Brain Tumor Location Research Articles

Measurement Extraction using Fuzzy Set Rule for Segmented Features of Brain Tumor in T-1 & T-2 Weighted Images

Aims: For neuro radiologist it becomes hard to accumulate features with minute dissimilarity in plenty of cases, so it is hard to make a correct decision. Therefore, the need is to generate some rules for prediction of degree of malignancy in tumors.
 Design: The pre-operative analysis of brain lesion is based on magnetic resonance imaging and clinical data set. Analysis of MRI finding and medical data set gives the relationship between regular pattern & interpretable pattern to acquire desired degree of malignancy. Until now the edge detection, segmentation and morphological operators are used to detect exact location of brain tumor. As uncertainty exits; here fuzzy set rules are evaluated to predict the degree by which a benign tumor is converted into malignant tumor.
 Methods: Fuzzy extraction theory has been applied along with image progressing algorithms like edge detection; segmentation and morphological operation based on spectral transformation are used to detect exact location of brain tumor to predict the degree malignancy. Step of Image analysis: a) Preprocessing: input 2D gif or tiff image b) Filtering of image using Anisodiff filter c) Thresholding, applying morphological operators and tumor line detection.
 Statistical Analysis used: A diagnostic feature includes blood flow, mass effect, temperature, calcification, edema, signal intensity & so on. Numerous features can be taken into consideration for better outcome.
 Results: Fuzzy set rule is one of the promising methods along with MR finding to achieve accuracy higher than 85% by considering few of the medical symptoms on different features.
 Conclusions: This research is limited to specific region and type of glioma and thus cannot deal heterogeneous cases in which situation is much complicated. The result evaluated here are usually retroactive. As studied, by analyzing signal intensity of T-1 & T-2 weighted image alone, accuracy of 60-70% has been achieved. So in order to get higher accuracy feature like cyst generation, oedema, blood supply are included to achieve 85% accuracy.

The correlation between brain tumor location and onset of neuroophthalmic symptoms in brain tumor patients in Adam Malik General Hospital Medan

Brain tumor is one of the tenth leading cause of death. Brain tumor can cause visual impairment by affecting the visual pathway. Important predictors for visual impairment are the tumor location (supratentorial or infratentorial) and elevated intracranial pressure. Patients with brain tumor often meet their ophthalmologist first. The signs and symptoms of neuroophthalmic can be one of suspicion to diagnose brain tumor. This study want to determine the correlation between brain tumor location and onset of neuroophthalmic symptoms in brain tumor

Clustering of Brain Tumor Based on Analysis of MRI Images Using Robust Principal Component Analysis (ROBPCA) Algorithm.

Automated detection of brain tumor location is essential for both medical and analytical uses. In this paper, we clustered brain MRI images to detect tumor location. To obtain perfect results, we presented an unsupervised robust PCA algorithm to clustered images. The proposed method clusters brain MR image pixels to four leverages. The algorithm is implemented for five brain diseases such as glioma, Huntington, meningioma, Pick, and Alzheimer's. We used ten images of each disease to validate the optimal identification rate. According to the results obtained, 2% of the data in the bad leverage part of the image were determined, which acceptably discerned the tumor. Results show that this method has the potential to detect tumor location for brain disease with high sensitivity. Moreover, results show that the method for the Glioma images has approximately better results than others. However, according to the ROC curve for all selected diseases, the present method can find lesion location.

Differential exposure to N-ethyl N-nitrosourea during pregnancy is relevant to the induction of glioma and PNSTs in the brain

Exposure to N-nitroso compounds (NOCs) during pregnancy has been associated with an increase in brain tumors in the progeny. This study investigated the brain tumorigenic effect of N-ethyl N-nitrosourea (ENU) after differential exposure of rats during pregnancy.Sprague Dawley rats were exposed to a single dose of ENU (80 mg/kg) in three different circumstances: 1) at first, second or third week of gestation; 2) at the 15th embryonic day (E15) in consecutive litters and 3) at E15 in three successive generations. Location and characterization of the offspring's brain tumors were performed by magnetic resonance imaging and histopathological studies. Finally, tumor incidence and latency and the animals' survival were recorded.ENU-exposure in the last two weeks of pregnancy induced intracranial tumors in over 70% of the offspring rats, these being mainly gliomas with some peripheral nerve sheath tumors (PNSTs). Tumors appeared in young adults; glioma-like small multifocal neoplasias converged on large glioblastomas in senescence and PNSTs in the sheath of the trigeminal nerve, extending to cover the brain convexity. ENU-exposure at E15 in subsequent pregnancies lead to an increase in glioma and PNST incidence. However, consecutive generational ENU-exposure (E15) decreased the animals' survival due to an early onset of both types of tumors. Moreover, PNST presented an inheritable component because progeny, which were not themselves exposed to ENU but whose progenitors were, developed PNSTs.Our results suggest that repeated exposure to ENU later in pregnancy and in successive generations favours the development of intracranial gliomas and PNSTs in the offspring.

