Automated Brain Tumor Diagnosis Using Deep Residual U-Net Segmentation Model

Automated segmentation and classification of biomedical images act as a vital part of the diagnosis of brain tumors(BT).A primary tumor brain analysis suggests a quicker response from treatment that utilizes for improving patient survival rate.The location and classification of BTs from huge medicinal images database,obtained from routine medical tasks with manual processes are a higher cost together in effort and time.An automatic recognition,place,and classifier process was desired and useful.This study introduces anAutomatedDeepResidualU-Net Segmentation with Classification model(ADRU-SCM)for Brain Tumor Diagnosis.The presentedADRUSCM model majorly focuses on the segmentation and classification of BT.To accomplish this,the presented ADRU-SCM model involves wiener filtering(WF)based preprocessing to eradicate the noise that exists in it.In addition,the ADRU-SCM model follows deep residual U-Net segmentation model to determine the affected brain regions.Moreover,VGG-19 model is exploited as a feature extractor.Finally,tunicate swarm optimization(TSO)with gated recurrent unit(GRU)model is applied as a classification model and the TSO algorithm effectually tunes theGRUhyperparameters.The performance validation of the ADRU-SCM model was tested utilizing FigShare dataset and the outcomes pointed out the better performance of the ADRU-SCM approach on recent approaches.

Computer-Aided Diagnosis Model Using Machine Learning for Brain Tumor Detection and Classification

The Brain Tumor(BT)is created by an uncontrollable rise of anomalous cells in brain tissue,and it consists of 2 types of cancers they are malignant and benign tumors.The benevolent BT does not affect the neighbouring healthy and normal tissue;however,the malignant could affect the adjacent brain tissues,which results in death.Initial recognition of BT is highly significant to protecting the patient’s life.Generally,the BT can be identified through the magnetic resonance imaging(MRI)scanning technique.But the radiotherapists are not offering effective tumor segmentation in MRI images because of the position and unequal shape of the tumor in the brain.Recently,ML has prevailed against standard image processing techniques.Several studies denote the superiority of machine learning(ML)techniques over standard techniques.Therefore,this study develops novel brain tumor detection and classification model using met heuristic optimization with machine learning(BTDC-MOML)model.To accomplish the detection of brain tumor effectively,a Computer-Aided Design(CAD)model using Machine Learning(ML)technique is proposed in this research manuscript.Initially,the input image pre-processing is performed using Gaborfiltering(GF)based noise removal,contrast enhancement,and skull stripping.Next,mayfly optimization with the Kapur’s thresholding based segmentation process takes place.For feature extraction proposes,local diagonal extreme patterns(LDEP)are exploited.At last,the Extreme Gradient Boosting(XGBoost)model can be used for the BT classification process.The accuracy analysis is performed in terms of Learning accuracy,and the validation accuracy is performed to determine the efficiency of the proposed research work.The experimental validation of the proposed model demonstrates its promising performance over other existing methods.

A Semi-automatic method for segmentation and 3D modeling of glioma tumors from brain MRI

This work presents an efficient method for volume rendering of glioma tumors from segmented 2D MRI Datasets with user interactive control, by replacing manual segmentation required in the state of art methods. The most common primary brain tumors are gliomas, evolving from the cerebral supportive cells. For clinical follow-up, the evaluation of the preoperative tumor volume is essential. Tumor portions were automatically segmented from 2D MR images using morphological filtering techniques. These segmented tumor slices were propagated and modeled with the software package. The 3D modeled tumor consists of gray level values of the original image with exact tumor boundary. Axial slices of FLAIR and T2 weighted images were used for extracting tumors. Volumetric assessment of tumor volume with manual segmentation of its outlines is a time-consuming process and is prone to error. These defects are overcome in this method. Authors verified the performance of our method on several sets of MRI scans. The 3D modeling was also done using segmented 2D slices with the help of medical software package called 3D DOCTOR for verification purposes. The results were validated with the ground truth models by the Radiologist.

