Search

Background: Glioma tumor is the most common primary brain tumor and extent of resection of this tumor plays a significant role in the survival rate of the patients. Intraoperative ultrasound (IOUS) has become a convenient technique in brain tumor surgery due to its non-invasiveness, affordability, and real-time imaging capabilities, making it appealing to neurosurgeons, and assists neurosurgeons in identifying the tumor's location and adjacent structures during surgery. Materials & Method: This study was done prospectively, in Rasul Akram and Shahada-ye-Haftam Tir hospitals, Tehran, Iran, and evaluated IOUS's use in 20 patients with brain glioma between 2019 and 2022. Simple random sampling was used to select patients. The resection was performed using IOUS, and the extent of resection was assessed through imaging. We used SonoSite convex ultrasound probe with bandwidth of 10-3 MHz and scan depth of 3 to 18 cm. Results: The study found that with ultrasound guidance, the average mass resection rate was 89.3%. The use of ultrasound during surgery improved the resection rate compared to previous studies. Motor complications observed in 15% of patients after surgery included paralysis and verbal deficits, while the rate of meningitis was low. The average length of hospitalization for patients was 10 days, and the average intraoperative bleeding was 344 cubic centimeters. Conclusion: Ultrasound can be a valuable tool in resection of brain glioma as it provides real-time imaging and assists in tumor resection, and detect adjacent structures. However, it should be used in conjunction with other monitoring modalities. Further studies are necessary to explore the full potential and limitations of IOUS in neurosurgery. [ABSTRACT FROM AUTHOR]

In the present study, preparation of [188Re]Re-Tricine-HYNIC-Tyr3-octreotide, stability in human serum, internalization, cell viability analysis, and in vivo tissue biodistribution in rats bearing C6 glioma tumor were investigated. Radiolabeled peptide exhibited a molar activity of 8.60 ± 0.76 GBq/µmol, with over 90% (n = 3) of radiochemical yield. The findings indicated significant internalization of radiolabeled peptide into glioma cells and the ability to reduce cancerous cell viability in MTT analysis. High tumor-to-muscle ratios were observed at various time points following injection. The results suggest that the radiolabeled peptide could be considered for treating tumors expressing somatostatin receptors. [ABSTRACT FROM AUTHOR]

Glioma is a tumor in the brain and spinal cord originating in the glial cells that surround the nerve cells. Among several microRNAs reported, miRNA‐363 is associated with human glioma. Based on miRNA‐363 levels, the development and progression of glioma can be monitored. The current study used an interdigitated electrode sensor to monitor microRNA‐363 levels, which indeed reflects the severity of glioma. The interdigitated electrode was generated using a photolithography technique followed by surface chemical modification carried out to insert miRNA‐363 complementary oligo as the probe complexed with gold nanoparticles. The proposed sensor works based on the dipole moment between two electrodes, and when molecular immobilization or interaction occurs, the response by the signal output changes. The changes in the target microRNA‐363 sequence were standardized to identify glioma. The limit of detection of miRNA‐363 was 10 fM with an R2 value of 0.996 on the linear coefficient regression ranges between 1 fM and 100 pM. Furthermore, unrelated sequences failed to increase the response of the current with the complementary probe, indicating specific miRNA‐363 detection on the interdigitated electrode. This study demonstrates the platform to be used for determining the presence of microRNA‐363 in glioma and as the basis for other biomarker analyses. [ABSTRACT FROM AUTHOR]

In recent years, the subject of medical image categorization has expanded significantly, with Convolutional Neural Networks (CNN) being the most popular neural network model for this purpose. Using components like convolutional layers, pooling layers, and fully connected layers, CNNs are built to learn features adaptively through backpropagation. This study aims to develop and deploy a CNN model for identifying brain cancers using MRI(Magnetic Resonance Image) scans obtained from Kaggle. The proposed methodology consists of two phases: preprocessing the pictures using various image processing techniques and then training a CNN model to categorize the modified images. We test the model on 3,264 MRI pictures representing four kinds of brain cancers (glioma, meningioma, no tumor, and pituitary). Results from the experiment showed a training dataset accuracy of 99.39% and a validation dataset accuracy of 96.18%. [ABSTRACT FROM AUTHOR]

