Pediatric Brain Tumor

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The Neuroimaging Center of the Pediatric Brain Tumor Consortium–Collaborative Neuroimaging in Pediatric Brain Tumor Research: A Work in Progress

The Neuroimaging Center of the Pediatric Brain Tumor Consortium–Collaborative Neuroimaging in Pediatric Brain Tumor Research: A Work in Progress

SUMMARY: As an essential part of the National Cancer Institute (NCI)-funded Pediatric Brain Tumor Consortium (PBTC), the Neuroimaging Center (NIC) is dedicated to infusing the study of pediatric brain tumors with imaging “best practice” by producing a correlative research plan that 1) resonates with novel therapeutic interventions being developed by the wider PBTC, 2) ensures that every PBTC protocol incorporates an imaging “end point” among its objectives, 3) promotes the widespread implementation of standardized technical protocols for neuroimaging, and 4) facilitates a quality assurance program that complies with the highest standards for image data transfer, diagnostic image quality, and data integrity. To accomplish these specific objectives, the NIC works with the various PBTC sites (10 in all, plus NCI/ National Institute of Neurological Diseases and Stroke representation) to ensure that the overarching mission of the consortium—to better understand tumor biology and develop new therapies for central nervous system tumors in children—is furthered by creating a uniform body of imaging techniques, technical protocols, and standards. Since the inception of the NIC in 2003, this broader mandate has been largely accomplished through a series of site visits and meetings aimed at assessing prevailing neuroimaging practices against NIC-recommended protocols, techniques, and strategies for achieving superior image quality and executing the secure transfer of data to the central PBTC. These ongoing evaluations periodically examine investigations into targeted drug therapies. In the future, the NIC will concentrate its efforts on improving image analysis for MR imaging and positron-emission tomography (PET) and on developing new ligands for PET; imaging markers for radiation therapy; and novel systemic, intrathecal, and intralesional therapeutic interventions.

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MethPed: a DNA methylation classifier tool for the identification of pediatric brain tumor subtypes

MethPed: a DNA methylation classifier tool for the identification of pediatric brain tumor subtypes

CNS-PNET is an embryonal neoplasm with medullo- blastoma-like histology; the current WHO criterion does not distinguish CNS-PNETs in the form of medulloblas- toma in the cerebellum or in the form of a supratentorial PNET. However, recent studies have shown that histologi- cally defined CNS-PNETs display heterogeneous methyla- tion profiles and show relationships to other pediatric brain tumor types [12]. Thus, a high frequency of PNETs might be misdiagnoses of other tumor forms, and new criteria for diagnosing true CNS-PNET tumors are there- fore needed, which is why we did not include the current PNET diagnosis group in MethPed. To illustrate the het- erogeneous profiles of PNETs, we ran a set of 28 CNS- PNET tumors through MethPed. Many of the samples could be accurately classified into one of the nine diagno- ses/subgroups in MethPed, whereas some could not confi- dently be classified into either of these, suggesting that they are true PNETs or alternatively other rare tumors. Pediatric GBMs have been reported to have a distinctive molecular pathogenesis with high frequency of H3F3A mutations; thus the histone mutations present in the two regional PNET cases classified as GBM by MethPed sup- port our results [4, 13]. We next re-examined the histo- pathological material from these cases and found focal areas of differentiated cells indicative of GBM. High-grade gliomas such as GBM typically arise from astrocytic

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Common genetic variations in cell cycle and DNA repair pathways associated with pediatric brain tumor susceptibility

Common genetic variations in cell cycle and DNA repair pathways associated with pediatric brain tumor susceptibility

