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Glioblastoma

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ONCOhabitats: A system for glioblastoma heterogeneity assessment through MRI.

International journal of medical informatics
BACKGROUND: Neuroimaging analysis is currently crucial for an early assessment of glioblastoma, to help improving treatment and tumor follow-up. To this end, multiple functional and morphological MRI sequences are usually employed, requiring the deve...

Classification of high dimensional biomedical data based on feature selection using redundant removal.

PloS one
High dimensional biomedical data contain tens of thousands of features, accurate and effective identification of the core features in these data can be used to assist diagnose related diseases. However, there are often a large number of irrelevant or...

Application of MR morphologic, diffusion tensor, and perfusion imaging in the classification of brain tumors using machine learning scheme.

Neuroradiology
PURPOSE: While MRI is the modality of choice for the assessment of patients with brain tumors, differentiation between various tumors based on their imaging characteristics might be challenging due to overlapping imaging features. The purpose of this...

A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning.

Medical image analysis
In this paper we present a method for simultaneously segmenting brain tumors and an extensive set of organs-at-risk for radiation therapy planning of glioblastomas. The method combines a contrast-adaptive generative model for whole-brain segmentation...

Differentiation of glioblastoma from solitary brain metastases using radiomic machine-learning classifiers.

Cancer letters
This study aimed to identify the optimal radiomic machine-learning classifier for differentiating glioblastoma (GBM) from solitary brain metastases (MET) preoperatively. Four hundred and twelve patients with solitary brain tumors (242 GBM and 170 sol...

Accurate Patient-Specific Machine Learning Models of Glioblastoma Invasion Using Transfer Learning.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: MR imaging-based modeling of tumor cell density can substantially improve targeted treatment of glioblastoma. Unfortunately, interpatient variability limits the predictive ability of many modeling approaches. We present a tran...

Deep Learning-Based Framework for Identification of Glioblastoma Tumor using Hyperspectral Images of Human Brain.

Sensors (Basel, Switzerland)
The main goal of brain cancer surgery is to perform an accurate resection of the tumor, preserving as much normal brain tissue as possible for the patient. The development of a non-contact and label-free method to provide reliable support for tumor r...

Highly accelerated, model-free diffusion tensor MRI reconstruction using neural networks.

Medical physics
PURPOSE: The purpose of this study was to develop a neural network that accurately performs diffusion tensor imaging (DTI) reconstruction from highly accelerated scans.

Prediction of IDH1 Mutation Status in Glioblastoma Using Machine Learning Technique Based on Quantitative Radiomic Data.

World neurosurgery
OBJECTIVE: Isocitrate dehydrogenase 1 (IDH1) mutation status is an independent favorable prognostic factor for glioblastoma (GBM) and is usually determined by sequencing or immunohistochemistry. An accurate prediction of IDH1 mutation status via noni...