Glioblastoma is the most common form of brain cancer in adults that produces severe damage to the brain leading to a very poor survival prognosis. The standard of care for glioblastoma is usually surgery, as well as radiotherapy followed by systemic temozolomide chemotherapy, resulting in a median survival time of about 12 to 15 months. Despite these therapeutic efforts, the tumor returns in the vast majority of patients. When relapsing, statistics suggest an imminent death dependent on the size of the tumor, the Karnofsky Performance Status, and the tumor localization. Following the standard of care, the administration of Bevacizumab, inhibiting the growth of the tumor vasculature, is an approved medicinal treatment option approved in the United States, but not in the European Union, as well as the recently approved alternating electric fields (AEFs) generator NovoTTF/Optune. However, it is clear that regardless of the current treatment regimens, glioma patients continue to have dismal prognosis and novel treatments are urgently needed. Here, we describe different approaches of recently developed therapeutic glioma brain cancer vaccines, which stimulate the patient's immune system to recognize tumor-associated antigens (TAA) on cancer cells, aiming to instruct the immune system to eventually attack and destroy the brain tumor cells, with minimal bystander damage to normal brain cells. These distinct immunotherapies may target particular glioma TAAs which are molecularly defined, but they may also target broad patient-derived tumor antigen preparations intentionally evoking a very broad polyclonal antitumor immune stimulation.

Early detection and diagnosis of brain tumors are essential for early intervention and eventually successful treatment plans leading to either a full recovery or an increase in the patient lifespan. However, diagnosis of brain tumors is not an easy task since it requires highly skilled professionals, making this procedure both costly and time-consuming. The diagnosis process relying on MR images gets even harder in the presence of similar objects in terms of their density, size, and shape. No matter how skilled professionals are, their task is still prone to human error. The main aim of this work is to propose a system that can automatically classify and diagnose glioma brain tumors into one of the four tumor types: (1) necrosis, (2) edema, (3) enhancing, and (4) non-enhancing. In this paper, we propose a combined texture discrete wavelet transform (DWT) and statistical features based on the first- and second-order features for the accurate classification and diagnosis of multiclass glioma tumors. Four well-known classifiers, namely, support vector machines (SVM), random forest (RF), multilayer perceptron (MLP), and naïve Bayes (NB), are used for classification. The BraTS 2018 dataset is used for the experiments, and with the combined DWT and statistical features, the RF classifier achieved the highest average accuracy whether for separated modalities or combined modalities. The highest average accuracy of 89.59% and 90.28% for HGG and LGG, respectively, was reported in this paper. It has also been observed that the proposed method outperforms similar existing methods reported in the extant literature. [ABSTRACT FROM AUTHOR]

IntroductionCancer bioenergetics is an essential hallmark of neoplastic transformation. Warburg postulated that mitochondrial OXPHOS is impaired in cancer cells, leading to aerobic glycolysis as the primary metabolic pathway. However, mitochondrial function is altered but not entirely compromised in most malignancies, and that mitochondrial uncoupling is known to increase the carcinogenic potential and modifies treatment response by altering metabolic reprogramming. Our earlier study showed that transient DNP exposure increases glycolysis in human glioma cells (BMG-1). The current study investigated the persistent effect of DNP on the energy metabolism of BMG-1 cells and its influence on tumor progression in glioma xenografts.MethodsBMG-1 cells were treated with 2,4-dinitrophenol (DNP) in-vitro, to establish the OXPHOS-modified (OPM-BMG) cells. Further cellular metabolic characterization was carried out in both in-vitro cellular model and in-vivo tumor xenografts to dissect the role of metabolic adaptation in these cells and compared them with their parental phenotype. Results and DiscussionChronic exposure to DNP in BMG-1 cells resulted in dual-state hyper-energy metabolism with elevated glycolysis++ and OXPHOS++ compared to parental BMG-1 cells with low glycolysis+ and OXPHOS+. Tumor xenograft of OPM-BMG cells showed relatively increased tumor-forming potential and accelerated tumor growth in nude mice. Moreover, compared to BMG-1, OPM-BMG tumor-derived cells also showed enhanced migration and invasion potential. Although mitochondrial uncouplers are proposed as a valuable anti-cancer strategy; however, our findings reveal that prolonged exposure to uncouplers provides tumor growth advantage over the existing glioma phenotype that may lead to poor clinical outcomes.

