PS1 - 176 Where Have All the Fat Cells Gone? A Comparative Analysis of Adiposity Patterns in Childhood Brain Tumor Survivors and Non-Cancer Controls

Brain tumors are the most common solid tumors in children in Canada. While technological advances have increased their survival rates, survivors of childhood brain tumors (SCBT) often develop obesity, which can reduce lifespan and quality of life. While adiposity is a known factor for cardiometabolic disorders in the general population, adiposity patterns in SCBT have not been determined. This study aims to investigate how adiposity patterns differ between SCBT and non-cancer controls, and how lifestyle and treatment factors may contribute to these patterns. Methods: Fifty-nine SCBT and 108 non-cancer controls were recruited from the clinics at McMaster Children’s Hospital. Sociodemographic and lifestyle details were collected using standardized tools to assess diet, physical activity, and sleep. Brain tumor type, location and treatment details were obtained from medical records. Total and visceral adiposity were determined by total fat mass (FM) as well as waist-to-hip (WHR) and waist-to-height ratio (WHTR). Results: SCBT have higher total and visceral adiposity, while BMI is similar to controls. Female SCBT who received radiotherapy and/or chemotherapy have higher adiposity. A dietary pattern of white bread and fried foods with low dark bread was positively associated with adiposity. Lower physical activity levels, but not sleep durations, were associated with higher adiposity. Conclusion: SCBT have higher visceral and total adiposity than non-cancer controls. Sex, chemoradiotherapy, high fat diet, and physical inactivity, can contribute to these adiposity patterns. These results provide multiple points of entry to design interventions that reduce adiposity, and may improve long-term outcomes in SCBT.

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EPID-17. COMPARATIVE EPIDEMIOLOGY OF PRIMARY BRAIN TUMORS AMONG ADOLESCENTS AND YOUNG ADULTS (AYA)

Neuro-Oncology ◽

10.1093/neuonc/noab196.350 ◽

2021 ◽

Vol 23 (Supplement_6) ◽

pp. vi89-vi89

Author(s):

Nayan Lamba ◽

Bryan Iorgulescu

Keyword(s):

Brain Tumor ◽

Brain Tumors ◽

Cox Regression ◽

Adolescent And Young Adult ◽

Lower Grade ◽

Diffuse Glioma ◽

Tumor Type ◽

Primary Brain Tumors ◽

Diffuse Gliomas ◽

Hiv Aids

Abstract INTRODUCTION We utilized national registry data to evaluate the unique epidemiology of primary adolescent and young adult (AYA) brain tumors according to the WHO2016 classification. METHODS AYA patients (15≤age≤39) presenting between 2004-2017 with a brain tumor were identified by ICD-O-3 coding from the National Cancer Database (comprising >70% of newly-diagnosed cancers in the U.S.), and compared to pediatric and adult populations. Epidemiology and overall survival (estimated by Kaplan-Meier techniques and multivariable Cox regression) were assessed by WHO2016 tumor type. RESULTS 108,705 AYA brain tumor patients were identified (56.9% female), compared to 23,928 pediatric (46.8% female) and 748,272 adult (55.6% female) patients. Among the 69.4% of AYA brain tumors with pathological diagnosis, diffuse gliomas (31.4%), sellar tumors (19.2%), and meningiomas (15.3%) predominated in both sexes. Diffuse glioma (31.4%), sellar (19.2%), cranial nerve (7.3%), and mesenchymal non-meningothelial (4.1%) tumors represented a greater proportion of AYA brain tumors than in either pediatric or adult populations. A majority of all intracranial GCTs (59.2%) and neuronal & mixed neuronal-glial tumors (51.6%) presented during AYA. Although the prevalence of diffuse gliomas was similar between AYAs and adults, AYA gliomas were more likely to be grade 2-3 astrocytomas (38.9% vs 14.3%) and oligodendrogliomas (19.3% vs 4.3%) than in adults. GBMs represented 76.0% of adult diffuse gliomas vs. only 25.7% of AYA diffuse gliomas, but with a similar prevalence of MGMT promoter methylation (40.8% vs 38.4%). Notably, 50.7% of AYA PCNSLs were associated with HIV/AIDS, vs only 7.1% in adults (p< 0.001). CONCLUSIONS The distribution, epidemiology, and survival outcomes of primary brain tumors in the AYA population are distinct from their pediatric and adult counterparts. Notably, AYA infiltrative gliomas were more often of lower grade than adults and AYA PCNSL were far more likely to be associated with HIV/AIDS. Primary brain tumors in AYA patients require specialized management.

