Meningioma Tumor Research Articles

Abstract 781: Methylation-based liquid biopsy of meningioma primary and recurrent samples

Abstract Meningiomas are mostly benign CNS tumors however, a subset of these tumors may become atypical or malignant. The standard of care to monitor patients after diagnosis requires serial MRI assessments, which have limited value in distinguishing malignant tumors from benign disease. Therefore the discovery of non-invasive methodologies that reflect meningioma tumor burden and its dynamic evolution in real-time is highly desirable. Liquid biopsy (LB) could be used to fine-tune surveillance and treatment with minimal risk to patients. Evidence of circulating tumor cells and cell-free (cf) tumor DNA in the blood has been shown in several tumor types however, limited progress has been made for brain tumors with known biomarkers; possibly due to the unlikelihood of capturing point mutations in circulating DNA fragments. On the other hand, DNA methylation signatures are maintained even in small DNA fragments, which suggests that DNA methylation is an attractive biomarker to be studied in liquid biopsy of brain cancers. In order to identify DNA methylation-based biomarkers using archival serum and tissue specimens, we generated and analyzed the epigenome (Illumina Human EPIC array) of patients with meningioma (including primary (n=6); recurrent (n=8)) and non-meningioma (including non-tumor patients (n=5), pituitary tumor (n=13), colorectal cancer (n=2) and other CNS diseases (n=6), such as inflammatory tissue and radiation necrosis). cfDNA fragment size distribution revealed peaks with 150~200bp on average. By randomly selecting 70% of our cohort as a discovery set, we identified 500 CpGs (FDR<0.05) differentially methylated between meningiomas and non-meningiomas, which show a DNA methylation profile similar to the matched meningioma tissue. Then, we trained a random-forest machine learning using the same signature and applied the model to the remaining 30% samples (validation set) from our cohort. The model was able to correctly classify samples into meningioma and non-meningiomas with a sensitivity of 100% and specificity of 100%. From this pilot data, we were able to investigate the LB methylome of meningioma patients and identify potential markers to detect tumor cells in the serum of these patients, which could eventually allow clinicians to monitor impending disease progression and recurrence. Citation Format: Thais S. Sabedot, Tathiane M. Malta, Ruicong She, James Snyder, Tobias Walbert, Ian Lee, Steven Kalkanis, James Ewing, AnaValeria Castro, Houtan Noushmehr. Methylation-based liquid biopsy of meningioma primary and recurrent samples [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 781.

Automatic detection of the meningioma tumor firmness in MRI images.

Meningioma is among the most common primary tumors of the brain. The firmness of Meningioma is a critical factor that influences operative strategy and patient counseling. Conventional methods to predict the tumor firmness rely on the correlation between the consistency of Meningioma and their preoperative MRI findings such as the signal intensity ratio between the tumor and the normal grey matter of the brain. Machine learning techniques have not been investigated yet to address the Meningioma firmness detection problem. The main purpose of this research is to couple supervised learning algorithms with typical descriptors for developing a computer-aided detection (CAD) of the Meningioma tumor firmness in MRI images. Specifically, Local Binary Patterns (LBP), Gray Level Co-occurrence Matrix (GLCM) and Discrete Wavelet Transform (DWT) are extracted from real labeled MRI-T2 weighted images and fed into classifiers, namely support vector machine (SVM) and k-nearest neighbor (KNN) algorithm to learn association between the visual properties of the region of interest and the pre-defined firm and soft classes. The learned model is then used to classify unlabeled MRI-T2 weighted images. This paper represents a baseline comparison of different features used in CAD system that intends to accurately recognize the Meningioma tumor firmness. The proposed system was implemented and assessed using a clinical dataset. Using LBP feature yielded the best performance with 95% of F-score, 87% of balanced accuracy and 0.87 of the area under ROC curve (AUC) when coupled with KNN classifier, respectively.