Diffusion MRI is widely used for the clinical examination of a variety of diseases of the nervous system. However, clinical MRI scanners are mostly capable of magnetic field gradients in the range of 20–80 mT/m and are thus limited in the detection of small tissue structures such as determining axon diameters. The availability of high gradient systems such as the Connectome MRI scanner with gradient strengths up to 300 mT/m enables quantification of the reduction of the apparent diffusion coefficient and thus resolution of a wider range of diffusion coefficients. In addition, biological tissues are heterogenous on many scales and the complexity of tissue microstructure may not be accurately captured by models based on pre-existing assumptions. Thus, it is important to analyze the diffusion distribution without prior assumptions of the underlying diffusion components and their symmetries. In this paper, we outline a framework for analyzing diffusion MRI data with b-values up to 17,800 s/mm2 to obtain a Full Diffusion Tensor Distribution (FDTD) with a wide variety of diffusion tensor structures and without prior assumption of the form of the distribution, and test it on a healthy subject. We then apply this method and use a machine learning method based on K-means classification to identify features in FDTD to visualize and characterize tissue heterogeneity in two subjects with diffuse gliomas.

Aggressive glioma exhibits a poor survival rate. Increased tumor aggression is linked to both tumor cells and tumor-associated macrophages (TAMs), which induce pro-aggression, invasion, and metastasis. Imperatively, for effective treatment, it is important to target both glioma cells and TAMs. Haloperidol, a neuropsychotic drug, avidly targets the sigma receptor (SR), which is expressed in higher levels in both the cell types. Herein, we present the development of a novel cationic lipid-conjugated reduced haloperidol (±RHPC8), which aims to mediate the SR-targeted antiglioma effect. Hypothetically, ±RHPC8 would act simultaneously as an SR-targeting ligand and anticancer agent. As the blood-brain barrier (BBB) obstructs direct targeting of in situ glioma, we used BBB-crossing glucose-based carbon nanospheres (CSPs) to deliver ±RHPC8 within the glioma tumor-bearing mouse brain. The resultant ±RHPC8-CSP nanoconjugate targeted SR-expressing glioma cells. In both orthotopic and subcutaneous mouse tumor models, ±RHPC8-CSP prolonged survival and regressed tumors compared to other treated groups. Notably, ±RHPC8-CSP was significantly taken up by SR-expressing TAMs thus resulting in macrophage polarization from M2 to M1, as exhibited by markedly reduced expression of immunosuppressive cytokines released by TAMs, including TGF-β, IL-10, and VEGF. In conclusion, the designed ±RHPC8-CSP nanoconjugate presented an effective nanodrug delivery system for brain cancer treatment.