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Cerebrospinal Fluid MicroRNA Signatures as Diagnostic Biomarkers in Brain Tumors

Cancers ◽

10.3390/cancers11101546 ◽

2019 ◽

Vol 11 (10) ◽

pp. 1546 ◽

Cited By ~ 8

Author(s):

Alena Kopkova ◽

Jiri Sana ◽

Tana Machackova ◽

Marek Vecera ◽

Lenka Radova ◽

...

Keyword(s):

Cerebrospinal Fluid ◽

Brain Tumor ◽

Brain Tumors ◽

Low Grade ◽

Tumor Type ◽

Tumor Patients ◽

Primary Tumors ◽

Mirna Profiling ◽

Tumor Types ◽

Cns Malignancies

Central nervous system (CNS) malignancies include primary tumors that originate within the CNS as well as secondary tumors that develop as a result of metastatic spread. Circulating microRNAs (miRNAs) were found in almost all human body fluids including cerebrospinal fluid (CSF), and they seem to be highly stable and resistant to even extreme conditions. The overall aim of our study was to identify specific CSF miRNA patterns that could differentiate among brain tumors. These new biomarkers could potentially aid borderline or uncertain imaging results onto diagnosis of CNS malignancies, avoiding most invasive procedures such as stereotactic biopsy or biopsy. In total, 175 brain tumor patients (glioblastomas, low-grade gliomas, meningiomas and brain metastases), and 40 non-tumor patients with hydrocephalus as controls were included in this prospective monocentric study. Firstly, we performed high-throughput miRNA profiling (Illumina small RNA sequencing) on a discovery cohort of 70 patients and 19 controls and identified specific miRNA signatures of all brain tumor types tested. Secondly, validation of 9 candidate miRNAs was carried out on an independent cohort of 105 brain tumor patients and 21 controls using qRT-PCR. Based on the successful results of validation and various combination patterns of only 5 miRNA levels (miR-30e, miR-140, let-7b, mR-10a and miR-21-3p) we proposed CSF-diagnostic scores for each tumor type which enabled to distinguish them from healthy donors and other tumor types tested. In addition to this primary diagnostic tool, we described the prognostic potential of the combination of miR-10b and miR-196b levels in CSF of glioblastoma patients. In conclusion, we performed the largest study so far focused on CSF miRNA profiling in patients with brain tumors, and we believe that this new class of biomarkers have a strong potential as a diagnostic and prognostic tool in these patients.

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3D extracellular matrix microenvironment in bioengineered tissue models of primary pediatric and adult brain tumors

Nature Communications ◽

10.1038/s41467-019-12420-1 ◽

2019 ◽

Vol 10 (1) ◽

Cited By ~ 18

Author(s):

Disha Sood ◽

Min Tang-Schomer ◽

Dimitra Pouli ◽

Craig Mizzoni ◽

Nicole Raia ◽

...

Keyword(s):

Extracellular Matrix ◽

Brain Tumor ◽

Brain Tumors ◽

Adult Brain ◽

Metabolic Imaging ◽

Tumor Type ◽

Label Free ◽

Pediatric Ependymoma ◽

Extracellular Lipid ◽

Tumor Cell Behavior

Abstract Dynamic alterations in the unique brain extracellular matrix (ECM) are involved in malignant brain tumors. Yet studies of brain ECM roles in tumor cell behavior have been difficult due to lack of access to the human brain. We present a tunable 3D bioengineered brain tissue platform by integrating microenvironmental cues of native brain-derived ECMs and live imaging to systematically evaluate patient-derived brain tumor responses. Using pediatric ependymoma and adult glioblastoma as examples, the 3D brain ECM-containing microenvironment with a balance of cell-cell and cell-matrix interactions supports distinctive phenotypes associated with tumor type-specific and ECM-dependent patterns in the tumor cells’ transcriptomic and release profiles. Label-free metabolic imaging of the composite model structure identifies metabolically distinct sub-populations within a tumor type and captures extracellular lipid-containing droplets with potential implications in drug response. The versatile bioengineered 3D tumor tissue system sets the stage for mechanistic studies deciphering microenvironmental role in brain tumor progression.

