scholarly journals Multiparametric Analysis of Permeability and ADC Histogram Metrics for Classification of Pediatric Brain Tumors by Tumor Grade

2018 ◽  
Vol 39 (3) ◽  
pp. 552-557 ◽  
Author(s):  
S. Vajapeyam ◽  
D. Brown ◽  
P.R. Johnston ◽  
K.I. Ricci ◽  
M.W. Kieran ◽  
...  
Keyword(s):  
Brain Tumors ◽  
Tumor Grade ◽  
Pediatric Brain Tumors ◽  
Multiparametric Analysis ◽  
Pediatric Brain

scholarly journals Advances in the classification of pediatric brain tumors through DNA methylation profiling: From research tool to frontline diagnostic

Cancer ◽  
2018 ◽  
Vol 124 (21) ◽  
pp. 4168-4180 ◽  
Author(s):  
Rahul Kumar ◽  
Anthony P. Y. Liu ◽  
Brent A. Orr ◽  
Paul A. Northcott ◽  
Giles W. Robinson
Keyword(s):  
Dna Methylation ◽  
Brain Tumors ◽  
Pediatric Brain Tumors ◽  
Research Tool ◽  
Dna Methylation Profiling ◽  
Pediatric Brain ◽  
Methylation Profiling

scholarly journals Comparison of Diffusion and Perfusion Techniques in Differentiating High Versus Low Grade Pediatric Brain Tumors

2020 ◽  
Vol 3 ◽  
Author(s):  
Eric Chen ◽  
Chang Ho ◽  
Benjamin Gray ◽  
Jason Parker ◽  
Emily Diller ◽  
...  
Keyword(s):  
Brain Tumors ◽  
Tumor Grade ◽  
Pediatric Brain Tumors ◽  
Solid Cancer ◽  
Low Grade ◽  
Dynamic Susceptibility ◽  
High Grade ◽  
Dynamic Contrast Enhancement ◽  
Significant Difference ◽  
Pediatric Brain

Background/Objective: Brain tumors are the most common solid cancer in children and cause significant mortality and morbidity. We compare the effectiveness of different parameters in predicting tumor grade between dynamic contrast enhancement (DCE), intravoxel incoherent motion (IVIM), dynamic susceptibility contrast (DSC) perfusion and diffusion weighted imaging (DWI).    Methods: A retrospective blinded review of pediatric brain tumors with DCE, IVIM, DWI, and DSC was performed. Parametric maps were registered to T2 weighted images. Volumetric regions of interest (ROI) were manually segmented from solid tumor components for each patient by a neuroradiologist (CH), neuroradiology fellow (BG), and medical student (EC). Resulting mean values for parameters from DCE (Ktrans, Kep, Ve, Vp,), IVIM (D, D*, f), DSC (rCBV) and DWI (ADC) were compared using Student’s t-test for high- and low-grade tumor groups based on WHO grading from pathology. For significant parameters, receiver operating characteristic (ROC) analysis with area under curve (AUC) was performed.     Results: 20 subjects were included with 9 low grade and 11 high grade tumors. Significant differences between low versus high grade were demonstrated for D (10−3 mm2/s) (1.4±0.4 vs 0.9±0.2, p=0.01), f (0.04±0.02 vs 0.07±0.02, p=0.02), ADC (10−3 mm2/s) (1.4±0.4 vs 0.9±0.3, p=0.009) and rCBV (2.2±0.9 vs 4.7±2.1, p=0.003). No significant difference was found for D* or any DCE parameter. AUC from ROC was similar for all significant parameters [D (0.81, p=0.003); f (0.80, p=0.003); ADC (0.83, p=0.001); rCBV (0.83, p=0.0005)].    Conclusion: D and f parameters from IVIM can significantly differentiate high versus low grade pediatric brain tumors similar to ADC and rCBV. Conversely, no DCE parameter was significant.    Scientific Implications: The results will assist the selection of MRI sequences that best predict tumor grade, as well as guide tumor biopsy for the most aggressive tumor portions. Further study of these techniques may correlate with molecular profiling and predict outcome. 