Diagnosis and treatment of mixed glioma

The authors present 83 patients with mixed glioma with experiences in clinical diagnosis and treatment.In all these cases.there were 44 tumors as grade 1 or 2,and 39 as grade 3 or 4.In 39 tumors.two glial components(oligodendrocytes and astrocytes) occurr

Experimental study on dendritic cells pulsed with brain tumor stem cells for immunotherapy of glioma

Objective To investigate the effect of dendritic cells pulsed with brain tumor stem cells which are used to treat on intracranial glioma. Methods We obtained murine brain tumor stem cells by grow ing C6 cells in epidermal grow th factor/basic fibroblast grow th factor w ithout serum.Dendritic cells isolated from rat bone marrow w ere pulsed w ith BTSCs. Rat brain

An Efficient Image Analysis Framework for the Classification of Glioma Brain Images Using CNN Approach

The identification of brain tumors is multifarious work for the separation of the similar intensity pixels from their surrounding neighbours.The detection of tumors is performed with the help of automatic computing technique as presented in the proposed work.The non-active cells in brain region are known to be benign and they will never cause the death of the patient.These non-active cells follow a uniform pattern in brain and have lower density than the surrounding pixels.The Magnetic Resonance(MR)image contrast is improved by the cost map construction technique.The deep learning algorithm for differentiating the normal brain MRI images from glioma cases is implemented in the proposed method.This technique permits to extract the linear features from the brain MR image and glioma tumors are detected based on these extracted features.Using k-mean clustering algorithm the tumor regions in glioma are classified.The proposed algorithm provides high sensitivity,specificity and tumor segmentation accuracy.

Application of Intraoperative Contrast-Enhanced Ultrasound in the Resection of Brain Tumors

Objective:To investigate the value of routine intraoperative ultrasound(IU)and intraoperative contrast-enhanced ultrasound(ICEUS)in the surgical treatment of brain tumors,and to explore the utilization of ICEUS for the removal of the remnants surrounding the resection cavity.Methods:In total,51 patients who underwent operations from 2012 to 2018 due to different tumors in the brain were included in this study.The clinical data were evaluated retrospectively.IU was performed in all patients,among which 28 patients underwent ICEUS.The effects of IU and ICEUS on tumor resection and recurrence were evaluated.

Psychiatric aspects of brain tumors:A review

Infrequently, psychiatric symptoms may be the only manifestation of brain tumors. They may present with mood symptoms, psychosis, memory problems, personality changes, anxiety, or anorexia. Symptoms may be misleading, complicating the clinical picture. A comprehensive review of the literature was conducted regarding reports of brain tumors and psychiatric symptoms from 1956-2014. Search engines used include Pub Med, Ovid, Psych Info, MEDLINE, and Med Scape. Search terms included psychiatric manifestations/symptoms, brain tumors/neoplasms. Our literature search yielded case reports, case studies, and case series. There are no double blind studies except for post-diagnosis/-surgery studies. Early diagnosis is critical for improved quality of life. Symptoms that suggest work-up with neuroimaging include: new-onset psychosis, mood/memory symptoms, occurrence of new or atypical symptoms, personality changes, and anorexia without body dysmorphic symptoms. This article reviews the existing literature regarding the diagnosis and management of this clinically complex condition.

Brain tumors:Cancer stem-like cells interact with tumor microenvironment

Cancer stem-like cells(CSCs)with potential of self-renewal drive tumorigenesis.Brain tumor microenvironment(TME)has been identified as a critical regulator of malignancy progression.Many researchers are searching new ways to characterize tumors with the goal of predicting how they respond to treatment.Here,we describe the striking parallels between normal stem cells and CSCs.We review the microenvironmental aspects of brain tumors,in particular composition and vital roles of immune cells infiltrating glioma and medulloblastoma.By highlighting that CSCs cooperate with TME via various cellular communication approaches,we discuss the recent advances in therapeutic strategies targeting the components of TME.Identification of the complex and interconnected factors can facilitate the development of promising treatments for these deadly malignancies.

EXPRESSION OF IL-13Ra2 GENE IN HUMAN BRAIN TUMORS

Objective： To investigate the expression of IL-13Ra2 gene in brain tumors. Methods： Seventy-nine human brain tumors were obtained from the department of Neurosurgery of China Medical University. Human IL-13Ra2 expression was evaluated by reverse transcriptase polymerase chain reaction and immunohistochemical analysis. Results： IL-13Ra2 gene was highly expressed in glioblastoma, medulloblastoma, malignant meningioma and benign meningioma. Conclusion： Human IL-13Ra2 gene is expressed in brain tumors in addition to gliomas, and our result indicates that the IL-13Ra2 gene promoter based gene therapy method can be used to treat brain tumors in addition to gliomas. Further studies involving larger numbers of samples are necessary to fully understand the expression profile of IL-13Ra2 gene in the brain tumors.