Brain tumors are the most common pediatric solid tumors and the leading cause of cancer mortality in children. High-dose ionizing radiation and rare inherited syndromes are the only established risk factors for brain tumors, causing a small proportion of cases [1]. Thus, brain tumors are considered as multifactorial disorders resulting from progressive accumulation of genetic and epigenetic alterations in concert with environmental exposures. It has been suggested that genetic polymorphisms in four main pathways including DNA repair, cell cycle, metabolism, and inflammation play an important role in brain carcinogenesis [2]. Therefore, candidate gene- association studies of adult brain tumors have mainly focused on these four hypothesized pathways to identify genetic susceptibility factors for adult brain tumors [3–9]. Whereas a considerable number of candidate gene-association studies are available on adult brain tumors, very few and small genetic studies have been performed on brain tumors in children and adolescents [10–13], due to difficulties in collecting a sufficient number of DNA samples. Hence, the role of genetic polymorphisms in pediatric brain tumor (PBT) etiology is largely unknown. However, some studies have found similar genetic mutation patterns for adult and pediatric brain tumor progression within specific histological types [14–18]. These similarities in prognostic factors provide an important starting point for identifying candidate genetic risk factors for PBTs; that might be similar to those involved in adult brain tumorigenesis [19]. Moreover, in our previous genetic association study of PBT risk, we concluded that single nucleotide polymorphisms (SNPs) identified by genome-wide association studies (GWAS) on adult glioma might also be associated with the risk of brain tumors in children [20].

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Advantages of an ICF Based Approach in School Reintegration of Pediatric Brain Tumor Patients: The School Participation Scales (S PS 24/7)

Advantages of an ICF Based Approach in School Reintegration of Pediatric Brain Tumor Patients: The School Participation Scales (S PS 24/7)

Delving deeper into this issue, we have found that the International Classification of Functioning, Disability and Health (ICF; WHO) [20] provided a special frame- work for neuropsychological rehabilitation in a broader sense. According to the ICF, an individual’s functioning does not only depend on certain body functions, but also on the possibilities to actively participate in everyday life. The advantage of the ICF is that it includes personal as well as environmental factors. Unfortunately, so far very few attempts have been made to use the ICF framework for pediatric brain tumor patients, especially when it came to a neuropsychological perspective and school integration. Some studies (e.g. [21]) used the ICF to fo- cus on social relationships, others to discuss participation of disabled children in general (e.g. [22]). Furthermore, the existing approaches mainly try to link widely used assessments to the ICF without using the ICF itself as a method for gathering knowledge about the individual patient [23,24]. In these attempts, the ICF often serves as checklist or as a mapping of disabilities.

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Original Article Embryonal tumor with multilayered rosettes, C19MC-altered (ETMR): a newly defined pediatric brain tumor

Original Article Embryonal tumor with multilayered rosettes, C19MC-altered (ETMR): a newly defined pediatric brain tumor

Histologically, the tumor consisted of spindle shaped cells arranged in clusters tending to form a typical papillary or trabecular structure (Figure 3A, 3B). Occasionally, deeply stained nuclei and mitotic figures can be found. Abundant blood vessels, necrosis and calcifica- tion were observed (Figure 3A, 3B). Immu- nohistochemically, the tumor cells were posi- tive for CD99 (Figure 3C), EMA (Figure 3E), Syn (Figure 3F) and LIN28A (Figure 3I). Vimentin was strongly expressed (Figure 3G), but CK expression was absent (Figure 3D). The Ki-67 labeling index was approximately 60% (Figure 3H). The FISH analysis showed amplification at the 19q13.42 locus, with an amplification index (Green/Red) of 1.55 (Figure 5A).

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Tackling the Challenges in Pediatric Brain Tumor Classification with Deep Learning.

Tackling the Challenges in Pediatric Brain Tumor Classification with Deep Learning.

Artificial Intelligence (AI) is one of the emerging fields of Computer Science. This can be seen in Figure 1.1, that shows the increasing interest overtime on Google search queries for the term "Artificial Intelligence." Deep Learning is a field of AI and machine learning that deals with learning algorithms inspired by the working of a human brain. These learning algorithms are called artificial neural networks and are trained using the back-propagation algorithm. Convolutional Neural Networks are designed to work well with images. It has been shown that convolutional neural networks outperform many traditional feature-based machine learning algorithms for problems where the inputs are images (Alom et al. 2018). Convolutional neural networks were first introduced in 1998 (LeCun et al. 2001) and have been around for almost two decades now. However, only with the recent advances in GPU hardware and the introduction of the ReLU activation function (Krizhevsky et al. 2012), deep learning has started to take off. This can be seen in figure 1.2 that shows a spike in interest on Google search queries for the term "Deep Learning."

