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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]

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]

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]

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]