Characterization and comparison of genomic profiles between primary cancer cell lines and parent atypical meningioma tumors

BackgroundMeningiomas are the second most common primary tumors of the central nervous system. However, there is a paucity of data on meningioma biology due to the lack of suitable preclinical in vitro and in vivo models. In this study, we report the establishment and characterization of patient-derived, spontaneously immortalized cancer cell lines derived from World Health Organization (WHO) grade I and atypical WHO grade II meningiomas.MethodsWe evaluated high-resolution 3T MRI neuroimaging findings in meningioma patients which were followed by histological analysis. RT-qPCR and immunostaining analyses were performed to determine the expression levels of meningioma-related factors. Additionally, flow cytometry and sorting assays were conducted to investigate and isolate the CD133 and CD44 positive cells from primary atypical meningioma cells. Further, we compared the gene expression profiles of meningiomas and cell lines derived from them by performing whole-exome sequencing of the blood and tumor samples from the patients, and the primary cancer cell lines established from the meningioma tumor.ResultsOur results were consistent with earlier studies that reported mutations in NF2, SMO, and AKT1 genes in atypical meningiomas, and we also observed mutations in MYBL2, a gene that was recently discovered. Significantly, the genomic signature was consistent between the atypical meningioma cancer cell lines and the tumor and blood samples from the patient.ConclusionOur results lead us to conclude that established meningioma cell lines with a genomic signature identical to tumors might be a valuable tool for understanding meningioma tumor biology, and for screening therapeutic agents to treat recurrent meningiomas.

A fuzzy logic‐based meningioma tumor detection in magnetic resonance brain images using CANFIS and U‐Net CNN classification

AbstractThis article develops a methodology for meningioma brain tumor detection process using fuzzy logic based enhancement and co‐active adaptive neuro fuzzy inference system and U‐Net convolutional neural network classification methods. The proposed meningioma tumor detection process consists of the following stages as, enhancement, feature extraction, and classifications. The enhancement of the source brain image is done using fuzzy logic and then dual tree‐complex wavelet transform is applied to this enhanced image at different levels of scale. The features are computed from the decomposed sub band images and these features are further classified using CANFIS classification method which identifies the meningioma brain image from nonmeningioma brain image. The performance of the proposed meningioma brain tumor detection and segmentation system is analyzed in terms of sensitivity, specificity, segmentation accuracy, and Dice coefficient index with detection rate.

Effective and Efficient LDA+ELM Model for Supervised Classification of Brain Tumor Types Using 2D MRI Scans

Application of machine learning in multiclass classification of brain tumor types has contributed to the development of computer aided diagnosis (CAD) system that can potentially enhance accuracy and speed up diagnosis of the disease. LDA+ELM model with different activation functions were investigated to achieve the optimum performances in terms of accuracy, Kappa statistic, sensitivity, precision, F-measure, training time and test time. We also proposed a user-friendly GUI in characterizing brain tumor types using MR images. First, a total of 3064 slices of CE T1-weighted brain MR images with ground truth were downloaded from a free online database. The manually segmented tumor region was augmented and then undergo several feature extraction techniques. All the feature descriptors obtained were then concatenated, followed by LDA dimensionality approach. Performance of different number of LDA features and ELM activation functions were investigated by repeated training and test. The ELM output of training data for each class was used to fit GMM and these probabilistic models used to estimate posterior probabilities of test data. LDA+ELM model with 5 LDA feature input, utilizing sigmoid function as hidden nodes activation functions achieves the best generalization performance with accuracy of 98.92% and corresponding F-scores for meningioma, glioma and pituitary tumor of 97.81%, 99.1% and 99.5% respectively. The proposed method (LDA+ELM) model performs better compared to other previous works using the same dataset and performing the same classification task.

The Potential of MLN3651 in Combination with Selumetinib as a Treatment for Merlin-Deficient Meningioma.

Meningioma is the most common primary intracranial tumour, and surgical resection is the main therapeutic option. Merlin is a tumour suppressor protein that is frequently mutated in meningioma. The activity of the E3 ubiquitin ligase complex, CRL4-DCAF1, and the Raf/MEK/ERK scaffold protein Kinase suppressor of Ras 1 (KSR1) are upregulated in Merlin-deficient tumours, which drives tumour growth. Identifying small molecules that inhibit these key pathways may provide an effective treatment option for patients with meningioma. We used meningioma tissue and primary cells derived from meningioma tumours to investigate the expression of DDB1 and Cullin 4-associated factor 1 (DCAF1) and KSR1, and confirmed these proteins were overexpressed. We then used primary cells to assess the therapeutic potential of MLN3651, a neddylation inhibitor which impacts the activity of the CRL family of E3 ubiquitin ligases and the MAPK/ERK kinase (MEK1/2) inhibitor selumetinib. MLN3651 treatment reduced proliferation and activated apoptosis, whilst increasing Raf/MEK/ERK pathway activation. The combination of MLN3651 and the MEK1/2 inhibitor selumetinib prevented the increase in Raf/MEK/ERK activity, and had an additive effect compared with either treatment alone. Therefore, the combined targeting of CRL4-DCAF1 and Raf/MEK/ERK activity represents an attractive novel strategy in the treatment of Merlin-deficient meningioma.