Brain Tumor Detection and Classification Using Deep Learning Techniques based on MRI Images

The application of deep learning approaches in context to improve health diagnosis is providing impactful solutions. According to the World Health Organization (WHO), proper brain tumor diagnosis involves detection, brain tumor location identification, and classification of the tumor on the basis of malignancy, grade, and type. This experimental work in the diagnosis of brain tumors using Magnetic Resonance Imaging (MRI) involves detecting the tumor, classifying the tumor in terms of grade, type, and identification of tumor location. This method has experimented in terms of utilizing one model for classifying brain MRI on different classification tasks rather than an individual model for each classification task. The Convolutional Neural Network (CNN) based multi-task classification is equipped for the classification and detection of tumors. The identification of brain tumor location is also done using a CNN-based model by segmenting the brain tumor.

Automatic clustering based approach for brain tumor extraction

The brain cancer is a deadly disease affecting almost 1.7% of world’s population with top mortality rate. The basic cause of brain cancer is the abnormal growth of brain tissues in the early stage. Further, these tissues are converted into tumors. Early detection and exact location of brain tumor can assist for further therapies of brain. In this paper a computer vision-based approach is developed for exacting an exact location of the brain tumors from the MRI images. The method starts from skull stripping for removal of the outer portion of brain and segregate the white matter. Further communication with local agent (CLA) clustering technique is applied followed by morphological post processing methods for extraction of tumor regions from white matter regions of the brain. The method is tested on a publicly available MRI dataset. Quantitative and qualitative measures show that the proposed method achieves an accuracy of 99.64% as compared to others.

A Study of anImproved Edge Detection Algorithm for MRI Brain Tumor Images Based on Image Quality Parameters

Biomedical imaging with emerging technology plays a vital role in medical diagnosis and treatment. MRI is one such biomedical imaging technology. The incidence of brain tumor in increasing all over the world. Accurate detection of size and location of brain tumor helps in right diagnosis and treatment. In any kind of image processing, image segmentation is a fundamental step. Edge detection is an important technique in the analysis of images, which plays a crucial role in detecting the contour of brain tumor. In this paper, an improved algorithm called the Luminance edge detection algorithm is proposed and compared with the existing edge detection algorithms like Prewitt, Sobel, and Canny for the MRI images of the brain. PSNR and SSIM were calculated to compare the image quality of different edge detection techniques. Results revealed that the PSNR and SSIM values were slightly higher for the images obtained by the Luminance edge detection algorithm when compared to the images obtained by Prewitt, Sobel, and Canny edge detection algorithm. It proves that the proposed algorithm is better than the other edge detection algorithms in producing a quality image of the MRI brain. Hence it can be concluded that the proposed algorithm was better in detecting the contour of brain and brain tumor than the other edge detection techniques.

SA-LuT-Nets: Learning Sample-Adaptive Intensity Lookup Tables for Brain Tumor Segmentation.

In clinics, the information about the appearance and location of brain tumors is essential to assist doctors in diagnosis and treatment. Automatic brain tumor segmentation on the images acquired by magnetic resonance imaging (MRI) is a common way to attain this information. However, MR images are not quantitative and can exhibit significant variation in signal depending on a range of factors, which increases the difficulty of training an automatic segmentation network and applying it to new MR images. To deal with this issue, this paper proposes to learn a sample-adaptive intensity lookup table (LuT) that dynamically transforms the intensity contrast of each input MR image to adapt to the following segmentation task. Specifically, the proposed deep SA-LuT-Net framework consists of a LuT module and a segmentation module, trained in an end-to-end manner: the LuT module learns a sample-specific nonlinear intensity mapping function through communication with the segmentation module, aiming at improving the final segmentation performance. In order to make the LuT learning sample-adaptive, we parameterize the intensity mapping function by exploring two families of non-linear functions (i.e., piece-wise linear and power functions) and predict the function parameters for each given sample. These sample-specific parameters make the intensity mapping adaptive to samples. We develop our SA-LuT-Nets separately based on two backbone networks for segmentation, i.e., DMFNet and the modified 3D Unet, and validate them on BRATS2018 and BRATS2019 datasets for brain tumor segmentation. Our experimental results clearly demonstrate the superior performance of the proposed SA-LuT-Nets using either single or multiple MR modalities. It not only significantly improves the two baselines (DMFNet and the modified 3D Unet), but also wins a set of state-of-the-art segmentation methods. Moreover, we show that, the LuTs learnt using one segmentation model could also be applied to improving the performance of another segmentation model, indicating the general segmentation information captured by LuTs.

