HSV-Tk.007, a highly active mutant of HSV-Tk, to rat brains as a preclinical toxicity study. We injected 10(6) vesicular stomatitis virus glycoprotein (VSV-G) pseudotyped functional lentiviral particles harboring the suicide gene HSV-Tk.007 into the brain of healthy, immunocompetent rats. During prodrug treatment with ganciclovir (GCV), we measured weight and assessed the behavior of the rats in an open field test. After 14 days of GCV treatment, we analyzed HSV-Tk.007 expression in different brain cell populations, as well as inflammatory responses and apoptosis. During prodrug treatment with GCV, behavior experiments did not reveal differences between the treated rats and the control groups. Analysis of HSV-Tk expression in different brain cell populations showed that transduced normal brain cells survived GCV treatment. There were no statistically significant differences in the number of transduced cells between treatment and control groups. Furthermore, inflammatory responses and apoptosis of brain cells were not observed. We show that HSV-Tk.007-mediated suicide gene therapy is not toxic to normal brain cells. This observation is of high relevance for the translation of lentivirus-mediated suicide gene therapies into the clinic for the treatment of brain tumor patients. Copyright © 2016 John Wiley & Sons, Ltd.

Development of an integrated practice unit: Utilizing a lean approach to impact value of care for brain tumor patients.

99 Background: Lack of care coordination across subspecialty departments involved in the treatment of brain tumor patients at our institution has negatively impacted patient outcomes, patient experience, and costs. Meanwhile, value-based health care has become increasingly relevant as a means to respond to changing payment structures and improve quality. With the aim to increase value, we restructured medical practice across the continuum of care for brain tumor patients by developing a virtual (non co-located) Integrated Practice Unit (IPU). Methods: From June 2014 to August 2015, we engaged a core team of physicians and administrators from Neurosurgery, Neuro-Oncology, Radiation Oncology, Neuroradiology and Neuropathology to re-design care pathways for the following diagnoses: glioma, metastatic cancer to the brain, and meningioma. We applied lean methodology to map out current state, identify root causes, and develop an implementation plan based on our analyses. Results: Root causes uncovered included: 1) multiple entry points into the system, 2) silo-ed intake processes, 3) varied scheduling processes across and within departments, and 4) no consensus regarding timing and ownership of follow-up care for patients for each diagnosis. Preliminary solutions generated included: 1) developing a centralized communication point and triage process, 2) standardizing requests at intake and obtaining blanket authorizations for select services, 3) standardizing scheduling workflows across departments, 4) delineating the timing and nature of necessary post-operative appointments, and 5) onboarding a nurse navigator to optimize care coordination. Shared metrics to be monitored over time were developed and include time from scheduled-to-seen for initial consults, proportion of patients with post-operative appointments scheduled prior to discharge, number of readmissions within 30 days, patient satisfaction, and costs. Conclusions: Value-based care redesign around the development of an IPU for brain tumor patients has the potential to meaningfully impact patient outcomes, patient experience, and reduce costs in the delivery of care.

Chapter 16 - Epilepsy and brain tumors

Seizures are common in patients with brain tumors, and epilepsy can significantly impact patient quality of life. Therefore, a thorough understanding of rates and predictors of seizures, and the likelihood of seizure freedom after resection, is critical in the treatment of brain tumors. Among all tumor types, seizures are most common with glioneuronal tumors (70-80%), particularly in patients with frontotemporal or insular lesions. Seizures are also common in individuals with glioma, with the highest rates of epilepsy (60-75%) observed in patients with low-grade gliomas located in superficial cortical or insular regions. Approximately 20-50% of patients with meningioma and 20-35% of those with brain metastases also suffer from seizures. After tumor resection, approximately 60-90% are rendered seizure-free, with most favorable seizure outcomes seen in individuals with glioneuronal tumors. Gross total resection, earlier surgical therapy, and a lack of generalized seizures are common predictors of a favorable seizure outcome. With regard to anticonvulsant medication selection, evidence-based guidelines for the treatment of focal epilepsy should be followed, and individual patient factors should also be considered, including patient age, sex, organ dysfunction, comorbidity, or cotherapy. As concomitant chemotherapy commonly forms an essential part of glioma treatment, enzyme-inducing anticonvulsants should be avoided when possible. Seizure freedom is the ultimate goal in the treatment of brain tumor patients with epilepsy, given the adverse effects of seizures on quality of life.

PCSM-08EFFICACY AND TOLERABILITY OF LACOSAMIDE IN PATIENTS WITH GLIOMA AND SEIZURES: A PROSPECTIVE STUDY

BACKGROUND: Lacosamide (LCM) has been suggested to improve seizure control as add-on treatment in brain tumor patients. We present the results of a prospective study on the efficacy and tolerability of LCM in a cohort of patients with low (LGG) and high grade (HGG) gliomas with active epilepsy, either on chemotherapy or radiotherapy or during observation alone with MRI. METHODS: We enrolled 62 adult patients with persisting seizures (seizures > 1 per month) despite an adequate treatment with other antiepileptic drugs. The endpoints of the study were a significant reduction of seizure frequency (> 50% compared with baseline) and seizure freedom (SF) at 3 and 6 months, being concomitant AEDs and steroids unchanged or reduced. RESULTS: Among grade II gliomas a significant reduction of seizures was observed in 5/22 patients (22.7.%) and 7/22 (36.8%) at 3 and 6 months after start of LCM, respectively; a SF was obtained in 6/22 (23.3%) and 8/22 (42.1%) at 3 and 6 months after start of LCM, respectively. Among HGG a significant reduction of seizures was obtained in 10/41 patients (24.4%) and 7/33 patients (21.1%) at 3 and 6 months after start of LCM, respectively; a SF was obtained in 19/40 (47.5%) and 15/40 (45.5%) at 3 and 6 months after the start of LCM, respectively. Four out of 11 and 7/15 patients with LGG who responded to LCM did not receive any antineoplastic treatment, whereas 18/40 patients with HGG had the tumor stable or responding on MRI (RANO criteria) following antineoplastic treatment. A resolution of a status epilepticus was obtained in 7 patients. In 2 cases only LCM was withdrawn due to dizziness and fatigue or resistant seizures. CONCLUSIONS: This is the first prospective study showing a significant activity of LCM as add-on therapy in gliomas with different grades of malignancy.