Rare Primary Central Nervous System Tumors in Adults: An Overview.

Overall, tumors of primary central nervous system (CNS) are quite common in adults with an incidence rate close to 30 new cases/100,000 inhabitants per year. Significant clinical and biological advances have been accomplished in the most common adult primary CNS tumors (i.e., diffuse gliomas). However, most CNS tumor subtypes are rare with an incidence rate below the threshold defining rare disease of 6.0 new cases/100,000 inhabitants per year. Close to 150 entities of primary CNS tumors have now been identified by the novel integrated histomolecular classification published by the World Health Organization (WHO) and its updates by the c-IMPACT NOW consortium (the Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy). While these entities can be better classified into smaller groups either by their histomolecular features and/or by their location, assessing their treatment by clinical trials and improving the survival of patients remain challenging. Despite these tumors are rare, research, and advances remain slower compared to diffuse gliomas for instance. In some cases (i.e., ependymoma, medulloblastoma) the understanding is high because single or few driver mutations have been defined. The European Union has launched European Reference Networks (ERNs) dedicated to support advances on the clinical side of rare diseases including rare cancers. The ERN for rare solid adult tumors is termed EURACAN. Within EURACAN, Domain 10 brings together the European patient advocacy groups (ePAGs) and physicians dedicated to improving outcomes in rare primary CNS tumors and also aims at supporting research, care and teaching in the field. In this review, we discuss the relevant biological and clinical characteristics, clinical management of patients, and research directions for the following types of rare primary CNS tumors: medulloblastoma, pineal region tumors, glioneuronal and rare glial tumors, ependymal tumors, grade III meningioma and mesenchymal tumors, primary central nervous system lymphoma, germ cell tumors, spinal cord tumors and rare pituitary tumors.

The effect of household income on outcomes following supratentorial meningioma resection

ObjectiveThis study assesses the impact of Median Household Income (MHI) on short- and long-term morbidity and mortality following supratentorial meningioma resection. Patients and methods351 consecutive patients undergoing supratentorial meningioma tumor resection, at a single health system over a six-year period (June 09, 2013 to April 26, 2019) were analyzed retrospectively. Outcomes assessed included readmission, emergency department (ED) evaluation, and mortality within 90 days of surgery. Univariate regression analysis was conducted amongst the entire population. The population was then divided into quartiles based on median household income and univariate analysis was conducted between the lowest (Q1) and highest (Q4) quartiles. Significance was set at a P-value < 0.05. Stepwise regression was performed to identify confounding variables in the logistic model. ResultsIn the whole population, lower Median Household Income correlated to a significant increase in ED evaluation within 90-days of supratentorial meningioma resection. No significant difference was noted between median household income and 90-day readmission, 90-day unplanned re-operation, return to surgery after index admission within 90-days, return to surgery during the duration of the follow-up period, mortality within 90-days, and mortality during the duration of the follow-up period. In addition, when comparing Q1 (MHI ≤ $51,780) and Q4 (MHI ≥ $87,958), similar results were noted with increased ED evaluation for patients with lower MHI, but no significant difference in other factors of morbidity or mortality. ConclusionFollowing supratentorial meningioma resection, a lower median household income was significantly associated with increased emergency department visits within 90 post-operative days.

DEEP LEARNING BASED BRAIN TUMOR CLASSIFICATION USING MAGNETIC RESONANCE IMAGING

Brain tumor means the aggregation of abnormal cells in some tissues of the brain. Brain tumor can be cancerous or noncancerous. The most common types of brain tumors are Glioma, Meningioma and Pituitary tumor. Early detection of tumor cells plays a major role in treatment and recovery of patient. Diagnosing a brain tumor usually undergoes a very complicated and time consuming process. The MRI images of various patients at various stages can be used for the detection of tumors. There are various types of feature extraction and classification methods which are used for detection of brain tumor from MRI images. Convolutional Neural Network image classification algorithm helps in detecting the tumor at early stage with high accuracy. We proposed a Recurrent Neural Network architecture for detection of tumor cells which gives accuracy of about 90%. A recurrent neural network (RNN) is a type of artificial neural networks in that connections between nodes form a directed graph along a temporal sequence.