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Expression and clinical importance of a newly discovered alternative splice variant of the gene for acrosin binding protein found in human brain tumors

Asian Biomedicine ◽

10.1515/abm-2020-0033 ◽

2020 ◽

Vol 14 (6) ◽

pp. 243-252

Author(s):

Baolong Zheng

Keyword(s):

Brain Tumor ◽

Brain Tumors ◽

Splice Variant ◽

Binding Protein ◽

Clinical Importance ◽

Seminiferous Tubules ◽

Tumor Type ◽

Splicing Variant ◽

Tumor Tissues ◽

Significant Difference

AbstractBackgroundAcrosin binding protein (ACRBP) is a member of the cancer–testis antigen (CTA) family. Normally, ACRBP mRNA is expressed only in seminiferous tubules, while abnormally it is expressed in various types of cancers in tumor tissues, such as brain tumor.ObjectivesTo determine the expression and clinical impact of a newly discovered splice variant of ACRBP in brain tumor.MethodsTotal RNA was extracted and reverse transcribed from 92 tumor specimens and 3 cell lines. Primers were designed to determine the expression of the new splice variant in all the samples. Quantitative real-time PCR (qPCR) was conducted for samples positive in reverse transcriptase-PCR. Association of the expression of ACRBP with the clinicopathological features of the various brain tumors was assessed statistically.ResultsThe primers identified a newly discovered splice variant of ACRBP named ACRBP-V5a. The proportions of samples of the various brain tumor types positive for the ACRBP-V5a splicing variant were as follows: astrocytoma 10/33 (30%), glioblastoma 10/30 (33%), medulloblastoma 14/29 (48%), all tumors 34/92 (37%). Although we did not find a significant difference in the proportions of samples of various types of brain tumor tissues positive for the new splice variant (P > 0.05), levels of expression of the ACRBP-V5a splice variant were significantly different for tumor grade (P = 0.01) and tumor type (P = 0.02).ConclusionsA newly discovered splice variant, ACRBP-V5a, is present in brain tumor. The new splicing variant may have discriminative value and potential importance in molecular-targeted therapy for brain tumors.

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Tumor Type, Epilepsy Burden, and Seizure Documentation: Experiences at a Single Center Neuro-Oncology Clinic

Neuro-Oncology Practice ◽

10.1093/nop/npab032 ◽

2021 ◽

Author(s):

Omar Bushara ◽

Alex Guzner ◽

Elizabeth Bachman ◽

Roger Stupp ◽

Rimas V Lukas ◽

...

Keyword(s):

Brain Tumor ◽

Clinical Practice ◽

Brain Tumors ◽

Antiepileptic Drug ◽

Retrospective Review ◽

Future Research ◽

Tumor Type ◽

Single Center ◽

Metastatic Brain Tumors ◽

Full Cohort

Abstract Background Patients with both primary and metastatic brain tumors have significant seizure burden due to their tumor. The management of tumor related epilepsy (TRE) and optimizing antiepileptic drug (AED) regimen requires collaboration between neurologists and seizure specialists, which is facilitated by seizure documentation in clinic notes. We aim to describe seizure incidence in patients seen in neuro-oncology clinical practice. Further, in the subset of those patients with TRE, we aim to analyze seizure documentation. Methods This is a retrospective review of patients with a primary or metastatic brain tumor seen in a neuro-oncology clinic in October 2019. Patients with TRE were included in the analysis of seizure documentation. These notes were analyzed for inclusion of seizure descriptors, terminology, AED regimens, and changes in management. Results Of the full cohort of 356 patients, 199 (55.9%) had TRE. Anaplastic astrocytomas had the highest percentage of patients with TRE. The analysis of seizure documentation in patients with TRE revealed that the majority of notes (90.9%) mentioned seizures. Fewer notes (39.6%) provided additional descriptions of the seizures or commented on AED regimens (58.3%). In notes for patients who had seizures within the previous 6 months, seizure descriptors were more likely. Conclusions This study defines the TRE burden in a cohort of patients seen in neuro-oncology clinic. Among patients with TRE, our study shows that documentation of many aspects of the characteristics and management of patient seizures can be improved, which would facilitate further analysis of impact on patient care as well as future research.

