AIMC Topic: Glioblastoma

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The application value of deep learning in the background of precision medicine in glioblastoma.

Science progress Jan 1, 2024
Glioblastoma is a highly malignant central nervous system tumor, World Health Organization Ⅳ, glioblastoma is the most common primary malignancy, due to its own specificity and complexity, different patients often benefit from the current convention...
Humans Deep Learning Glioblastoma Brain Precision Medicine Radiomics
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Image-Based Subtype Classification for Glioblastoma Using Deep Learning: Prognostic Significance and Biologic Relevance.

JCO clinical cancer informatics Jan 1, 2024
PURPOSE: To apply deep learning algorithms to histopathology images, construct image-based subtypes independent of known clinical and molecular classifications for glioblastoma, and produce novel insights into molecular and immune characteristics of ...
Humans Prognosis Proteomics Tumor Microenvironment Biological Products Deep Learning Glioblastoma
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Expert-centered Evaluation of Deep Learning Algorithms for Brain Tumor Segmentation.

Radiology. Artificial intelligence Jan 1, 2024
Purpose To present results from a literature survey on practices in deep learning segmentation algorithm evaluation and perform a study on expert quality perception of brain tumor segmentation. Materials and Methods A total of 180 articles reporting ...
Deep Learning Glioblastoma Benchmarking Humans Algorithms Brain Neoplasms
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Enhancing Spatial Transcriptomics Analysis by Integrating Image-Aware Deep Learning Methods.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 1, 2024
Spatial transcriptomics (ST) represents a pivotal advancement in biomedical research, enabling the transcriptional profiling of cells within their morphological context and providing a pivotal tool for understanding spatial heterogeneity in cancer ti...
Deep Learning Computational Biology Glioblastoma Humans Biomedical Research Gene Expression Profiling
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Bioinformatics and machine learning approach identifies potential drug targets and pathways in COVID-19.

Briefings in bioinformatics Sep 2, 2021
Current coronavirus disease-2019 (COVID-19) pandemic has caused massive loss of lives. Clinical trials of vaccines and drugs are currently being conducted around the world; however, till now no effective drug is available for COVID-19. Identification...
Computational Biology Humans Glioblastoma Machine Learning Central Nervous System Diseases Algorithms Brain Neoplasms Disease Progression
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Machine learning revealed stemness features and a novel stemness-based classification with appealing implications in discriminating the prognosis, immunotherapy and temozolomide responses of 906 glioblastoma patients.

Briefings in bioinformatics Sep 2, 2021
Glioblastoma (GBM) is the most malignant and lethal intracranial tumor, with extremely limited treatment options. Immunotherapy has been widely studied in GBM, but none can significantly prolong the overall survival (OS) of patients without selection...
Humans Machine Learning Antineoplastic Agents, Alkylating Prognosis Atlases as Topic Female Programmed Cell Death 1 Receptor Aged Survival Analysis Adult Temozolomide Immunotherapy Gene Expression Regulation, Neoplastic Middle Aged Treatment Outcome Tumor Microenvironment Neoplastic Stem Cells Gene Expression Profiling Brain Neoplasms Glioblastoma Transcriptome Patient Selection Male
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Bioinformatics and machine learning methodologies to identify the effects of central nervous system disorders on glioblastoma progression.

Briefings in bioinformatics Sep 2, 2021
Glioblastoma (GBM) is a common malignant brain tumor which often presents as a comorbidity with central nervous system (CNS) disorders. Both CNS disorders and GBM cells release glutamate and show an abnormality, but differ in cellular behavior. So, t...
Datasets as Topic Central Nervous System Proportional Hazards Models Signal Transduction Neoplasm Proteins Glioblastoma Atlases as Topic Glutamic Acid Kaplan-Meier Estimate Machine Learning Molecular Sequence Annotation Brain Neoplasms Gene Ontology Humans Gene Regulatory Networks Gene Expression Profiling Computational Biology Gene Expression Regulation, Neoplastic
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THE SAFETY AND EFFICACY OF ROBOT-ASSISTED STEREOTACTIC BIOPSY FOR BRAIN GLIOMA: EARLIEST INSTITUTIONAL EXPERIENCES AND EVALUATION OF LITERATURE.

Acta clinica Croatica Jun 1, 2021
Robot-assisted brain tumor biopsy is becoming one of the most important innovative technologies in neurosurgical practice. The idea behind its engagement is to advance the safety and efficacy of the biopsy procedure, which is much in demand when plan...
Humans Brain Brain Neoplasms Biopsy Glioma Robotics Glioblastoma
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Challenges in Building of Deep Learning Models for Glioblastoma Segmentation: Evidence from Clinical Data.

Studies in health technology and informatics May 27, 2021
In this article, we compare the performance of a state-of-the-art segmentation network (UNet) on two different glioblastoma (GB) segmentation datasets. Our experiments show that the same training procedure yields almost twice as bad results on the re...
Deep Learning Humans Magnetic Resonance Imaging Algorithms Retrospective Studies Glioblastoma
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Differentiation of rare brain tumors through unsupervised machine learning: Clinical significance of in-depth methylation and copy number profiling illustrated through an unusual case of IDH wildtype glioblastoma.

Clinical neuropathology Jan 1, 2021
Methylation profiling has become a mainstay in brain tumor diagnostics since the introduction of the first publicly available classification tool by the German Cancer Research Center in 2017. We demonstrate the capability of this system through an ex...
Adult DNA Copy Number Variations Humans Brain Neoplasms Unsupervised Machine Learning Female DNA Methylation Glioblastoma
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