Abstract A12: DNA methylation-based liquid biopsy detects primary and recurrent meningioma

Abstract Meningiomas are mostly benign CNS tumors; however, a subset of these tumors may become atypical or malignant. The standard of care to monitor patients after diagnosis requires serial MRI assessments, which have limited value in distinguishing malignant tumors from benign disease. Therefore, the discovery of noninvasive methodologies that reflect meningioma tumor burden and its dynamic evolution in real-time is highly desirable. Liquid biopsy (LB) could be used to fine-tune surveillance and treatment with minimal risk to patients. Evidence of circulating tumor cells and cell-free (cf) tumor DNA in the blood has been shown in several tumor types; however, limited progress has been made for brain tumors with known biomarkers, possibly due to the unlikelihood of capturing point mutations in circulating DNA fragments. On the other hand, DNA methylation signatures are maintained even in small DNA fragments, which suggests that DNA methylation is an attractive marker to be studied in liquid biopsy of brain cancers. In order to identify DNA methylation-based biomarkers, we used archival serum and matching tissue specimens from primary (n=10) and recurrent (n=4) meningiomas. From isolated cfDNA from meningiomas and epileptic patients (n=5) as a control, we generated epigenetic data using Illumina Human EPIC array. cfDNA fragment size distribution revealed peaks with 150~200bp on average. We identified 482 CpGs (FDR&amp;lt;0.001) differentially methylated between primary meningiomas and controls, of which 294 (61%) show a DNA methylation profile similar to the epigenome of the matched tumor tissue. Overall, we observed that recurrent samples are hypermethylated (56%) compared to primary. From this pilot data, we were able to investigate the LB methylome of meningioma patients and identify potential markers to detect tumor cells in the serum of these patients, which could eventually allow clinicians to monitor impending disease progression and recurrence. Citation Format: Thais S. Sabedot, Tathiane M. Malta, James Snyder, Tobias Walbert, Ian Lee, Steven Kalkanis, Ana Valeria Castro, Houtan Noushmehr. DNA methylation-based liquid biopsy detects primary and recurrent meningioma [abstract]. In: Proceedings of the AACR Special Conference on Advances in Liquid Biopsies; Jan 13-16, 2020; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(11_Suppl):Abstract nr A12.

Prediction value of preoperative findings on meningioma grading using artificial neural network

ObjectivesMeningioma is the most common brain tumor in adults. Grade 1 meningiomas have excellent prognoses, but grades 2 and 3 usually have worse outcomes, higher recurrence rates, and higher mortality rates. Preoperative determination of tumor grade may be helpful in deciding the type of surgery and the rate of resection. Blood markers have been used to predict the rate of malignancy and prognosis of tumors in different regions, including the brain. The current study investigated the use of blood markers on predicting meningioma grade. Patients and methodsPatients with newly diagnosed meningiomas were retrospectively reviewed. Data on the patients’ demographics, tumor locations, blood markers, and tumor pathology grades was extracted. The relationship between preoperative findings and tumor grade was statistically analyzed, and using the same findings and an artificial neural network, the accuracy of tumor grade prediction was evaluated. ResultsThis study included 95 patients, 69 cases (72.4 %) of grade 1, 23 cases of grade 2 (24.4 %) and 3 cases of grade 3 (3.2 %) meningiomas. Monocyte and neutrophil counts as well as lymphocyte-to-monocyte ratio (LMR) were significantly different between low grade and high grade meningiomas, with higher monocyte and neutrophil counts and higher LMR associated with high grade meningiomas (p < 0.05). Evaluation of the data with an artificial neural network using RBF with 5 variables (age, monocyte count, LMR, platelet-to-lymphocyte ratio (PLR), and neutrophil count) indicated that tumor grade can be determined with 83 % accuracy using an artificial neural network. ConclusionA preoperative high monocyte count and high LMR are associated with high grade meningioma. An artificial neural network using preoperative data can acceptably be used to characterize meningioma tumor grades.

MON-237 Primary Pituitary Lymphoma - an Unusual Guest in the Sella!

