Neuroimaging of Pediatric Brain Tumors: From Basic to Advanced Magnetic Resonance Imaging (MRI)

AbstractBrain tumors represent the highest cause of mortality in the pediatric oncological population. Diagnosis is commonly performed with magnetic resonance imaging. Survival biomarkers are challenging to identify due to the relatively low numbers of individual tumor types. 69 children with biopsy-confirmed brain tumors were recruited into this study. All participants had perfusion and diffusion weighted imaging performed at diagnosis. Imaging data were processed using conventional methods, and a Bayesian survival analysis performed. Unsupervised and supervised machine learning were performed with the survival features, to determine novel sub-groups related to survival. Sub-group analysis was undertaken to understand differences in imaging features. Survival analysis showed that a combination of diffusion and perfusion imaging were able to determine two novel sub-groups of brain tumors with different survival characteristics (p < 0.01), which were subsequently classified with high accuracy (98%) by a neural network. Analysis of high-grade tumors showed a marked difference in survival (p = 0.029) between the two clusters with high risk and low risk imaging features. This study has developed a novel model of survival for pediatric brain tumors. Tumor perfusion plays a key role in determining survival and should be considered as a high priority for future imaging protocols.

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Magnetic Resonance Imaging of Pediatric Brain Tumors: State of the Art

Topics in Magnetic Resonance Imaging ◽

10.1097/00002142-200112000-00004 ◽

2001 ◽

Vol 12 (6) ◽

pp. 411-433 ◽

Cited By ~ 27

Author(s):

Tina Young Poussaint

Keyword(s):

Magnetic Resonance Imaging ◽

Magnetic Resonance ◽

Brain Tumors ◽

State Of The Art ◽

Pediatric Brain Tumors ◽

Resonance Imaging ◽

Pediatric Brain

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833 The Use of Intraoperative Magnetic Resonance Imaging for the Treatment of Pediatric Brain Tumors

Neurosurgery ◽

10.1097/00006123-200008000-00181 ◽

2000 ◽

Vol 47 (2) ◽

pp. 538-538

Author(s):

Mark R. Proctor ◽

Elizabeth A. Eldredge ◽

Ferenc A. Jolesz ◽

Liliana Goumnerova ◽

R. Michael Scott ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Magnetic Resonance ◽

Brain Tumors ◽

Pediatric Brain Tumors ◽

Resonance Imaging ◽

Intraoperative Magnetic Resonance Imaging ◽

Pediatric Brain

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MRI image analysis methods and applications: an algorithmic perspective using brain tumors as an exemplar

Neuro-Oncology Advances ◽

10.1093/noajnl/vdaa049 ◽

2020 ◽

Vol 2 (1) ◽

Author(s):

Vachan Vadmal ◽

Grant Junno ◽

Chaitra Badve ◽

William Huang ◽

Kristin A Waite ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Computer Vision ◽

Image Analysis ◽

Magnetic Resonance ◽

Brain Tumors ◽

Computational Techniques ◽

Complex Nature ◽

Resonance Imaging ◽

Magnetic Resonance Imaging Mri ◽

Mri Image

Abstract The use of magnetic resonance imaging (MRI) in healthcare and the emergence of radiology as a practice are both relatively new compared with the classical specialties in medicine. Having its naissance in the 1970s and later adoption in the 1980s, the use of MRI has grown exponentially, consequently engendering exciting new areas of research. One such development is the use of computational techniques to analyze MRI images much like the way a radiologist would. With the advent of affordable, powerful computing hardware and parallel developments in computer vision, MRI image analysis has also witnessed unprecedented growth. Due to the interdisciplinary and complex nature of this subfield, it is important to survey the current landscape and examine the current approaches for analysis and trend trends moving forward.

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