Currently, the analysis of magnetic resonance imaging (MRI) brain images of pathological patients is performed manually, both for the recognition of brain structures or lesions and for their characterization. Physicians sometimes encounter difficulties in interpreting these images for a reliable diagnosis of the patient's condition. This is due to the difficulty of detecting the nature of the lesions, particularly glioma. Glioma is one of the most common tumors, and one of the most difficult to detect because of its shape, irregularities, and ambiguous limits. The segmentation of these tumors is one of the most crucial steps for their classification and surgical planning. This article presents a new, accurate, and automatic approach for the precise segmentation of early gliomas (benign tumors), combining the random walk (RW) algorithm and the simple linear iterative clustering algorithm. The study was carried out in four steps. The first step consisted of decomposing the image into superpixels to obtain an initial outline of the tumor. The superpixels were generated using the SLIC algorithm. In the second step, for each superpixel, a set of statistical and multifractal characteristics were calculated (gray‐level co‐occurrence matrix, multifractal detrending moving average). In the third step, the superpixels were classified using a supervised random forest (RF) type classier into healthy or tumorous brain tissue. In the final step, the contour of the detected tumor was enhanced using the customized RW algorithm. The proposed method was evaluated using the Brain Tumor Image Segmentation Challenge 2013 database. The results obtained are competitive compared to other existing methods. [ABSTRACT FROM AUTHOR]

Classification of brain tumor is one of the most vital tasks within medical image processing. Classification of images greatly depends on the features extracted from the image, and thus, feature extraction plays a great role in the correct classification of images. In this paper, an enhanced method is presented for glioma MR images classification using hybrid statistical and wavelet features. In the proposed method, 52 features are extracted using the first-order and second-order statistical features (based on the four MRI modalities: Flair, T1, T1c, and T2) in addition to the discrete wavelet transform producing a total of 152 features. The extracted features are applied to the multilayer perceptron (MLP) classifier. The results using the MLP were compared with various known classifiers. The method was tested on the dataset MICCAI BraTS 2015 which is a standard dataset used for research purposes. The proposed hybrid statistical and wavelet features produced 96.72% accuracy for high-grade glioma and 96.04% accuracy for low-grade glioma, which are relatively better compared to the existing studies.

We have developed six convolutional neural network (CNN) models for finding optimal brain tumor detection system on high‐grade glioma and low‐grade glioma lesions from voluminous magnetic resonance imaging human brain scans. Glioma is the most common form of brain tumor. The models are constructed based on the different combinations and settings of hyperparameters with conventional CNN architecture. The six models are two layers with five epochs, five layers with dropout, five layers with stopping criteria (FLSC), FLSC and dropout (FLSCD), FLSC and batch normalization (FLSCBN), and FLSCBN and dropout. The models were trained and tested with BraTS2013 and whole brain atlas data sets. Among them, FLSCBN model yielded the best classification results for brain tumor detection. Experimental results revealed that our deep learning approach was better than the conventional state‐of‐art methods. [ABSTRACT FROM AUTHOR]

Background: Long noncoding RNAs play influential roles in the progression of many types of human malignancies. The present study aimed to explore the prognostic value of long noncoding RNA FTX (FTX) on patients with glioma. Methods: FTX expression in glioma specimens and matched adjacent non‐neoplasm specimens was examined by a quantitative real‐time polymerase chain reaction assay. Furthermore, assays of the relationships between FTX expression and clinicopathologic characteristics of patients with glioma were also performed. Kaplan–Meier methods were applied for the assays of the overall survival (OS) and progression‐free survival (PFS) of patients and Cox regression assays were used to analyze the clinical value of FTX used as a possible biomarker. Results: FTX levels were significantly up‐regulated in glioma specimens compared to the paired non‐neoplasm specimens (p < 0.01). Furthermore, high FTX expression in neoplasm tissues was dramatically associated with World Health Organization grade (p = 0.001) and Karnofsky Performance Score (p = 0.009). Kaplan–Meier assays with 187 patients revealed that patients with high level of FTX expression displayed poorer OS (p = 0.002) and PFS (p = 0.000). Subsequently, multivariable Cox regression analysis identified FTX expression as an independent prognostic factor of unfavorable survivals in glioma (OS: p = 0.001; PFS: p = 0.002). Conclusions: These findings indicated that FTX may be a novel predictor for prognostic assessment of glioma patients. However, studies conducted with larger numbers of patients are essential to confirm our findings. [ABSTRACT FROM AUTHOR]