Tumor hypoxia represses γδ T cell-mediated antitumor immunity against brain tumors.

The anatomic location and immunologic characteristics of brain tumors result in strong lymphocyte suppression. Consequently, conventional immunotherapies targeting CD8 T cells are ineffective against brain tumors. Tumor cells escape immunosurveillance by various mechanisms and tumor cell metabolism can affect the metabolic states and functions of tumor-infiltrating lymphocytes. Here, we discovered that brain tumor cells had a particularly high demand for oxygen, which affected γδ T cell-mediated antitumor immune responses but not those of conventional T cells. Specifically, tumor hypoxia activated the γδ T cell protein kinase A pathway at a transcriptional level, resulting in repression of the activatory receptor NKG2D. Alleviating tumor hypoxia reinvigorated NKG2D expression and the antitumor function of γδ T cells. These results reveal a hypoxia-mediated mechanism through which brain tumors and γδ T cells interact and emphasize the importance of γδ T cells for antitumor immunity against brain tumors.

Improving the characterization of meningioma microstructure in proton therapy from conventional apparent diffusion coefficient measurements using Monte Carlo simulations of diffusion MRI.

Proton therapy could benefit from noninvasively gaining tumor microstructure information, at both planning and monitoring stages. The anatomical location of brain tumors, such as meningiomas, often hinders the recovery of such information from histopathology, and conventional noninvasive imaging biomarkers, like the apparent diffusion coefficient (ADC) from diffusion-weighted MRI (DW-MRI), are nonspecific. The aim of this study was to retrieve discriminative microstructural markers from conventional ADC for meningiomas treated with proton therapy. These markers were employed for tumor grading and tumor response assessment. DW-MRIs from patients affected by meningioma and enrolled in proton therapy were collected before (n=35) and 3 months after (n=25) treatment. For the latter group, the risk of an adverse outcome was inferred by their clinical history. Using Monte Carlo methods, DW-MRI signals were simulated from packings of synthetic cells built with well-defined geometrical and diffusion properties. Patients' ADC was modeled as a weighted sum of selected simulated signals. The weights that best described a patient's ADC were determined through an optimization procedure and used to estimate a set of markers of tumor microstructure: diffusion coefficient (D), volume fraction (vf), and radius (R). Apparent cellularity (ρapp ) was estimated from vf and R for an easier clinical interpretability. Differences between meningothelial and atypical subtypes, and low- and high-grade meningiomas were assessed with nonparametric statistical tests, whereas sensitivity and specificity with ROC analyses. Similar analyses were performed for patients showing low or high risk of an adverse outcome to preliminary evaluate response to treatment. Significant (P<0.05) differences in median ADC, D, vf, R, and ρapp values were found when comparing meningiomas' subtypes and grades. ROC analyses showed that estimated microstructural parameters reached higher specificity than ADC for subtyping (0.93 for D and vf vs 0.80 for ADC) and grading (0.75 for R vs 0.67 for ADC). High- and low-risk patients showed significant differences in ADC and microstructural parameters. The skewness of ρapp was the parameter with highest AUC (0.90) and sensitivity (0.75). Matching measured with simulated ADC yielded a set of potential imaging markers for meningiomas grading and response monitoring in proton therapy, showing higher specificity than conventional ADC. These markers can provide discriminative information about spatial patterns of tumor microstructure implying important advantages for patient-specific proton therapy workflows.