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A Survey: Brain Tumor Detection Techniques from Various Clinical Images

A Survey: Brain Tumor Detection Techniques from Various Clinical Images

The brain tumor detection is the key to diagnosis. One of the methods of diagnosis is biopsy. A biopsy may be done by either of the two methods surgery or sterotactic biopsy. Surgery, craniotomy, in which all or part of the brain tumor is removed. Sterotactic, needle biopsy may be used if surgery is suspected to be too risky or difficult. Another diagnosis approach would be lumbar puncture in which a small amount of cerebrospinal fluid is removed using a needle and tested for abnormalities. Neurologic examination is another medical method in which the vision, hearing, alertness, coordination, and reflexes are tested to detect the brain tumor [7].

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Temporal profile of (space occupying lesion of brain)Headache.

Temporal profile of (space occupying lesion of brain)Headache.

Although headache is commonly seen with brain tumour, it is rarely seen as an isolated symptom. In most cases, it is associated with other neurologic symptoms such as seizures or focal weakness during the course if not at the onset. Among adults, only 2 to 16 percent of patients with brain tumour present with headache alone 9,12,19,28 . Isolated headache of more than 10 weeks duration is seldom caused by brain tumour 19 . Tumour-related headaches tend to be worse at night and may awaken the patient. This nocturnal pattern is thought to be due in part to transient increases in PCO2, a potent vasodilator, during sleep. Other possible physiologic explanations include recumbency and decreased cerebral venous return. The classic "brain tumour triad," comprising nocturnal or early morning occurrence, nausea/vomiting, and severe nature, has not been borne out as a typical pattern in modern studies.

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Early decompressive craniectomy and duraplasty for refractory intracranial hypertension in children: results of a pilot study

Early decompressive craniectomy and duraplasty for refractory intracranial hypertension in children: results of a pilot study

Patients We report on the clinical course of six children treated with decompressive craniectomy after TBI at a pediatric intensive care unit. The standard protocol of intensive care treatment included continuous intracranial pressure (ICP) monitoring, sedation and muscle relaxation, normothermia, mild hyperventilation and catecholamines to maintain an adequate cerebral perfusion pressure. Decompressive craniectomy including dura opening was initiated in cases of a sustained increase in ICP > 20 mmHg for > 30 min despite maximally intensified conservative therapy (optimized sedation and ventilation, barbiturates or mannitol).

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Segmentation and Classification of MR Images by DWT and RBF

Segmentation and Classification of MR Images by DWT and RBF

Abstract---Diagnosing brain tumor is a crucial task. Brain which can be affected by number of problems like glioma the most frequent primary brain tumor in adults originating from glial cells, these could cause changes in brains normal structure and its normal behavior. Fusing several methods together based on their hierarchy, a powerful computational tool has been recently developed which is segmenting and classifying brain tumor based on neural networks. This method produces an efficient and automatic way of detecting the suspicious region/tumor in the central nervous system.

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Immunotherapy for pediatric brain tumors

Immunotherapy for pediatric brain tumors

modalities can be immunomodulatory and useful for shifting the immune balance toward anti-tumor im- munity. Advanced surgical practice, radiation, and chemotherapy, including novel, targeted agents, remain important tools for treating our pediatric patients. It is important to point out that the most impactful treat- ment for brain tumors in the last decade is probably not an immunotherapy; BRAF and MEK inhibitors tar- geting the MAP kinase pathway, which is constitutively overactive in pilocytic astrocytoma and a fraction of other glial tumors, are radically changing how these diseases are treated and improving outcomes [118] . Taken

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BRAIN TUMOR SEGMENTATION

BRAIN TUMOR SEGMENTATION

Based on the literature survey, we found that the existing methods developed by different researchers concentrate on the detection of the brain tumor using various segmentation techniques like water shed algorithm, threshold segmentation, etc. We need to consider one important criteria i.e., illumination, while detecting the tumor. In order to avoid the wrong prediction of the tumor location in brain images, which was caused due to illumination, Here, we used the preprocessing techniques Local Binary Pattern(LBP). Even though, several methods proposed, but, less no of literature work found in the preprocessing step. So, here, we made an attempt for eliminating the illumination on brain images. The effect of illumination gives an impact for tumor in brain tumor segmentation.