BackgroundPrimary Pituitary Lymphoma (PPL) is a rare differential diagnosis for a sellar / suprasellar lesion. Less than 40 cases have been reported.Case presentationA 75-year old Chinese lady with known subclinical hypothyroidism presented with 3-day history of dull frontal headache, giddiness and diplopia. Incomplete left cranial nerve (CN) III palsy was noted with no other neurological deficit or hemodynamic instability. MRI brain and pituitary showed a T1- and T2 isointense, 2.0 x 1.1 x 1.3 cm enhancing mass arising from the left sellar region, extending to the left sphenoid and cavernous sinuses, displacing the pituitary stalk towards the right, with no optic chiasm compression. There was no imaging evidence of apoplexy.Evaluation of anterior pituitary hormones revealed hypocortisolism (peak cortisol post 1mcg Synacthen 344 nmol/L, ACTH 3.5 ng/L [10 - 60]), subclinical hypothyroidism (free T4 9.6 pmol/L [8.8 - 14.4], TSH 7.19 mU/L [0.65 - 3.70]), normal prolactin 7.4 ug/L [5.0-27.7], mildly elevated IGF-1 193.7 mcg/L [67.0 - 189.0] with normal GH 1.0 ng/ml, and elevated FSH appropriate for menopause. Glucocorticoid replacement was started.Though the clinical presentation was not typical of a pituitary macroadenoma, in view of symptomatic improvement and neurological stability, she was conservatively managed with plans for early repeat imaging outpatient.Patient was readmitted 1 month later for headache and decreased left-sided visual acuity. A complete CN III palsy, and new CN II, IV and VI deficits were noted (orbital apex syndrome). Repeat MRI showed increase in the size of the sellar lesion to 2.6 x 2.1 x 1.3 cm, surrounding the optic nerve and with left cavernous and sphenoid sinus invasion. Again, there was no suggestion of apoplexy. Biopsy of the lesion was performed, and histology was consistent with Diffuse Large B-cell Lymphoma and Grade 3A Follicular Lymphoma with high proliferative index (Ki67 70%). Immunohistochemistry excluded carcinoma, meningioma and pituitary tumor. PET-CT and bone marrow aspirate confirmed the diagnosis of PPL without metastasis.After 3 cycles of chemotherapy (Methotrexate, Rituximab, Cyclophosphamide, Doxorubicin, Vincristine and Prednisolone), significant decrease in the size of the lesion was noted on repeat MRI. Only mild asymmetric soft tissue thickening remained noticeable over the left sella and cavernous sinus. Cranial nerve deficits had since completely resolved. Glucocorticoid replacement was continued in the meantime while awaiting the completion of chemotherapy.ConclusionPPL may present in a similar manner as pituitary apoplexy. Absence of typical imaging characteristics of apoplexy in patients with rapid symptom progression and CN involvement should alert clinicians to consider alternative diagnosis including aggressive neoplastic, inflammatory and infective lesions.

Intracranial extended Psammomatoid Juvenile Ossifying Fibroma: case report and systematic review

Psammomatoid Juvenile Ossifying Fibroma is an uncommon fibro-osseous neoplasm of aggressive but benign nature found in the younger age. Its aggressive path can lead to facial deformation, eye proptosis, and development of intracranial extensions leading to various neurological symptoms. A systematic review based by the MOOSE guideline in Medline, EMBASE and Lilacs, resulted in 23 reported cases of intracranial extended PJOF. Hence, we found it relevant to present a case report of a 15-year-old male with facial deformation and left eye proptosis absent of visual disturbances with PJOF. The lesion was present in the left anterior base of the skull and extended to the intra-orbital space and over the zygomatic arch. The diagnosis was only confirmed as PJOF by histopathological analysis of the completely resected lesion. Follow up visits documented unremarkable regression of the facial deformity and post-op images showed a completely resected lesion. Our case raises the need to be aware of this rare tumor that can be confused with a meningioma or other intracranial tumors.

