AIMC Topic: Brain Neoplasms

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Comparison of deep learning synthesis of synthetic CTs using clinical MRI inputs.

Physics in medicine and biology
There has been substantial interest in developing techniques for synthesizing CT-like images from MRI inputs, with important applications in simultaneous PET/MR and radiotherapy planning. Deep learning has recently shown great potential for solving t...

Feasibility of automated planning for whole-brain radiation therapy using deep learning.

Journal of applied clinical medical physics
PURPOSE: The purpose of this study was to develop automated planning for whole-brain radiation therapy (WBRT) using a U-net-based deep-learning model for predicting the multileaf collimator (MLC) shape bypassing the contouring processes.

Diagnostic Accuracy of Machine Learning-Based Radiomics in Grading Gliomas: Systematic Review and Meta-Analysis.

Contrast media & molecular imaging
PURPOSE: This study aimed to estimate the diagnostic accuracy of machine learning- (ML-) based radiomics in differentiating high-grade gliomas (HGG) from low-grade gliomas (LGG) and to identify potential covariates that could affect the diagnostic ac...

Improved Glioma Grading Using Deep Convolutional Neural Networks.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Accurate determination of glioma grade leads to improved treatment planning. The criterion standard for glioma grading is invasive tissue sampling. Recently, radiomic features have shown excellent potential in glioma-grade pre...

Analyzing magnetic resonance imaging data from glioma patients using deep learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The quantitative analysis of images acquired in the diagnosis and treatment of patients with brain tumors has seen a significant rise in the clinical use of computational tools. The underlying technology to the vast majority of these tools are machin...

Estimating Local Cellular Density in Glioma Using MR Imaging Data.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Increased cellular density is a hallmark of gliomas, both in the bulk of the tumor and in areas of tumor infiltration into surrounding brain. Altered cellular density causes altered imaging findings, but the degree to which ce...

Noninvasive Determination of and 1p19q Status of Lower-grade Gliomas Using MRI Radiomics: A Systematic Review.

AJNR. American journal of neuroradiology
BACKGROUND: Determination of () status and, if -mutant, assessing 1p19q codeletion are an important component of diagnosis of World Health Organization grades II/III or lower-grade gliomas. This has led to research into noninvasively correlating ima...

Discriminating pseudoprogression and true progression in diffuse infiltrating glioma using multi-parametric MRI data through deep learning.

Scientific reports
Differentiating pseudoprogression from true tumor progression has become a significant challenge in follow-up of diffuse infiltrating gliomas, particularly high grade, which leads to a potential treatment delay for patients with early glioma recurren...

Context aware deep learning for brain tumor segmentation, subtype classification, and survival prediction using radiology images.

Scientific reports
A brain tumor is an uncontrolled growth of cancerous cells in the brain. Accurate segmentation and classification of tumors are critical for subsequent prognosis and treatment planning. This work proposes context aware deep learning for brain tumor s...

Machine learning assisted intraoperative assessment of brain tumor margins using HRMAS NMR spectroscopy.

PLoS computational biology
Complete resection of the tumor is important for survival in glioma patients. Even if the gross total resection was achieved, left-over micro-scale tissue in the excision cavity risks recurrence. High Resolution Magic Angle Spinning Nuclear Magnetic ...