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APPLICATION OF WAVELET TRANSFORM FOR THE DETECTION OF  BRAIN TUMOR FROM MAGNETIC RESONANCE IMAGES

APPLICATION OF WAVELET TRANSFORM FOR THE DETECTION OF BRAIN TUMOR FROM MAGNETIC RESONANCE IMAGES

The proposed algorithm was developed for segmentation of tumors from MR images. The brain tumor segmentation and detection was done using Magnetic Resonance images. The method used T1 and T2 weighted MR images. This method enhanced the MR images and segment tumor using wavelet transform. This used undecimated wavelet transform for enhancing MR images. Gabor wavelet was applied to an image and captured characteristic of tumor at 1st level of decomposition which segments tumor precisely. Working on area parameter, brain tumor was detected with area value in pixel, location from x and y axis. Table 3.1 and 3.2 contains the different parameters of brain tumor. If tumor is not present inside brain, algorithm does not find any parameters. Though tumor size and location inside skull are different, tumor is detected very precisely. As diagnosis tumor is a complicated and sensitive task; therefore, accuracy and reliability are always assigned much importance. The proposed algorithm provides accuracy, precision and reliability.

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Brain Tumor Detection and Identification Using Histogram Method

Brain Tumor Detection and Identification Using Histogram Method

A human brain is centre of the nervous system; it is a collection of white mass of cells. A tumor of brain is collection of uncontrolled increasing of these cells abnormally found in different part of the brain namely Glial cells, neurons, lymphatic tissues, blood vessels, pituitary glands and other part of brain which lead to the cancer. Cancer of Brain is of two types. Benign which is not cancerous not danger at all, other one is Malignant which is cancerous tumor; it grows abnormally by multiplying the cells rapidly, which leads to the death of the person if not detected. Manually it is not so easily possible to detect and identify the tumor. Programming division method by MRI is way to detect and identify the tumor. In order to give precise output a strong segmentation method is needed. In this paper we discussed the Histogram segmentation method of the MR Images. The scanned human brain MRI images are taken from the database and experimented in the MATLAB.

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Brain Tumor Detection in Fmri Images

Brain Tumor Detection in Fmri Images

Brain tumors belong to the most dangerous and deadly diseases. Not many diseases are as life changing as brain tumors are, as they affect the brain which is without doubt the most important organ in the human body. It is what we are, if you apply minor changes to a person brain the person will change. Now if something like a brain tumor grows in the brain, exerts pressure on other brain areas and possibly also spreads out to other areas of body and brain, this affects a person’s brain and therefore everything the person is. Alas, it often ends fatally. This year’s estimations predict that around 17,000 people will die from brain or spinal cord tumors and around 24,000 people will be diagnosed with such a tumor in the USA this year [1].

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Automatic Detection of Brain Tumor Using K Means Clustering

Automatic Detection of Brain Tumor Using K Means Clustering

Abstract: Brain tumor is an uncommon and uncontrolled growth of cell in brain. In medical image processing one of the most challenging tasks is study of Brain tumor. Recently, magnetic resonance imaging (MRI) is the most widely used imaging modality for identifying brain tumor and other discrepancies. From these MRI images, we are able to determine the detailed anatomical information to examine the development of the human brain and to diagnose the various diseases. The tumor detection becomes most complicated for the huge image database. So a software approach is required to help the accurate and faster clinical diagnosis. The proposed system identifies and segments the tumor portions of the image successfully using MATLAB. The aim is to introduce an algorithm employed for performing useful operations on the MRI images such as filtering, pre-processing, morphological operation, K-means clustering, feature Extraction, Decision making system, SVM Classifier & Naïve Bayes. This work helps to save the time of both pathologist and doctor. The experimental results show that the proposed method detects tumor areas efficiently in the image of the brain.

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 NANOROBOTS IN BRAIN TUMOR

 NANOROBOTS IN BRAIN TUMOR

Brain cancer is rare. But other types of cancers are not. Doctors know what drug to use, but they don't know how to target the tumor so the rest of the healthy cells won't be destroyed. Consider this: The researchers at the University of Michigan (U-M) Comprehensive Cancer Center have found a way to inject the drug Photofrin into nanoparticles, which in turn target the cancerous area -- a not too invasive process that resulted in the survival of brain cancer afflicted lab rats and extended their lifespans way longer than expected ( fig 7). How does it happen? "Photofrin is drawn through the bloodstream to tumors. Doctors then use a special laser light to activate the drug, which collapses the blood vessels that feed the tumor. Without its blood supply, the tumor starves."We have seen but the tip of the iceberg! MIT scientists have devised remotely controlled nanoparticles that, when pulsed with an electromagnetic field, release drugs to attack tumors 24 . Here, dark gray nanoparticles carry different drug payloads (one red, one green). A remotely generated five-minute pulse of a low-energy electromagnetic field releases the green drug but not the red. A five-minute pulse of a higher-energy electromagnetic field releases the red drug, which had been tethered using a DNA strand twice as long as the green tether, as measured in base pairs 25 . Once you've identified a group of cells that needs some chemical substance delivered to it, you can simply release the agent from onboard tanks after the nanorobot arrives on the scene. A 1 cm 3

