AIMC Topic: Glioblastoma

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Deep Learning of Imaging Phenotype and Genotype for Predicting Overall Survival Time of Glioblastoma Patients.

IEEE transactions on medical imaging Jan 6, 2020
Glioblastoma (GBM) is the most common and deadly malignant brain tumor. For personalized treatment, an accurate pre-operative prognosis for GBM patients is highly desired. Recently, many machine learning-based methods have been adopted to predict ove...
Brain Neoplasms Deep Learning Genotype Magnetic Resonance Imaging Glioblastoma Humans Phenotype
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Brain tumor segmentation approach based on the extreme learning machine and significantly fast and robust fuzzy C-means clustering algorithms running on Raspberry Pi hardware.

Medical hypotheses Nov 18, 2019
Automatic decision support systems have gained importance in health sector in recent years. In parallel with recent developments in the fields of artificial intelligence and image processing, embedded systems are also used in decision support systems...
Diagnosis, Computer-Assisted Image Processing, Computer-Assisted Support Vector Machine Magnetic Resonance Imaging Humans Fuzzy Logic Software Algorithms Brain Cluster Analysis Brain Neoplasms Glioblastoma Neurons
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Novel approaches for glioblastoma treatment: Focus on tumor heterogeneity, treatment resistance, and computational tools.

Cancer reports (Hoboken, N.J.) Nov 11, 2019
BACKGROUND: Glioblastoma (GBM) is a highly aggressive primary brain tumor. Currently, the suggested line of action is the surgical resection followed by radiotherapy and treatment with the adjuvant temozolomide, a DNA alkylating agent. However, the a...
Tumor Suppressor Proteins DNA Repair Enzymes Prognosis Drug Resistance, Neoplasm Tumor Microenvironment Genetic Heterogeneity Cancer Vaccines Humans Microtubules Brain Neoplasms Models, Genetic Machine Learning Chemoradiotherapy, Adjuvant Tumor Suppressor Protein p53 Antineoplastic Combined Chemotherapy Protocols Brain Immune Checkpoint Inhibitors Clonal Evolution Glioblastoma Precision Medicine Computational Biology DNA Modification Methylases Mutation DNA Mutational Analysis
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An expert system for brain tumor detection: Fuzzy C-means with super resolution and convolutional neural network with extreme learning machine.

Medical hypotheses Oct 15, 2019
Super-resolution, which is one of the trend issues of recent times, increases the resolution of the images to higher levels. Increasing the resolution of a vital image in terms of the information it contains such as brain magnetic resonance image (MR...
Brain Neoplasms Diagnosis, Computer-Assisted Deep Learning Image Processing, Computer-Assisted Magnetic Resonance Imaging Humans Early Detection of Cancer Glioblastoma Algorithms Expert Systems Neuroimaging Software Design
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Integration of Cancer Genomics Data for Tree-based Dimensionality Reduction and Cancer Outcome Prediction.

Molecular informatics Sep 6, 2019
Accurate outcome prediction is crucial for precision medicine and personalized treatment of cancer. Researchers have found that multi-dimensional cancer omics studies outperform each data type (mRNA, microRNA, methylation or somatic copy number alter...
Humans Cohort Studies Lung Neoplasms MicroRNAs Decision Trees Gene Expression Regulation, Neoplastic Glioblastoma Support Vector Machine Carcinoma, Non-Small-Cell Lung Databases, Genetic
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Radiomics Analysis for Glioma Malignancy Evaluation Using Diffusion Kurtosis and Tensor Imaging.

International journal of radiation oncology, biology, physics Jul 22, 2019
PURPOSE: A noninvasive diagnostic method to predict the degree of malignancy accurately would be of great help in glioma management. This study aimed to create a highly accurate machine learning model to perform glioma grading.
Humans Oligodendroglioma Machine Learning Adolescent Glioma Young Adult Diagnosis, Differential Adult Glioblastoma Retrospective Studies Datasets as Topic Brain Neoplasms Neoplasm Grading Female Male Diffusion Tensor Imaging Aged Middle Aged Anisotropy Aged, 80 and over Diffusion Magnetic Resonance Imaging Astrocytoma
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Deep learning derived tumor infiltration maps for personalized target definition in Glioblastoma radiotherapy.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Jul 11, 2019
PURPOSE: Glioblastoma is routinely treated by concomitant radiochemotherapy. Current target definition guidelines use anatomic MRI (magnetic resonance imaging) scans, taking into account contrast enhancement and the rather unspecific hyperintensity o...
Male Middle Aged Deep Learning Glioblastoma Neoplasm Recurrence, Local Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Diffusion Tensor Imaging Female Aged Aged, 80 and over Humans Adult Precision Medicine Brain Neoplasms Retrospective Studies
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Deep Convolution Neural Network (DCNN) Multiplane Approach to Synthetic CT Generation From MR images-Application in Brain Proton Therapy.

International journal of radiation oncology, biology, physics Jul 2, 2019
PURPOSE: The first aim of this work is to present a novel deep convolution neural network (DCNN) multiplane approach and compare it to single-plane prediction of synthetic computed tomography (sCT) by using the real computed tomography (CT) as ground...
Magnetic Resonance Imaging Multimodal Imaging Glioblastoma Feasibility Studies Radiotherapy Planning, Computer-Assisted Algorithms Tomography, X-Ray Computed Reproducibility of Results Brain Neoplasms Radiotherapy, Image-Guided Technology, Radiologic Head Radiotherapy Dosage Air Skull Proton Therapy Humans Neural Networks, Computer
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A high-throughput screening and computation platform for identifying synthetic promoters with enhanced cell-state specificity (SPECS).

Nature communications Jun 28, 2019
Cell state-specific promoters constitute essential tools for basic research and biotechnology because they activate gene expression only under certain biological conditions. Synthetic Promoters with Enhanced Cell-State Specificity (SPECS) can be supe...
Gene Expression Regulation Promoter Regions, Genetic Lentivirus Cell Separation Gene Library Regulatory Elements, Transcriptional Induced Pluripotent Stem Cells Organoids Female Humans Breast Neoplasms Machine Learning Cell Line, Tumor Software Neoplastic Stem Cells Glioblastoma
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F-FDG-PET-based radiomics features to distinguish primary central nervous system lymphoma from glioblastoma.

NeuroImage. Clinical Jun 27, 2019
The differential diagnosis of primary central nervous system lymphoma from glioblastoma multiforme (GBM) is essential due to the difference in treatment strategies. This study retrospectively reviewed 77 patients (24 with lymphoma and 53 with GBM) to...
Humans Machine Learning Retrospective Studies Neuroimaging Female Diagnosis, Differential Glioblastoma Aged Radiopharmaceuticals Positron-Emission Tomography Brain Neoplasms Fluorodeoxyglucose F18 Male Middle Aged Lymphoma
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