Predictors of preoperative and early postoperative seizures in patients with intra-axial primary and metastatic brain tumors: A retrospective observational single center study.

Antiepileptic treatment of brain tumor patients mainly depends on the individual physician's choice rather than on well-defined predictive factors. We investigated the predictive value of defined clinical parameters to formulate a model of risk estimations for subpopulations of brain tumor patients. We enclosed 650 patients > 18 years of age who underwent brain tumor surgery and included a number of clinical data. Logistic regressions were performed to determine the effect sizes of seizure-related risk factors and to develop prognostic scores for the occurrence of preoperative and early postoperative seizures. A total of 492 patients (334 gliomas) were eligible for logistic regression for preoperative seizures, and 338 patients for early postoperative seizures. Age ≤ 60 years (odds ratio [OR] = 1.66, p = 0.020), grades I and II glioma (OR = 4.00, p = 0.0002), total tumor/edema volume ≤ 64cm(3) (OR = 2.18, p = 0.0003), and frontal location (OR = 2.28, p = 0.034) demonstrated an increased risk for preoperative seizures. Isocitrate-dehydrogenase mutations (OR = 2.52, p = 0.026) were an independent risk factor in the glioma subgroup. Age ≥ 60 years (OR = 3.32, p = 0.041), total tumor/edema volume ≤ 64cm(3) (OR = 3.17, p = 0.034), complete resection (OR = 15.50, p = 0.0009), diencephalic location (OR = 12.2, p = 0.013), and high-grade tumors (OR = 5.67, p = 0.013) were significant risk factors for surgery-related seizures. Antiepileptics (OR = 1.20, p = 0.60) did not affect seizure occurrence. For seizure occurrence, patients could be stratified into 3 prognostic preoperative and into 2 prognostic early postoperative groups. Based on the developed prognostic scores, seizure prophylaxis should be considered in high-risk patients and patient stratification for prospective studies may be feasible in the future.

O9.09 * EFFICACY AND TOLERABILITY OF LACOSAMIDE IN PATIENTS WITH GLIOMA: A PROSPECTIVE STUDY

BACKGROUND: Lacosamide (LCM) has been suggested in some retrospective studies to improve seizure control as an add-on treatment in brain tumor patients. We present here the preliminary results of a prospective study focused on a cohort of patients with gliomas and active epilepsy who received LCM. METHODS: Eligibility criteria were as follows: 1) biopsy-proven grade II or III or IV gliomas according to WHO 2007; 2) persisting seizures (seizure frequency > 1 per month) despite a treatment with 1 or more antiepileptic drugs (AEDs) for at least 3 months and adequate serum concentration; 3) stable steroid dose; 4) available information on tumor response on MRI according to RANO criteria following chemotherapy or radiotherapy; 5) age > 18 years. LCM was given at 50 mg daily for one week with an increase of 50 mg twice daily every week to a target dose of 200-400 mg/day. The endpoints were > 50% decrease of seizure frequency and seizure freedom at 3 and 6 months. RESULTS: Since January 2012 42 patients were enrolled. There were 17 grade II gliomas (5 astrocytomas, 8 oligodendrogliomas, 4 oligoastrocytomas), 13 grade III (4 astrocytomas, 3 oligodendrogliomas, 6 gliomatosis cerebri) and 12 glioblastomas. Ten patients (23.8%) had generalized seizures and 32 (76.2%) partial seizures. Thirty-six patients (85.7%) were on AED monotherapy while 6 (14.3%) on polytherapy. Reasons for introduction of LCM were unacceptable side effects of traditional AEDs in 5 patients (12%) and/or lack of efficacy of previous AEDs associated with either PD in 16 (38%) or without PD in 26 (62%). Median duration of follow up was 6.6 months (range 3-24 months). Among low grade gliomas after 3 months of LCM 5/17 patients (29.4%) had a reduction of >50% of seizure frequency while seizure freedom was obtained in other 3/17 (17.6%); at 6 months a reduction of >50% of seizure frequency was observed in 5 (29.4%) while seizure freedom was obtained in other 2 (11.8%) for overall seizure control of 41.2%. As for high grade gliomas after 3 months 6/25 patients (24%) had a reduction of >50% of seizure frequency while seizure freedom was obtained in other 11/25 (44%); at 6 months a reduction of >50% of seizure frequency was observed in 7/22 (31.8%) and seizure freedom in other 6 (27.3%) for overall seizure control of 59.1% . A resolution of an epileptic status was obtained in 3 patients of whom one receveing LCM alone. Secondary generalized seizures disappeared in 4 patients. However, responding patients were not able to reduce the doses of previous AEDs. Most patients (97.6%) did not report significant toxicities: in 2 cases only LCM was withdrawn due to dizziness and fatigue and resistant epilepsy, respectively. CONCLUSION: Among patients both with high grade and low grade gliomas LCM is well tolerated without interactions with antitumoral treatments. LCM shows significant activity as add-on therapy regardless of tumor response to antineoplastic treatment.