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Pre processing and Segmentation of Brain Image for Tumor Detection

Pre processing and Segmentation of Brain Image for Tumor Detection

Recent work by [8] used a combination of mathematical morphology, wavelet based segmentation and K-means to achieve tumor detection. Another novel approach using color based feature extraction using wavelet decomposition can be found in [9]. Authors used Berkeley wavelet transform for feature extraction and a Support Vector Machine for classification of features [10]. A paper introduced frame and Gabor systems and its use in wavelet analysis for brain segmentation [11]. The use of frame theory and wavelet is used for image restoration [12]. Thus, it signifies that frame theory and wavelets can be used effectively in image processing. Authors also employed techniques of discrete wavelet transform (DWT) and principal component analysis (PCA), and used K-nearest neighbor (KNN) for segmentation [13]. Some employed stationary wavelet transforms to take place of traditional Discrete Wavelet Transform [14]. Afterwards, to train the classifier, they introduced a novel algorithm that is a hybridization of PSO and ABC. Researchers combined discrete wavelet transform with principal component analysis for segmentation [15]. Although those above mentioned algorithms give good results, still, segmentation accuracy could be improved further. Here we design an algorithm based on frame theoretic methods and using discrete wavelet transforms mirror and apply it to brain MRI images and compare its performance with other segmentation algorithms. Significant gains in performance are observed, although the execution time is traded off.

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Brain Tumor Detection using Modified Fuzzy Segmentation

Brain Tumor Detection using Modified Fuzzy Segmentation

To eliminate the dependence of the operator and improve the accuracy of diagnosis system aided diagnosis computer are a valuable and beneficial means for the detection of cancer and classification. Segmentation techniques based on gray level techniques such as threshold and techniques based on region are the simplest and find limited application techniques. However, their performance can be improved by incorporating them with the methods of hybrid clustering. techniques based on textural characteristics using atlas or look-up table can have excellent results on the segmentation of medical images , however, they need expertise in the construction of the atlas Limiting the technical atlas based is that , in some circumstances , it becomes difficult to choose correctly and label data has difficulty in segmenting complex structure with variable shape, size and properties in such situations it is best to use unsupervised methods such as fuzzy algorithms. In this work we proposed a novel fuzzy based MRI Image Segmentation algorithm, Fuzzy Segmentation involves the task of dividing data points into homogeneous classes or clusters so that items in the same class are as similar as possible and items in different classes are as dissimilar as possible. The results show that Fuzzy based Segmentation can successfully segment a tumor provided the parameters are set properly in the environment

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PARP inhibition sensitizes childhood high grade glioma, medulloblastoma and ependymoma to radiation

PARP inhibition sensitizes childhood high grade glioma, medulloblastoma and ependymoma to radiation

Poly ADP-ribose polymerase (PARP) enzymes have an essential role in single strand break (SSB) DNA repair and currently there are several clinically relevant PARP inhibitors available. Upon DNA damage, PARP1 is recruited to SSBs and the thereby induced PAR-chains, allow base-excision repair [14]. Moreover, PARP1 is involved in delaying the replication fork in homologous recombination (HR) proficient DNA damaged cells and in alternative pathways of non-homologous end joining (NHEJ) [15-17]. PARP inhibitors are in particular synthetically lethal in tumors with clear DNA repair deficiencies such as BRCA- tumors. However, both in vitro and mice studies indicate the rationale to combine PARP inhibitors with DNA damaging agents in many different tumor types. Recently, PARP inhibitor Olaparib was shown to increase radiosensitivity of non-small cell lung cancer in vitro and in vivo, by inhibition of DNA repair and reduction of hypoxia by increased tumor perfusion [18]. Moreover, PARP inhibitors are being investigated in phase I and II clinical trials for multiple cancers, as chemo- and radiosensitizers [19, 20]

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