Background: The most common primary tumors of the brain are gliomas. Despite improvements in treatment strategy, the prognosis for patients with high-grade gliomas has stayed poor, while it is relatively good for low-grade gliomas. The main and gold standard way for grading glial tumor is biopsy. Accurate assessment of tumor grade is vital for the determination of best treatment plan. The purpose of this study was to evaluate the role of diffusion tensor imaging (as a noninvasive method) using relative anisotropy (RA) in glial tumor grading. Methods: A total of 20 histologically confirmed gliomas patients were scanned using a 1.5-Tesla magnetic resonance scanner. We described two regions of interest (ROIs), white matter adjacent to the tumor and the homologous fiber tracts to first ROI in the contralateral hemisphere. Findings: The relative anisotropy values and ratio of the low-grade gliomas tended to be higher than those of the high-grade gliomas in the peritumoral fibers (P = 0.008, for RAt and P = 0.039 for RAt/n). Conclusion: Our findings prove that the relative anisotropy was different between low- and high-grade gliomas, which may be helpful in grading.

Purpose: The identification of a brain tumor imaged with PET or SPECT is usually performed with visual inspection of an expert medical clinician. However an automated diagnostic of such images hasn't been established or applied. In this study, we explored the possibility of establishing an automated statistical analysis for the diagnosis of glioma by means of IPA‐SPECT data. Methods: On the basis of a dataset of 100 patients that have undergone MRI and IPA‐SPECT acquisition, in this work, we identify an automated workflow. Three different approaches were explored: I. statistical non‐parametric mapping analysis (SnPM), II. statistical non‐parametric analysis with an increased number of permutations due to sign‐flipping function (PALM) and III. statistical parametric analysis (SPM). The automated methods were compared with the visual inspection. Results: The study proved PALM and SPM approaches to have a high diagnostic power. Compared to the parametric methods, the non‐parametric method is the mathematically correct approach for the problem in question. If we take the high resolution structural MRI information into account, the diagnostic power of PALM was not significantly inferior to the visual inspection (P = 0.5150), showing an area under the ROC curve (AUC) smaller only by less than 3%. Conclusions: The automated diagnostic method based on statistical inference, here applied to diagnose glioma tumors in IPA‐SPECT data, seems to be a promising tool that can support the visual investigation in nuclear medicine. Moreover in the foreseeable future, the presented methodology has a big potential in various application like localization of active tumor tissues in surgical resection or stereotactic radiosurgery. [ABSTRACT FROM AUTHOR]

Abstract: Glioma is the severe type of brain tumor which leads to immediate death for the case high‐grade Glioma. The Glioma tumor patient in case of low grade can extend their life period if tumor is timely detected and providing proper surgery. In this article, a computer‐aided fully automated Glioma brain tumor detection and segmentation system is proposed using Adaptive Neuro Fuzzy Inference System (ANFIS) classifier based Graph cut approach. Initially, orientation analysis is performed on the brain image to obtain the edge enhanced abnormal regions in the brain. Then, features are extracted from the orientation enhanced image and these features are trained and classified using ANFIS classifier to classify the test brain image into either normal or abnormal. Normalized Graph cur segmentation methodology is applied on the classified abnormal brain image to segment the tumor region. The proposed Glioma tumor segmentation method is validated using the metric parameters as sensitivity, specificity, accuracy and dice similarity coefficient. [ABSTRACT FROM AUTHOR]