DIFFERENTIAL DIAGNOSIS IN THE INTRACRANIAL DISEASES: PROTON MAGNETIC RESONANCE S PECTROSCOPY STUDY

Objective To investigate th e value of proton magnetic resonance spectroscopy ( 1H-MRS) on diagnosis a nd differential diagnosis of the intracranial diseases by the MRS results of 52 patients. Methods 12 patients with benign glioma, 16 patients with malignant glioma, 10 patients with meningioma, 8 patients with virus encephalitis, and 6 patients with cerebral infarction underwent MRS in th e lesion region. We measured the area within the spectra of N-acetyl-aspartate (NAA), creatine/phosphocreatine (Cr), choline compounds (Cho), and lactate (Lac ). Results The spectra of meningiomas were characterized by abs ence of NAA. The spectra of gliomas were characterized by the decrease of NAA an d Cr, but the increase of Cho. The ratio of Cho to Cr was 2.25±1.21 in benign g liomas, while the ratio of Cho to Cr was 4.65±2.21 in malignant gliomas. The sp ectra of virus encephalitis appeared the decrease of NAA and the normality of Cr , with the 1.25±0.21 ratio of Cho/Cr. The apparent Lac wave could be seen in al l cerebral infarctions. Conclusion The value of 1H-MRS plays a significant role in the diagnosis and differential diagnosis of gliomas, mening iomas, virus encephalitis, and cerebral infarctions.

Classification of Brain Tumors Using Hybrid Feature Extraction Based on Modified Deep Learning Techniques

According to the World Health Organization(WHO),Brain Tumors(BrT)have a high rate of mortality across the world.The mortality rate,however,decreases with early diagnosis.Brain images,Computed Tomography(CT)scans,Magnetic Resonance Imaging scans(MRIs),segmentation,analysis,and evaluation make up the critical tools and steps used to diagnose brain cancer in its early stages.For physicians,diagnosis can be challenging and time-consuming,especially for those with little expertise.As technology advances,Artificial Intelligence(AI)has been used in various domains as a diagnostic tool and offers promising outcomes.Deep-learning techniques are especially useful and have achieved exquisite results.This study proposes a new Computer-Aided Diagnosis(CAD)system to recognize and distinguish between tumors and non-tumor tissues using a newly developed middleware to integrate two deep-learning technologies to segment brain MRI scans and classify any discovered tumors.The segmentation mechanism is used to determine the shape,area,diameter,and outline of any tumors,while the classification mechanism categorizes the type of cancer as slow-growing or aggressive.The main goal is to diagnose tumors early and to support the work of physicians.The proposed system integrates a Convolutional Neural Network(CNN),VGG-19,and Long Short-Term Memory Networks(LSTMs).A middleware framework is developed to perform the integration process and allow the system to collect the required data for the classification of tumors.Numerous experiments have been conducted on different five datasets to evaluate the presented system.These experiments reveal that the system achieves 97.98%average accuracy when the segmentation and classification functions were utilized,demonstrating that the proposed system is a powerful and valuable method to diagnose BrT early using MRI images.In addition,the system can be deployed in medical facilities to support and assist physicians to provide an early diagnosis to save patients’lives and avoid the high cost of treatments.

Mobile Phone Usage and the Risk of Brain Tumors

Purpose: This literature review investigated the possible association between the use of mobile phones and brain tumors. Methods: In brief, 11 publications were retrieved from JSTOR, PubMed, Google Scholar and Summon in order to compare the association between the usage of mobile phones in patients with a brain tumor and those without. Papers published in English, and after 2001 were selected for. There was no limit on age, gender, geographical location and type of brain tumor. Results: For regular mobile phone usage, the combined odds ratios (OR) (95% confidence intervals) for three studies are: 1.5 (1.2 - 1.8), 1.3 (0.95 - 1.9), and 1.1 (0.8 - 1.4), respectively. Furthermore, the odds ratio did not increase, regardless of mobile phone use duration. Additionally, Lonn et al. (2005) observed that the risk also did not significantly increase when assessing the laterality (ipsilateral or contralateral) of the tumor in relation to side of head used for the mobile phone. Kan et al. (2007) observed an OR of 1.22 when comparing analog phone to digital phone use. Conclusion: This review concludes that there is no current association between mobile phone use and the development of brain tumors. Although certain studies speak in favor of an increased risk, many are plagued with either: sampling bias, misclassification bias, or issues concerning risk estimates. Further research needs to be done in order to evaluate the long-term effect of mobile phone usage on the risk of developing a brain tumor.