scholarly journals Advances in the molecular classification of pediatric brain tumors: a guide to the galaxy

2020 ◽  
Vol 251 (3) ◽  
pp. 249-261 ◽  
Author(s):  
Chantel Cacciotti ◽  
Adam Fleming ◽  
Vijay Ramaswamy
Keyword(s):  
Brain Tumors ◽  
Molecular Classification ◽  
Pediatric Brain Tumors ◽  
The Galaxy ◽  
Pediatric Brain

MicroRNA expression profiling for Molecular Classification of pediatric brain tumors

2011 ◽  
Vol 57 (1) ◽  
pp. 183-184 ◽  
Author(s):  
Simone Treiger Sredni ◽  
Chiang-Ching Huang ◽  
Maria de Fátima Bonaldo ◽  
Tadanori Tomita
Keyword(s):  
Brain Tumors ◽  
Expression Profiling ◽  
Molecular Classification ◽  
Pediatric Brain Tumors ◽  
Microrna Expression ◽  
Pediatric Brain

scholarly journals Application of pattern recognition techniques for classification of pediatric brain tumors by in vivo 3T 1 H‐MR spectroscopy—A multi‐center study

2017 ◽  
Vol 79 (4) ◽  
pp. 2359-2366 ◽  
Author(s):  
Niloufar Zarinabad ◽  
Laurence J. Abernethy ◽  
Shivaram Avula ◽  
Nigel P. Davies ◽  
Daniel Rodriguez Gutierrez ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Brain Tumors ◽  
Mr Spectroscopy ◽  
Pediatric Brain Tumors ◽  
Pattern Recognition Techniques ◽  
Multi Center Study ◽  
Pediatric Brain ◽  
Center Study

scholarly journals Pediatric Brain Tumors: From Modern Classification System to Current Principles of Management

2021 ◽  
Author(s):  
Ahmad Ozair ◽  
Erum Khan ◽  
Vivek Bhat ◽  
Arjumand Faruqi ◽  
Anil Nanda
Keyword(s):  
Brain Tumors ◽  
Pediatric Brain Tumors ◽  
Solid Organ ◽  
Therapeutic Decision ◽  
Modern Classification ◽  
Therapeutic Decision Making ◽  
Pediatric Brain ◽  
Cns Malignancies

Central nervous system (CNS) malignancies contribute significantly to the global burden of cancer. Brain tumors constitute the most common solid organ tumors in children and the second most common malignancies of childhood overall. Accounting for nearly 20% of all pediatric malignancies, these are the foremost cause of cancer-related deaths in children 0–14 years of age. This book chapter provides a state-of-the-art overview of pediatric brain tumors. It discusses their morbidity and mortality and introduces the WHO 2021 classification of CNS tumors, which is critical to therapeutic decision-making. It then describes the modern understanding of tumor grading and its clinical implications, followed by the general principles of diagnosis and management. The chapter then discusses, in detail, those brain tumors which have the highest disease burden in children, including medulloblastoma, astrocytoma, ependymoma, schwannoma, meningioma, amongst others. The landscape of treatment of pediatric brain tumors has been rapidly evolving, with several effective therapies on the horizon.

scholarly journals Multiclass imbalance learning: Improving classification of pediatric brain tumors from magnetic resonance spectroscopy

2016 ◽  
Vol 77 (6) ◽  
pp. 2114-2124 ◽  
Author(s):  
Niloufar Zarinabad ◽  
Martin Wilson ◽  
Simrandip K Gill ◽  
Karen A Manias ◽  
Nigel P Davies ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Spectroscopy ◽  
Brain Tumors ◽  
Pediatric Brain Tumors ◽  
Resonance Spectroscopy ◽  
Imbalance Learning ◽  
Pediatric Brain
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