A type-2 neutrosophic-entropy-fusion based multiple thresholding method for the brain tumor tissue structures segmentation

In this paper, we introduce an extension theory of the neutrosophic set (NS) called type-2 neutrosophic set (T2NS). This new theory provides a granular representation of features and helps to model uncertainties with six different memberships very effectively. To demonstrate the real-time application of this theory, a new segmentation method for brain tumor tissue structures in magnetic resonance imaging (MRI) is presented. There are inconsistencies in the gray levels observed in MRIs due to their low illumination. The proposed theory addressed this problem by performing neutrosophication operation on gray levels based on six different membership functions called type-2 neutrosophic membership functions. During segmentation, a concept of T2NS entropy is used to quantify each gray level of MRIs. The proposed method is able to select multiple adaptive thresholds for segmentation of brain tumor tissue structures in MRIs from the locations of maximum entropy values of gray levels. Finally, an image fusion operation is performed on the segmented images with different thresholds to include all features and identify the location of brain tumor. The fusion images are compared with the segmented images obtained by five different methods, including fuzzy c-means algorithm, modified fuzzy c-means algorithm, fuzzy-K-means clustering algorithm, kernel intuitionistic fuzzy entropy c-means and neutrosophic entropy-based adaptive thresholding method. The proposed method achieves Jaccard similarity coefficients of 97.07%, 97.92% and 97.13% in the case of three different sets of MRIs, namely Set I, Set II and Set III, respectively. The proposed method exhibits correlation coefficients of 0.9638, 0.9698 and 0.9610 for the Set I, Set II and Set III of MRIs, respectively. Similarly, the proposed method shows uniformity measures of 0.9624, 0.9633 and 0.9660 for the Set I, Set II and Set III of MRIs, respectively. These three performance evaluation metrics show the effectiveness of the proposed method compared to the existing methods.

Brain Tumor Location Identification and Patient Observation from MRI Images

Many patients are dying day after day due to the hypothetical treatment of brain tumors. Brain tumor detection and location identification are very ticklish and time-consuming task in biomedical image processing due to the complex structure of the brain. It is an important factor for detecting the tumor at the early stage that prevents the death from this epidemic disease. One of the most used and popular for the brain tumor diagnosis system is Magnetic Resonance Imaging (MRI) but it is a manual and time-consuming approach. In this research, an automated system has proposed to detect, locate and observe the tumor area in the patient brain. In the tumor extraction stage, the morphological operation based and marker control watershed segmentation methods used to extract the affected region from brain hemisphere through some additional step like filtrations and noise removal. In the tumor location identification stage, the brain hemisphere divided into four parts and compared among them by counting the pixels value that gave the proper location of the tumor in the hemisphere of the brain. Finally, this research study proposed a patient observation technique that compared the tumor growth of patient in timely.

Automated multi-class brain tumor types detection by extracting RICA based features and employing machine learning techniques.

Among the other cancer types, the brain tumor is one the leading cause of cancer across globe. If the tumor is properly identified at an earlier stage, then the chances of the survival can be increased. To categorize the brain tumor there are several factors including texture, type and location of brain tumor. We proposed a novel reconstruction independent component analysis (RICA) feature extraction method to detect multi-class brain tumor types (pituitary, meningioma, and glioma). We then employed the robust machine learning techniques as support vector machine (SVM) with quadratic and linear kernels and linear discriminant analysis (LDA). For training and testing of the data validation, a 10-fold cross validation was employed. For the multi-class classification, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy and AUC were, respectively, 97.78%, 100%, 100%, 99.07, 99.34% and 0.9892 to detect pituitary using SVM Cubic followed by meningioma with accuracy (96.96%0, AUC (0.9348) and glioma with accuracy (95.88%), AUC (0.9635). The findings indicates that RICA feature based proposed methodology has more potential to detect the multiclass brain tumor types for improving diagnostic efficiency and can further improve the prediction accuracy to achieve the clinical outcomes.