Objective To explore the value of diffusion weighted imaging (DWl) in differential diagnosis of brain metastases and glioma tumors. Methods The MRI data (including conventional unenhanced, enhanced scan and DWI imaging) of 29 cases of brain metastases and 22 cases of glioma tumors were collected and retrospectively analyzed. Results In the brain metastases, lesions of high DWI signal intensity were seen in 10 cases, and lesions of low DWI signal intensity were seen in 19 cases. Lesions of high signal intensity on DWI were seen in 15 cases of gliomas, while obvious high DWI signals were not found in the other 7 cases. The ADC values in the solid area of brain metastases and glioma tumors were (0.86 ± 0.23) x 10-3 mm²/s and (1.07 ±0.14) x 10-3 mm²/s, respectively, with no statistically significant difference (t=-1.863 P = 0.085). The ADC values in the necrotic area were (2.41 ± 0.47) x 10-3 mm²/s and (2.83 ± 0.08) x 10-3mm²/s, respectively, and the difference was statistically significant between the two tumors (t = -3.696 P = 0.001). The ADC values in the proximal peritumoral edema of brain metastases and glioma tumors were (1.58 ±0.18) x 10-3 mm²/s and (1.41 ± 0.16) x 10-3mm²/s, respectively, with statistically significant difference (t= 2.877 P = 0.006). The ADC values in the distal peritumoral edema were (1.59 ±0.08) x 10-3 mm²/s and (1.51 ±0.15) x 10-3mm²/s, respectively, with no statistically significant difference (t = 1.787 P =0.096). Conclusion DWI is an effective method for differential diagnosis of brain metastases and glioma tumors. [ABSTRACT FROM AUTHOR]

Background: Detection of brain tumor is a complicated task, which requires specialized skills and interpretation techniques. Accurate brain tumor classification and segmentation from MR images provide an essential choice for medical treatments. Different objects within an MR image have similar size, shape, and density, which makes the tumor classification and segmentation even more complex., Objective: Classification of the brain MR images into tumorous and non-tumorous using deep features and different classifiers to get higher accuracy., Methods: In this study, a novel four-step process is proposed; pre-processing for image enhancement and compression, feature extraction using convolutional neural networks (CNN), classification using the multilayer perceptron and finally, tumor segmentation using enhanced fuzzy cmeans method., Results: The system is tested on 65 cases in four modalities consisting of 40,300 MR Images obtained from the BRATS-2015 dataset. These include images of 26 Low-Grade Glioma (LGG) tumor cases and 39 High-Grade Glioma (HGG) tumor cases. The proposed CNN feature-based classification technique outperforms the existing methods by achieving an average accuracy of 98.77% and a noticeable improvement in the segmentation results are measured., Conclusion: The proposed method for brain MR image classification to detect Glioma Tumor detection can be adopted as it gives better results with high accuracies., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Objective: Despite advances in surgical and adjuvant radiation therapy and chemotherapy strategies, malignant gliomas continue to be associated with poor prognosis in patients. To address suitable strategies, scientists pay close attention to factors that cause angiogenesis. Angiogenesis is a possible target in the treatment of human gliomas. Genistein has been previously introduced as an anti-angiogenic agent for different cancers. To evaluate the effect of Genistein on the angiogenic cascade, we investigated the expression of hepatocyte growth factor (HGF) transcripts in mesenchymal stem cell like cells from high grade and low grade brain tumors before and after treatment with Genistein. Materials and Methods: Brain tumor obtained from patient diagnoses with GBM and low grade glioma in sterile condition, wash with PBS, cut in to small pieces, digested with collagenase type I and culture in tissue culture flask contain MDEM 10% FBS. As cells arise to passage 3, they were treated with Genistein and then the HGF gene expression was determined by QRT-PCR in treated and untreated cells. Results: Genistein has a significant effect on both types of tumors after cells expose to compound compared to untreated cells. According to the results, Genistein has a remarkable effect on downregulating HGF transcripts where it showed 13×106- and 2-fold lower expression than untreated cells in high grade and low grade glioma cells, respectively. Conclusion: Our research showed that Genistein has a significant effect on downregulation of HGF. Consequently, Genistein have some important advantages for treatment of invasive brain tumors, however more investment cannot be ignore to make a suitable decision about effect of this compound on brain tumors [ABSTRACT FROM AUTHOR]