AIMC Topic: Glioblastoma

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Differentiation between treatment-related changes and progressive disease in patients with high grade brain tumors using support vector machine classification based on DCE MRI.

Journal of neuro-oncology Jan 11, 2016
Differentiation between treatment-related changes and progressive disease (PD) remains a major clinical challenge in the follow-up of patients with high grade brain tumors. The aim of this study was to differentiate between treatment-related changes ...
Glioblastoma Neoplasm Grading Combined Modality Therapy Tissue Distribution Tumor Burden Image Interpretation, Computer-Assisted Longitudinal Studies Contrast Media Magnetic Resonance Imaging Middle Aged Disease Progression Humans Support Vector Machine Prognosis Female Aged Young Adult Brain Neoplasms Male Adult Survival Rate
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Evaluation of data discretization methods to derive platform independent isoform expression signatures for multi-class tumor subtyping.

BMC genomics Nov 10, 2015
BACKGROUND: Many supervised learning algorithms have been applied in deriving gene signatures for patient stratification from gene expression data. However, transferring the multi-gene signatures from one analytical platform to another without loss o...
Glioblastoma Gene Expression Profiling RNA Isoforms Computational Biology Cluster Analysis Humans Machine Learning Algorithms
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Automated Grading of Gliomas using Deep Learning in Digital Pathology Images: A modular approach with ensemble of convolutional neural networks.

AMIA ... Annual Symposium proceedings. AMIA Symposium Nov 5, 2015
Brain glioma is the most common primary malignant brain tumors in adults with different pathologic subtypes: Lower Grade Glioma (LGG) Grade II, Lower Grade Glioma (LGG) Grade III, and Glioblastoma Multiforme (GBM) Grade IV. The survival and treatment...
Electronic Data Processing Humans Image Processing, Computer-Assisted Neural Networks, Computer Glioma Glioblastoma Machine Learning Brain Neoplasms
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Artificial intelligence applications in the screening and classification of glioblastoma.

Journal of neurosurgical sciences Aug 1, 2025
Glioblastoma is the most aggressive primary brain tumor, with poor prognosis following initial identification. Current diagnostic methods, including neuroimaging and molecular pathology, face several limitations in tumor delineation, differentiation ...
Artificial Intelligence Humans Brain Neoplasms Machine Learning Glioblastoma
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GBMPurity: A machine learning tool for estimating glioblastoma tumor purity from bulk RNA-sequencing data.

Neuro-oncology Jul 30, 2025
BACKGROUND: Glioblastoma (GBM) presents a significant clinical challenge due to its aggressive nature and extensive heterogeneity. Tumor purity, the proportion of malignant cells within a tumor, is an important covariate for understanding the disease...
Brain Neoplasms Sequence Analysis, RNA Deep Learning Humans RNA-Seq Glioblastoma Machine Learning Biomarkers, Tumor
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Evaluating an information theoretic approach for selecting multimodal data fusion methods.

Journal of biomedical informatics Jul 1, 2025
OBJECTIVE: Interest has grown in combining radiology, pathology, genomic, and clinical data to improve the accuracy of diagnostic and prognostic predictions toward precision health. However, most existing works choose their datasets and modeling appr...
Humans Algorithms Prognosis Medical Informatics Carcinoma, Non-Small-Cell Lung Male Information Theory Glioblastoma Lung Neoplasms Prostatic Neoplasms Genomics
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Multi-class brain malignant tumor diagnosis in magnetic resonance imaging using convolutional neural networks.

Brain research bulletin Jun 1, 2025
Glioblastoma (GBM), primary central nervous system lymphoma (PCNSL), and brain metastases (BM) are common malignant brain tumors with similar radiological features, while the accurate and non-invasive dialgnosis is essential for selecting appropriate...
Middle Aged Deep Learning Humans Convolutional Neural Networks Adult Glioblastoma Brain Neoplasms Neural Networks, Computer Lymphoma Male Female Magnetic Resonance Imaging Aged
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Multiple deep learning models based on MRI images in discriminating glioblastoma from solitary brain metastases: a multicentre study.

BMC medical imaging May 19, 2025
OBJECTIVE: Development of a deep learning model for accurate preoperative identification of glioblastoma and solitary brain metastases by combining multi-centre and multi-sequence magnetic resonance images and comparison of the performance of differe...
Male Middle Aged Deep Learning Aged Adult Brain Neoplasms Image Interpretation, Computer-Assisted Magnetic Resonance Imaging Diagnosis, Differential Glioblastoma Humans Retrospective Studies Female
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A self-supervised multimodal deep learning approach to differentiate post-radiotherapy progression from pseudoprogression in glioblastoma.

Scientific reports May 17, 2025
Accurate differentiation of pseudoprogression (PsP) from True Progression (TP) following radiotherapy (RT) in glioblastoma patients is crucial for optimal treatment planning. However, this task remains challenging due to the overlapping imaging chara...
Magnetic Resonance Imaging Glioblastoma Radiotherapy Planning, Computer-Assisted Middle Aged Disease Progression Female Deep Learning Humans Brain Neoplasms Male
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Automatic Segmentation of Histopathological Glioblastoma Whole-Slide Images Utilizing MONAI.

Studies in health technology and informatics May 15, 2025
Manual segmentation of histopathological images is both resource-intensive and prone to human error, particularly when dealing with challenging tumor types like Glioblastoma (GBM), an aggressive and highly heterogeneous brain tumor. The fuzzy borders...
Humans Glioblastoma Image Interpretation, Computer-Assisted Artificial Intelligence Brain Neoplasms
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