MRI Brain Tumor Segmentation Using 3D U-Net with Dense Encoder Blocks and Residual Decoder Blocks

The main task of magnetic resonance imaging (MRI) automatic brain tumor segmentation is to automatically segment the brain tumor edema, peritumoral edema, endoscopic core, enhancing tumor core and nonenhancing tumor core from 3D MR images. Because the location, size, shape and intensity of brain tumors vary greatly, it is very difficult to segment these brain tumor regions automatically. In this paper, by combining the advantages of DenseNet and ResNet, we proposed a new 3D U-Net with dense encoder blocks and residual decoder blocks. We used dense blocks in the encoder part and residual blocks in the decoder part. The number of output feature maps increases with the network layers in contracting path of encoder, which is consistent with the characteristics of dense blocks. Using dense blocks can decrease the number of network parameters, deepen network layers, strengthen feature propagation, alleviate vanishing-gradient and enlarge receptive fields. The residual blocks were used in the decoder to replace the convolution neural block of original U-Net, which made the network performance better. Our proposed approach was trained and validated on the BraTS2019 training and validation data set. We obtained dice scores of 0.901, 0.815 and 0.766 for whole tumor, tumor core and enhancing tumor core respectively on the BraTS2019 validation data set. Our method has the better performance than the original 3D U-Net. The results of our experiment demonstrate that compared with some state-of-the-art methods, our approach is a competitive automatic brain tumor segmentation method.

Detection of Exact Location of Brain Tumor from MRI Data Using Big Data Analytics

MRI is the imaging technique most often used to detect brain tumor. A brain tumor is a knot, or mass, of abnormal cells in parts of the brain. Brain tumors can be either malignant or benign and can be located in the tissues of the brain. In this research study, a computerized approach has been presented where MRI gray- scale images were assimilated for the detection of brain tumor. This study suggested a computerized approach that involves improvement at the elementary stage to reduce the gray-scale color variations. Filter operation was used to eliminate undesired noises as much as feasible to accommodate better segmentation. As this study test grayscale images therefore; threshold-based OTSU segmentation was used instead of color segmentation. Finally, specialists in the field of pathology provided feature intelligence that was used to recognize the zone of interests for brain tumor. This study pertained a novel architecture, named Xception, which permitted both elevated presentation, diminished expanse and estimated charge of deep neural networks employing depth wise separable convolution to establish high performance computer aided diagnosis system for brain tumor detection from MRI. Preparatory appraisal for the Xception model employing transfer learning exhibited exceptional performance with immense efficiency and prediction probability. Fascinatingly, prediction probabilities were distinct when various layers were reviewed.

Towards effective machine learning in medical imaging analysis: A novel approach and expert evaluation of high-grade glioma ‘ground truth’ simulation on MRI

Purpose/objective(s)Gliomas are uniformly fatal brain tumours with significant neurological and quality of life detriment to patients. Improvement in outcomes has remained largely unchanged in nearly 20 years. MRI (magnetic resonance imaging) is often used in diagnosis and management. Machine learning analyses of large-scale MRI data are pivotal in advancing the diagnosis, management and improve outcomes in neuro-oncology. A common challenge to robust machine learning approaches is the lack of large ‘ground truth’ datasets in supervised learning for building classification and prediction models. The creation of these datasets relies on human-expert input and is time-consuming and subjective error-prone, limiting effective machine learning applications. Simulation of mechanistic aspects such as geometry, location and physical properties of brain tumours can generate large-scale ground-truth datasets allowing for comparison of analysis techniques in clinical applications. We aimed to develop a transparent and convenient method for building ‘ground truth’ presentations of simulated glioma lesions on anatomical MRI. Materials/methodsThe simulation workflow was created using the Feature Manipulation Engine (FME®), a data integration platform specializing in the spatial data processing. By compiling and integrating FME’s functions to read, integrate, transform, validate, save, and display MRI data, and experimenting with ways to manipulate the parameters concerning location, size, shape, and signal intensity with the presentations of glioma, we were able to generate simulated appearances of high-grade gliomas on gadolinium-based high-resolution 3D T1-weighted MRI (1 mm3). Data of patients with canonical high-grade tumours were used as real-world tumours for validating the accuracy of the simulation. Twenty raters who are experienced with brain tumour interpretation on MRI independently completed a survey, designed to distinguish simulated and real-world brain tumours. Sensitivity and specificity were calculated for assessing the performance of the approach with the binary classification of simulated vs real-world tumours. Correlation and regression were used in run time analysis, assessing the software toolset’s efficiency in producing different numbers of simulated lesions. Differences in the group means were examined using the non-parametric Kruskal-Wallis test. ResultsThe simulation method was developed as an interpretable and useful workflow for the easy creation of tumour simulations and incorporation into 3D MRI. A linear increase in the running time and memory usage was observed with an increasing number of generated lesions. The respondents' accuracy rate ranged between 33.3 and 83.3 %. The sensitivity and specificity were low for a human expert to differentiate simulated lesions from real gliomas (0.43 and 0.58) or vice versa (0.65 and 0.62). The mean scores ranking the real-world gliomas did not differ between the simulated and real tumours. ConclusionThe reliable and user-friendly software method can allow for robust simulation of high-grade glioma on MRI. Ongoing research efforts include optimizing the workflow for generating glioma datasets as well as adapting it to simulating additional MRI brain changes.

