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Glioblastoma

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A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme.

Scientific reports
Traditional radiomics models mainly rely on explicitly-designed handcrafted features from medical images. This paper aimed to investigate if deep features extracted via transfer learning can generate radiomics signatures for prediction of overall sur...

DCE-MRI prediction of survival time for patients with glioblastoma multiforme: using an adaptive neuro-fuzzy-based model and nested model selection technique.

NMR in biomedicine
This pilot study investigates the construction of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for the prediction of the survival time of patients with glioblastoma multiforme (GBM). ANFIS is trained by the pharmacokinetic (PK) parameters estimat...

A machine learning classifier trained on cancer transcriptomes detects NF1 inactivation signal in glioblastoma.

BMC genomics
BACKGROUND: We have identified molecules that exhibit synthetic lethality in cells with loss of the neurofibromin 1 (NF1) tumor suppressor gene. However, recognizing tumors that have inactivation of the NF1 tumor suppressor function is challenging be...

Learning MRI-based classification models for MGMT methylation status prediction in glioblastoma.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The O-methylguanine-DNA-methyltransferase (MGMT) promoter methylation has been shown to be associated with improved outcomes in patients with glioblastoma (GBM) and may be a predictive marker of sensitivity to chemotherapy. ...

Artificial Neural Networks approach to pharmacokinetic model selection in DCE-MRI studies.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: In pharmacokinetic analysis of Dynamic Contrast Enhanced MRI data, a descriptive physiological model should be selected properly out of a set of candidate models. Classical techniques suggested for this purpose suffer from issues like comput...

Diffuse intrinsic pontine gliomas in children: Interest of robotic frameless assisted biopsy. A technical note.

Neuro-Chirurgie
INTRODUCTION: Diffuse intrinsic pontine gliomas (DIPG) constitute 10-15% of all brain tumors in the pediatric population; currently prognosis remains poor, with an overall survival of 7-14 months. Recently the indication of DIPG biopsy has been enlar...

Brain tumor segmentation with Deep Neural Networks.

Medical image analysis
In this paper, we present a fully automatic brain tumor segmentation method based on Deep Neural Networks (DNNs). The proposed networks are tailored to glioblastomas (both low and high grade) pictured in MR images. By their very nature, these tumors ...

Improve Glioblastoma Multiforme Prognosis Prediction by Using Feature Selection and Multiple Kernel Learning.

IEEE/ACM transactions on computational biology and bioinformatics
Glioblastoma multiforme (GBM) is a highly aggressive type of brain cancer with very low median survival. In order to predict the patient's prognosis, researchers have proposed rules to classify different glioma cancer cell subtypes. However, survival...

Pseudo progression identification of glioblastoma with dictionary learning.

Computers in biology and medicine
OBJECTIVE: Although the use of temozolomide in chemoradiotherapy is effective, the challenging clinical problem of pseudo progression has been raised in brain tumor treatment. This study aims to distinguish pseudo progression from true progression.