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Brain MRI Images Segmentation Based on U-Net Architecture

Iraqi Journal for Electrical And Electronic Engineering ◽

10.37917/ijeee.18.1.3 ◽

2021 ◽

Vol 18 (1) ◽

pp. 21-27

Author(s):

Assalah Atiyah ◽

Khawla Ali

Keyword(s):

Brain Tumor ◽

Brain Tumors ◽

Performance Metrics ◽

Brain Mri ◽

Regular Function ◽

Survival Rates ◽

Tumor Segmentation ◽

Brain Tumor Segmentation ◽

Edge Based ◽

The Brain

Brain tumors are collections of abnormal tissues within the brain. The regular function of the brain may be affected as it grows within the region of the skull. Brain tumors are critical for improving treatment options and patient survival rates to prevent and treat them. The diagnosis of cancer utilizing manual approaches for numerous magnetic resonance imaging (MRI) images is the most complex and time-consuming task. Brain tumor segmentation must be carried out automatically. A proposed strategy for brain tumor segmentation is developed in this paper. For this purpose, images are segmented based on region-based and edge-based. Brain tumor segmentation 2020 (BraTS2020) dataset is utilized in this study. A comparative analysis of the segmentation of images using the edge-based and region-based approach with U-Net with ResNet50 encoder, architecture is performed. The edge-based segmentation model performed better in all performance metrics compared to the region-based segmentation model and the edge-based model achieved the dice loss score of 0. 008768, IoU score of 0. 7542, f1 score of 0. 9870, the accuracy of 0. 9935, the precision of 0. 9852, recall of 0. 9888, and specificity of 0. 9951.

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Significance of hemorrhage into brain tumors: clinicopathological study

Journal of Neurosurgery ◽

10.3171/jns.1987.67.6.0852 ◽

1987 ◽

Vol 67 (6) ◽

pp. 852-857 ◽

Cited By ~ 190

Author(s):

Douglas Kondziolka ◽

Mark Bernstein ◽

Lothar Resch ◽

Charles H. Tator ◽

J. F. Ross Fleming ◽

...

Keyword(s):

Brain Tumor ◽

Brain Tumors ◽

Tumor Type ◽

Recurrent Tumor ◽

Content Type ◽

Intratumoral Hemorrhage ◽

Clinicopathological Study ◽

Pathological Review ◽

Neurological Presentation ◽

Fine Print

✓ A retrospective clinical and pathological review of 905 consecutive brain tumor cases (excluding pituitary adenoma and recurrent tumor) was conducted to identify cases in which intratumoral hemorrhage was confirmed grossly and/or pathologically. There were 132 cases so identified, for an overall tumor hemorrhage rate of 14.6%; of these, 5.4% were classified as macroscopic and 9.2% as microscopic. The presence of hemorrhage was correlated with the neurological presentation. The highest hemorrhage rate (70.0%) was found in patients with prior neurological history who experienced apoplectic deterioration (acute-on-chronic presentation). Only 57.1% of patients with acute deterioration in the absence of prior neurological symptoms had hemorrhages. The highest hemorrhage rate for primary brain tumors was 29.2% for mixed oligodendroglioma/astrocytoma, while the highest hemorrhage rate for any tumor type was 50% for metastatic melanoma. The clinical relevance of tumor hemorrhage is discussed.

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Perceptions of Visualizing Physical Activity as a 3D-Printed Object: Formative Study (Preprint)

10.2196/preprints.12064 ◽

2018 ◽

Author(s):

Sam Graeme Morgan Crossley ◽

Melitta Anne McNarry ◽

Joanne Hudson ◽

Parisa Eslambolchilar ◽

Zoe Knowles ◽

...

Keyword(s):