Final height in growth hormone-deficient childhood cancer survivors after growth hormone therapy

Growth hormone deficiency (GHD) is the most common endocrine late effect in children with tumours involving the hypothalamus-pituitary (HP) area or exposed to cranial, craniospinal or total body radiotherapy (RT). The location of brain tumours in continuity with the sellar/suprasellar area and HP radiation doses ≥18 Gy represent the most important risk factors for GHD.

The Impact Radiation Therapy Variables on Pediatric High-Grade Glioma (pHGG) Outcomes: A National Cancer Database Analysis

Pediatric high-grade gliomas (HGGs) are a devastating disease with a poor prognosis. Radiation therapy (RT) is standard of care in the definitive treatment of pediatric HGG, yet data addressing the impact of varying RT variables on outcomes are lacking. The purpose of this analysis is to evaluate the impact of RT variables on outcomes of pediatric HGG patients in the National Cancer Database (NCDB). The NCDB participant user file including cases diagnosed between 2003-2015 was used to select patients age <22 with histologically proven WHO Grade III and IV high-grade gliomas, brain tumor location codes (ICD-O-3 C710-C719), and who received RT of dose ≥50Gy and <76Gy between 2004 and 2013. RT variables including RT dose, timing between diagnosis and RT initiation (<4 weeks, 4-6 weeks, or >6 weeks), and RT modality were analyzed along with baseline demographic, tumor and treatment variables to assess the impact on overall survival in Univariate and Multivariable analyses by the proportional hazards regression. Kaplan-Meier Curves were generated and compared by Log-rank tests for the covariates above. 498 pediatric HGG patients were included. The median age at diagnosis was 15 years (range 0-21), 54% of patients were male and 79% white. Histologies included glioblastoma (30%), astrocytoma (55%), oligogendroglioma (5%) and gliomas not otherwise specific (10%). Surgery was performed in 73.5% of patients and 90% of patients received chemotherapy. Chemotherapy was initiated either before or during radiation in 80% of patients, and after RT was completed in 5% of patients. The median RT dose was 59.4 Gy (SD 2.9 Gy) starting a median of 4.4 weeks from diagnosis (SD 2.5 weeks). Median follow-up was 19.6 months (Q1-Q3: 11.8 – 44.5) with 1- and 3-year OS (95% CI) of 78.4% and 40.4%, respectively. There was a non-significant trend between RT dose >59.4 Gy and longer overall survival (HR 0.80 (0.62-1.04), p = 0.092) on univariate analyses. On Multivariable analysis, female gender, older age, and private insurance remained independently associated with lower rate of overall death. Radiation delay of ≤4 weeks from diagnosis and glioblastoma histology were significantly associated with higher rate of overall death. There was no difference in OS according to whether RT was delivered with photons (IMRT or 3-D conformal) or proton therapy. Outcomes for pediatric HGG are poor. Delay of RT initiation beyond 6 weeks from diagnosis does not appear to negatively impact outcomes. Early initiation of RT within 4 weeks may be detrimental or related to unknown prognostic factors.Tabled 1Abstract 2552; Table. Multivariable Survival Analysis of Overall Survival Main EffectCovariateDescriptionHazard Ratio (95% CI)HR P-valueSexFemale0.71 (0.56 - 0.90)0.005Primary PayorPrivate insurance0.73 (0.56 - 0.96)0.025Radiation Delay<4 weeks from diagnosis1.55 (1.05 - 2.25)0.028HistologyGlioblastoma1.72 (1.33 - 2.24)<0.001 Open table in a new tab

BRAF G469A, BRAF L597V, FGFR1 N546K, FGFR1 K656E and P53 R273H mutations: How are they distributed in Moroccan patients with pediatric Low-Grade Gliomas?

