INTRODUCTION: Survival varies in patients with glioblastoma due to intratumoral heterogeneity and radiomics/imaging biomarkers have potential to demonstrate heterogeneity. The objective was to combine radiomic, semantic and clinical features to impro...
Methylation of the O-methylguanine methyltransferase (MGMT) gene promoter is correlated with the effectiveness of the current standard of care in glioblastoma patients. In this study, a deep learning pipeline is designed for automatic prediction of M...
Cancer imaging : the official publication of the International Cancer Imaging Society
Aug 5, 2020
BACKGROUND: This study aims to identify robust radiomic features for Magnetic Resonance Imaging (MRI), assess feature selection and machine learning methods for overall survival classification of Glioblastoma multiforme patients, and to robustify mod...
Clinical cancer research : an official journal of the American Association for Cancer Research
Jul 21, 2020
PURPOSE: Glioblastoma (GBM) is one of the deadliest cancers with no cure. While conventional MRI has been widely adopted to examine GBM clinically, accurate neuroimaging assessment of tumor histopathology for improved diagnosis, surgical planning, an...
Glioblastoma is the most common malignant brain parenchymal tumor yet remains challenging to treat. The current standard of care-resection and chemoradiation-is limited in part due to the genetic heterogeneity of glioblastoma. Previous studies have i...
BACKGROUND: Measurement of volumetric features is challenging in glioblastoma. We investigate whether volumetric features derived from preoperative MRI using a convolutional neural network-assisted segmentation is correlated with survival.
A goal of cancer research is to reveal cell subsets linked to continuous clinical outcomes to generate new therapeutic and biomarker hypotheses. We introduce a machine learning algorithm, Risk Assessment Population IDentification (RAPID), that is uns...
PURPOSE: While neural networks gain popularity in medical research, attempts to make the decisions of a model explainable are often only made towards the end of the development process once a high predictive accuracy has been achieved.
Glioblastoma is a heterogeneous lethal disease, regulated by a stem-cell hierarchy and the neurotransmitter microenvironment. The identification of chemotherapies targeting individual cancer stem cells is a clinical need. A robotic workstation was ...
Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]
May 11, 2020
BACKGROUND: Magnetic resonance imaging (MRI) and amino acid positron-emission tomography (PET) of the brain contain a vast amount of structural and functional information that can be analyzed by machine learning algorithms and radiomics for the use o...
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