Brain tumor classification using modified local binary patterns (LBP) feature extraction methods

Automatic classification of brain tumor types is very important for accelerating the treatment process, planning and increasing the patient’s survival rate. Today, MR images are used to determine the type of brain tumor. Manual diagnosis of brain tumor type depends on the experience and sensitivity of radiologists. Therefore, researchers have developed many brain tumor classification models to minimize the human factor. In this study, two different feature extraction (nLBP and αLBP) approaches were used to classify the most common brain tumor types; Glioma, Meningioma, and Pituitary brain tumors. nLBP is formed based on the relationship for each pixel around the neighbors. The nLBP method has a d parameter that specifies the distance between consecutive neighbors for comparison. Different patterns are obtained for different d parameter values. The αLBP operator calculates the value of each pixel based on an angle value. The angle values used for calculation are 0, 45, 90 and 135. To test the proposed methods, it was applied to images obtained from the brain tumor database collected from Nanfang Hospital, Guangzhou, China, and Tianjin Medical University General Hospital between the years of 2005 and 2010. The classification process was performed by using K-Nearest Neighbor (Knn) and Artificial Neural Networks (ANN), Random Forest (RF), A1DE, Linear Discriminant Analysis (LDA) classification methods, with the feature matrices obtained with nLBP, αLBP and classical LBP from the images in the data set. The highest success rate in brain tumor classification was 95.56% with the nLBPd = 1 feature extraction method and Knn model.

Radioguided surgery with \u03b2\u2212 radiation in pancreatic Neuroendocrine Tumors: a feasibility study

The possibility to use β− decaying isotopes for radioguided surgery (RGS) has been recently proposed, and first promising tests on ex-vivo samples of Meningioma and intestinal Neuroendocrine Tumor (NET) have been published. This paper reports a study of the uptake of 68Ga-DOTATOC in pancreatic NETs (pNETs) in order to assess the feasibility of a new RGS approach using 90Y-DOTATOC. Tumor and healthy pancreas uptakes were estimated from 68Ga-DOTATOC PET/CT scans of 30 patients with pNETs. From the obtained SUVs (Standardised Uptake Value) and TNRs (Tumor Non tumor Ratio), an analysis algorithm relying on a Monte Carlo simulation of the detector has been applied to evaluate the performances of the proposed technique. Almost all considered patients resulted to be compatible with the application of β−-RGS assuming to administer 1.5 MBq/kg of activity of 90Y-DOTATOC 24 h before surgery, and a sampling time of few seconds. In just 2 cases the technique would have required a mildly increased amount of activity or of sampling time. Despite a high physiological uptake of 68Ga-DOTATOC in the healthy pancreas, the proposed RGS technique promises to be effective. This approach allows RGS to find application also in pancreatic diseases, where traditional techniques are not viable.

Chordoid Meningioma: Literature Review

AbstractChordoid meningiomas (CMs) are a rare subgroup of tumors, accounting for ∼ 0.5% of all meningiomas. Chordoid meningioma tumors correspond to World Health Organization (WHO) Grade II lesions and behave aggressively, with an increased likelihood of recurrence. There are few genetic studies about CMs, but we understand that there is deletion at many chromosomal loci. Histologically, CMs are characterized by strands and cords of meningothelial cells arranged in a mucinous stroma. Morphologically, it can mimic other chondroid and myxoid tumors within the brain and its vicinity, thus posing a diagnostic challenge. Chordoid meningiomas have an aggressive clinical course and a propensity to recur compared with classical meningiomas. The goal of the treatment is surgery, with total resection of the tumor; however, due to its high degree of recurrence, radiotherapy is often necessary as an adjuvant treatment.

Improved Classification of Brain Tumor in MR Images using RNN Classification Framework

Classification of brain tumor for medical applications is considered as an important constraint in computer-aided diagnosis (CAD). In this paper, we study the classification of brain tumor by considering the constraint as a classification problem in order to segregate the tumors among pituitary tumors, gliomatumorand meningioma tumor. This method adopts deep learning principle to extract the brain features from the MRI images. In this study, Recurrent Neural Network is used to classify the extracted features from brain. The experiments are carried out in terms of three fold cross-validation process over MRI brain image dataset. The results show that the proposed RNN classifier classifies the brain tumors effectively with 98% of mean classification accuracy than other existing methods.