Physical Activity ◽

3D Printing ◽

3D Model ◽

Vigorous Physical Activity ◽

3D Models ◽

Primary School Children ◽

Activity Levels ◽

Activity Data ◽

Physical Activity Guidelines ◽

Technological Advances

BACKGROUND The UK government recommends that children engage in moderate-to-vigorous physical activity for at least 60 min every day. Despite associated physiological and psychosocial benefits of physical activity, many youth fail to meet these guidelines partly due to sedentary screen-based pursuits displacing active behaviors. However, technological advances such as 3D printing have enabled innovative methods of visualizing and conceptualizing physical activity as a tangible output. OBJECTIVE The aim of this study was to elicit children’s, adolescents’, parents’, and teachers’ perceptions and understanding of 3D physical activity objects to inform the design of future 3D models of physical activity. METHODS A total of 28 primary school children (aged 8.4 [SD 0.3] years; 15 boys) and 42 secondary school adolescents (aged 14.4 [SD 0.3] years; 22 boys) participated in semistructured focus groups, with individual interviews conducted with 8 teachers (2 male) and 7 parents (2 male). Questions addressed understanding of the physical activity guidelines, 3D model design, and both motivation for and potential engagement with a 3D physical activity model intervention. Pupils were asked to use Play-Doh to create and describe a model that could represent their physical activity levels (PAL). Data were transcribed verbatim and thematically analyzed, and key emergent themes were represented using pen profiles. RESULTS Pupils understood the concept of visualizing physical activity as a 3D object, although adolescents were able to better analyze and critique differences between low and high PAL. Both youths and adults preferred a 3D model representing a week of physical activity data when compared with other temporal representations. Furthermore, all participants highlighted that 3D models could act as a motivational tool to enhance youths’ physical activity. From the Play-Doh designs, 2 key themes were identified by pupils, with preferences indicated for models of abstract representations of physical activity or bar charts depicting physical activity, respectively. CONCLUSIONS These novel findings highlight the potential utility of 3D objects of physical activity as a mechanism to enhance children’s and adolescents’ understanding of, and motivation to increase, their PAL. This study suggests that 3D printing may offer a unique strategy for promoting physical activity in these groups.

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Image Based Group Classifier for Brain Tumor Detection Using Machine Learning Technique

Traitement du signal ◽

10.18280/ts.370520 ◽

2020 ◽

Vol 37 (5) ◽

pp. 865-871

Author(s):

Putta Rama Krishnaveni ◽

Gattim Naveen Kishore

Keyword(s):

Machine Learning ◽

Brain Tumor ◽

Brain Tumors ◽

Brain Mri ◽

The United States ◽

Tumor Detection ◽

Tumor Type ◽

Machine Learning Technique ◽

Learning Technique ◽

The Brain

In view of insights of the Central Brain Tumor Registry of the United States (CBTRUS), brain tumor is one of the main sources of disease related deaths in the World. It is the subsequent reason for tumor related deaths in adults under the age 20-39. Magnetic Resonance Imaging (MRI) is assuming a significant job in the examination of neuroscience for contemplating brain images. The investigation of brain MRI Images is useful in brain tumor analysis process. Features will be extricated and selected from the segmented pictures and afterward grouped by utilizing the classification procedures to analyze whether the patient is ordinary (having no tumor) or irregular (having tumor). One of the most dangerous cancers is brain tumor or cancer which affects the human body's main nervous system. Infection that can affect is very sensitive to the brain. Two types of brain tumors are present. The tumor may be categorized as benign and malignant. The benign tumor represents a change in the shape and structure of the cells, but cannot contaminate or spread to other cells in the brain. The malignant tumor can spread and grow if not carefully treated and removed. The detection of brain tumors is a difficult and sensitive task involving the classifier's experience. In the proposed work a Group based Classifier for Brain Tumor Recognition (GbCBTD) is introduced for the efficient segmentation of MRI images and for identification of tumor. The use of Convolutional Neural Network (CNN) system to classify the brain tumor type is presented in this work. Relevant features are extracted from images and by using CNN with machine learning technique, tumor can be recognized. CNN can reduce the cost and increase the performance of brain tumor detection. The proposed work is compared to the traditional methods and the results show that the proposed method is effective in detecting tumors.

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Brain Tumor Detection and Segmentation: A Survey

IRA-International Journal of Technology & Engineering (ISSN 2455-4480) ◽

10.21013/jte.v10.n4.p1 ◽

2020 ◽

Vol 10 (4) ◽

pp. 55

Author(s):

Kalifa Shantta ◽

Otman Basir

Keyword(s):

Brain Tumor ◽

Brain Tumors ◽

Survival Rates ◽

Automatic Detection ◽

Tumor Detection ◽

Detection Techniques ◽

Pros And Cons ◽

Accuracy Performance ◽

Image Modality

<p class="Abstract">Even with the enormous progress in medical technology, brain tumor detection is still an extremely tedious and complex task for the physicians. The early and accurate detection of brain tumors enables effective and efficient therapy and thus can result in increased survival rates. Automatic detection and classification of brain tumors have the potential to achieve efficiency and a higher degree of predictable accuracy. However, it is well established that the accuracy performance of automatic detection and classification techniques varies from technique to technique, and tends to be image modality dependent. This paper reviews the state-of-the-art detection techniques and highlights their pros and cons.</p>

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