The exciting potential of nanotherapy in brain-tumor targeted drug delivery approaches

Delivering therapeutics to the central nervous system（CNS） and brain-tumor has been a major challenge. The current standard treatment approaches for the brain-tumor comprise of surgical resection followed by immunotherapy, radiotherapy, and chemotherapy. However, the current treatments are limited in providing significant benefits to the patients and despite recent technological advancements; brain-tumor is still challenging to treat. Brain-tumor therapy is limited by the lack of effective and targeted strategies to deliver chemotherapeutic agents across the blood-brain barrier（BBB）. The BBB is the main obstacle that must be overcome to allow compounds to reach their targets in the brain. Recent advances have boosted the nanotherapeutic approaches in providing an attractive strategy in improving the drug delivery across the BBB and into the CNS. Compared to conventional formulations, nanoformulations offer significant ad vantages in CNS drug delivery approaches. Considering the above facts, in this review, the physiological/anatomical features of the brain-tumor and the BBB are briefly discussed. The drug transport mechanisms at the BBB are outlined. The approaches to deliver chemotherapeutic drugs across the CNS into the brain-tumor using nanocarriers are summarized. In addition, the challenges that need to be addressed in nanotherapeutic approaches for their enhanced clinical application in brain-tumor therapy are discussed.

Effects of Photodynamic Therapy on the Ultrastructure of Glioma Cells

To study the change in ultrastructure of C6 glioma cells after photodynamic therapy （PDT）, to compare morphological differences in necrosis and apoptosis before and after PDT treatment, and to evaluate the effect of photodynamic therapy on the blood brain tumor barrier （BTB） of C6 glioma. Methods The model was produced by transplanting C6 glioma cells cultured in vitro using Peterson method into the caudate nuclei of Wister rats. The experiment group received PDT for two weeks after the operation. The sub-cellular structure, blood-brain-barrier （BBB） and BTB in both groups were observed under electron microscope. Results Apoptosis in different phases and necrosis could be observed in some C6 glioma cells. Swelling occurred on the ultrastructure of cellular organs such as mitochondria and endoplasmic reticulum in most of the cells. Damage to the BTB, reduction of the number of cellular organs in endothelial cells of the capillary blood vessels, stretch of the tight junction, and enlargement of the gaps between endothelial cells were also seen in the experiment group. Meanwhile, limited impact on the normal sub-ceUular structures and BBB was observed. Conclusion PDT could induce apoptosis and necrosis of C6 glioma cells due to the damage to the ultrastructure of mitochondria and endoplasmic reticulum. The weakened function of C6 glioma BTB initiated by PDT makes it possible to perform a combined therapy of PDT and chemotherapy for glioma.

Apoptosis in glioma-bearing rats after neural stem cell transplantation

Abnormal activation of the Ras/Raf/Mek/Erk signaling cascade plays an important role in glioma. Inhibition of this aberrant activity could effectively hinder glioma cell proliferation and promote cell apoptosis. To investigate the mechanism of gJioblastoma treatment by neural stem ceiJ trans- plantation with respect to the Ras/Raf/Mek/Erk pathway, C6 glioma cells were prepared in sus- pension and then infused into the rat brain to establish a glioblastoma model. Neural stem cells isolated from fetal rats were then injected into the brain of this glioblastoma model. Results showed that Raf-1, Erk and Bcl-2 protein expression significantly increased, while Caspase-3 protein expression decreased. After transplantation of neural stem cells, Raf-1, Erk and Bcl-2 protein expression significantly decreased, while Caspase-3 protein expression significantly in-creased. Our findings indicate that transplantation of neural stem cells may promote apoptosis of glioma cells by inhibiting Ras/Raf/Mek/Erk signaling, and thus may represent a novel treatment approach for glioblastoma.

Magnetic resonance imaging characteristics of postoperative intracranial dissemination of recurrent gliomas

Ten intracranial gliomas cases, that had postoperative intracranial dissemination, underwent magnetic resonance imaging （MRI） examinations, including T1 weighted imaging, fat-suppressed T1 weighted imaging, T2 weighted imaging and fluid attenuated inversion recovery （FLAIR）. Results showed that tumor metastasis had occurred via the cerebrospinal fluid, the brain white matter fibers and the surgical access site alone. On the plain MRI scans, 1/7 cases were linearly thickened with isointensity and 5/7 cases exhibited nodular foci on T1 weighted imaging; the cerebral sulci and cisterns in 2/7 cases had become shallow and five cases had nodular foci on T2 weighted imaging. FLAIR imaging revealed that the cerebral sulci and cisterns in 2/7 cases had become shallow and that six cases had affected nodular foci. The contrast-enhanced MRI scans revealed linear thickening in seven cases, nodules in seven cases, similarities to ＂mould-like＂ signs in six cases and hydrocephalus in six cases. These findings suggested that MRI with different sequences can diagnose glioma metastasis.