Clinicopathological study of Meningioma

Introduction: Meningiomas are tumors that arise from the meningothelial cells. They are commonly located at intracranial, intraspinal or occasionally ectopic site. They show histological diversity and are categorized into three grades. This grading helps in predicting their behaviour and deciding treatment strategy. Aims and Objectives: To study the incidence, anatomical location, sex and age Predilection, histological variants and grading of meningiomas based on WHO 2016 classification. To correlate clinical features and radiological findings with those of histopathological findings. Materials and Methods: The study is carried out in the Department of Pathology, Dhiraj General Hospital, Piparia from November 2016 to July 2018. 30 tumors specimen diagnosed as meningioma by radiology and neurosurgery department, sent to department of pathology were included in the study. Analysis of histological features, typing and grading of all cases was done according to WHO 2016 classification of meningioma. Results: Total 30 meningioma tumors were included in the study. Most of them were intracranial, predominantly involving the posterior fossa of brain, females and the 41-60 age group. The most common histological subtype was psammomatous followed by meningothelial. Majority (93.33%) were benign grade I tumors. In 90% cases radiological diagnosis matched exactly with histopathological diagnosis. Conclusion: Meningiomas are slow growing tumors arising from the meningothelial cells accounting for 15-30 % of all CNS neoplasms showing a variety of histological patterns, more common in women, predominantly Grade I tumors. Recurrence of tumors depends on histological grade and extent of surgery.

'T-SLIP' MRI imaging of cerebrospinal fluid flow through the mesencephalic aqueduct.

A 9-year-old neutered male Chihuahua was presented to the Animal Medical Center at Nihon University as an emergency patient. The dog was stuporous with a previous history of seizures and progressive neurological deficits, including circling and blindness, for 1 month. MRI revealed compression of the mesencephalic aqueduct by an extra-axial mass that displaced the diencephalon and brainstem dorsally, bilateral enlargement of the lateral ventricles and absence of cerebrospinal fluid (CSF) signal in the cerebral sulci. The mass was T1-hyperintense, T2- and FLAIR-hypointense and enhanced homogenously on post-contrast T1-weighted images (Fig. 1). Obstructive hydrocephalus secondary to a presumed brain tumour (e.g. meningioma, histiocytic sarcoma or granular cell tumour) was diagnosed and treated by ventriculoperitoneal shunting. Intraoperative intracranial pressure was ~24 mmHg (measured with ICP EXPRESS, CODMAN/Johnson and Johnson, New Brunswick, NJ, USA) before shunting and ~4 mmHg after shunting with a medium pressure valve system (Medtronic Japan, Tokyo, Japan). There was visible reduction in ventricular size and reappearance of CSF signal in the cerebral sulci on postoperative MR images (Fig. 1). T-SLIP images were acquired only after surgery (because we needed to limit anaesthesia time when the dog initially presented as an emergency) and confirmed CSF flow through the aqueduct (Fig. 2, Video S1, Supporting information). The mass was treated with radiotherapy (six doses of 5.5Gy; total 33Gy) and a good quality of life, including resolution of circling, recovery of mentation and blindness, was maintained for more than 8 months at the time of writing. T-SLIP directly images CSF flow and, in this case, implied that the aqueduct might have been narrowed rather than completely obstructed. As commonly occurs, treatment of obstructive hydrocephalus associated with an intracranial mass lesion by ventriculoperitoneal shunting and radiation therapy ameliorated the clinical signs for a prolonged period. The authors gratefully acknowledge Shinya Yamada for technical assistance. No conflicts of interest have been declared by any of the authors. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

An audit of brain tumor patients treated in 5 years at a single institute: Our regional cancer center experience.

Brain tumors constitute a small presentation of all cancers (1.4%) and cancer related deaths (2.5%). Most of the brain tumors are malignant and carry bad prognosis and even if those which are benign still can interfere with brain functions that are required for daily living. We did an audit of brain tumor patients treated in our center and see their prognosis in correlation with different histological types. We analyzed 497 patients treated at our center from June 2007 to June 2012 of different histological types. All the patients underwent complete central nervous system examination and thorough workup including hematological investigations, radiological investigations, and confirmation of histological type by biopsy. These patients were then treated with surgery followed by radiotherapy or upfront curative radiotherapy as per staging and then kept on regular follow-up as per institutional protocol. Majority of patients were astrocytomas (309 patients) followed by a pituitary adenoma (39 patients), oligodendroglioma (33 patients), medulloblastoma (22 patients), arteriovenous malformations (19 patients), craniopharyngioma (16 patients), and recurrent brain tumors (12 patients). There were few cases of central nervous lymphoma, meningioma, melanocytoma, ependymomas, schwannomas, hemangiopericytoma, gliosarcoma, and primitive neuroectodermal tumor. The histological types vary differently regarding presentation, treatment, and prognosis. Astrocytomas are the most common tumors in presentation (309 patients) in which the high-grade astrocytomas carried worse prognosis. Benign tumors such as pituitary adenomas, schwannomas, and craniopharyngiomas had the best prognosis with survival seen up to almost last follow-up.