BackgroundLow-Grade Gliomas are common brain tumors in children. They are known for their heterogeneity, especially their molecular heterogeneity. This makes their definition and classification based only on histological criteria difficult and lacking in precision. If this presented a limit to progress in the understanding of these tumors, advances in molecular biology and molecular genetics have allowed us to deepen our knowledge of the nature of these tumors and the way they develop, which helps us considerably in improving their classification, diagnosis and treatment. AimWe identified the distribution of BRAF G469A, BRAF L597V, FGFR1 N546K, FGFR1 K656E and P53 R273H mutations in Moroccan patients with pLGGs, according to their sex, age, brain tumor location and subtype and we compared the results with their distribution in Moroccan patients with pHGGs. MethodologyWe studied BRAF G469A, BRAF L597V, FGFR1 N546K, FGFR1 K656E and P53 R273H mutations in 89 pLGG tissues by comparing them with 123 hLGG tissues, using PCR-RFLP and TaqMan. Data was analyzed by using IBM SPSS software v24. ResultsBy studying the involvement of the BRAF G469A mutation, the BRAF L597V mutation, the FGFR1 N546K mutation, the FGFR1 K656E mutation and the P53 R273H mutation in the development of Pediatric Low-Grade Gliomas, we obtained significant results for the BRAF L597V mutation. 6.7% of the pLGG patients studied were carriers of this mutation (P = 0.0450). (2/2) of these patients with the tumor localized in the hemispheres, (1/8) in the SAI nervous system and (3/29) in the spinal cord were positive. As for the P53 R273H mutation, (19/78) of the pLGG positive cases were Pilocytic Astrocytomas, (1/4) Pilomyxoid Astrocytomas and (4/6) Diffuse Gliomas. By studying the different interactions between the mutations, we obtained significant interactions between the presence of the BRAF L597V mutation and the absence of the BRAF G469A mutation (P = 0.0415), the presence of the BRAF L597V mutation and the absence of the FGFR1 N546K mutation (P = 0.0448) and the presence of the FGFR1 K656E and P53 R273H mutations (P = 0.0056). ConclusionIn our future studies, we plan to continue in the same vein and explore other mutations suspected to be involved in the development of pediatric Low-Grade Gliomas. Studying them would be an opportunity to understand the origin of these tumors but also a field to improve their classification and thus their diagnosis and treatment.

Risk factors for osteonecrosis of the femoral head in brain tumor patients receiving corticosteroid after surgery.

PurposeNon-traumatic osteonecrosis of the femoral head (ONFH) is a plausible complication in brain tumor patients. Frequent use of corticosteroid therapy, chemotherapy, and oxidative stress for managing brain tumors may be associated with the development of ONFH. However, there is little knowledge on the prevalence and risk factors of ONFH from brain tumor. This study aimed to investigate the prevalence and risk factors of ONFH in patients with primary brain tumors.MethodsThis retrospective cohort study included data from consecutive patients between December 2005 and August 2016 from a tertiary university hospital in South Korea. A total of 73 cases of ONFH were identified among 10,674 primary brain tumor patients. After excluding subjects (25 out of 73) with missing data, history of alcohol consumption or smoking, history of femoral bone trauma or surgery, comorbidities such as systemic lupus erythematosus (SLE), sickle cell disease, cancer patients other than brain tumor, and previous diagnosis of contralateral ONFH, we performed a 1:2 propensity score-matched, case–control study (ONFH group, 48; control group, 96). Risk factors of ONFH in primary brain tumor were evaluated by univariate and multivariate logistic regression analyses.ResultsThe prevalence of ONFH in patients with surgical resection of primary brain tumor was 683.9 per 100,000 persons (73 of 10,674). In this cohort, 55 of 74 patients (74.3%) underwent THA for ONFH treatment. We found that diabetes was an independent factor associated with an increased risk of ONFH in primary brain tumor patients (OR = 7.201, 95% CI, 1.349–38.453, p = 0.021). There was a significant difference in univariate analysis, including panhypopituitarism (OR = 4.394, 95% CI, 1.794–11.008, p = 0.002), supratentorial location of brain tumor (OR = 2.616, 95% CI, 1.245–5.499, p = 0.011), and chemotherapy (OR = 2.867, 95% CI, 1.018–8.069, p = 0.046).ConclusionsThis study demonstrated that the prevalence of ONFH after surgical resection of primary brain tumor was 0.68%. Diabetes was an independent risk factor for developing ONFH, whereas corticosteroid dose was not. Routine screening for brain tumor-associated ONFH is not recommended; however, a high index of clinical suspicion in these patients at risk may allow for early intervention and preservation of the joints.