Health-related quality of life in high-grade glioma patients

Gliomas are malignant primary brain tumors and yet incurable. Palliation and the maintenance or improvement of the patient's quality of life is therefore of main importance. For that reason, health-related quality of life(HRQoL) has become an important outcome measure in clinical trials, next to traditional outcome measures such as overall and progression-free survivals, and radiological response to treatment. HRQoL is a multidimensional concept covering physical, psychological, and social domains, as well as symptoms induced by the disease and its treatment. HRQoL is assessed by using self-reported, validated questionnaires. Various generic HRQoL questionnaires, which can be supplemented with a brain tumor- specific module, are available. Both the tumor and its treatment can have a negative effect on HRQoL. However, treatment with surgery, radiotherapy, chemotherapy, and supportive treatment may also improve patients' HRQoL, in addition to extending survival. It is expected that the impact of HRQoL measurements in both clinical trials and clinical practice will increase. Hence, it is important that HRQoL data are collected, analyzed, and interpreted correctly. Methodological issues such as selection bias and missing data may hamper the interpretation of HRQoL data and should therefore be accounted. In clinical trials, HRQoL can be used to assess the benefits of a new treatment strategy, which should be weighed carefully against the adverse effects of that treatment. In daily clinical practice, HRQoL assessments of an individual patient can be used to inform physicians about the impact of a specific treatment strategy, and it may facilitate the communication between the physicians and the patients.

IL-13Ra2-and glioma stem cell-pulsed dendritic cells induce glioma cell death in vitro

Objective Gliomas are the most common malignant tumors in the central nervous system.Despite multiple therapies including surgery,chemotherapy,and radiotherapy,the prognosis of patients remains poor.Immunotherapy is an alternative method of treating glioma,and the use of dendritic cell vaccines is one of the promising treatment options.However,there is no specific tumor cell antigen that can trigger dendritic cells(DCs).IL-13Ra2 is a specific antigen expressed in glioma cells;in the current study,we have attempted to explore whether IL-13Ra2 could be the antigen that triggers DCs and to envisage its application as potential therapy for glioma.Methods The expression of IL-13Ra2 was detected in U251 glioma cell lines and primary glioma tissues using different methods.DCs from human blood were isolated and pulsed with recombinant IL-13Ra2,following which the cytotoxicity of these DCs on glioma cells was detected and analyzed.Results About 55.9% human glioma tissue cells expressed IL-13Ra2,while normal brain tissue cells did not show any expression.DC vaccines loaded with IL-13Ra2,glioma cell antigen,and brain tumor stem cell(BTSC) antigen could significantly stimulate the proliferation of T lymphocytes and induce cell death in the glioma tissue.Compared to other groups,DC vaccines loaded with BTSC antigen showed the strongest ability to activate cytotoxic T lymphocytes(CTLs),while the glioma cell antigen group showed no significant difference.Conclusion IL-13Ra2,which is expressed in gliomas and by glioma stem cells,as well as IL-13Ra2 could prove to be potential antigens for DC vaccine-based immunotherapy.

CXCR4-Expressing Glial Precursor Cells Demonstrate Enhanced Migratory Tropism for Glioma

Malignant glioma remains one of the most intractable of human cancers principally due to the highly infiltrative nature of these neoplasms. The use of neural precursor cells (NPC) has received considerable attention based on their ability to selectively migrate towards disseminated areas of tumor in vivo and their described ability to deliver tumor-directed therapies specifically to infiltrating tumor cells. Fundamental to optimizing the use of these cells for potential clinical translation is the development of an understanding regarding the biologic cues that govern their ability to migrate towards infiltrative glioma foci. To this end, in this paper we detail that NPC selected for double-expression of the glial-precursor marker A2B5 and the cell-surface chemokine receptor, CXCR4, demonstrate enhanced in vitro gliomadirected tropism. These findings demonstrate the relevance of these markers for the phenotypic segregation of an optimally tumor-tropic NPC sub-population as a means of enhancing NPC-based therapeutic strategies for the treatment of glioma.