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Clinical Feature Ranking Based on Ensemble Machine Learning Reveals Top Survival Factors for Glioblastoma Multiforme
Glioblastoma multiforme (GM) is a malignant tumor of the central nervous system considered to be highly aggressive and often carrying a terrible...
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ASE-Net for Segmentation of Post-Operative Glioblastoma and Patient-Specific Fine-Tuning for Segmentation Refinement of Follow-Up MRI Scans
Volumetric quantification of tumors is usually done manually by radiologists requiring precious medical time and suffering from inter-observer...
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Deep learning-based algorithm for postoperative glioblastoma MRI segmentation: a promising new tool for tumor burden assessment
ObjectiveClinical and surgical decisions for glioblastoma patients depend on a tumor imaging-based evaluation. Artificial Intelligence (AI) can be...
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3-D Attention-SEV-Net for Segmentation of Post-operative Glioblastoma with Interactive Correction of Over-Segmentation
Accurate localization and volumetric quantification of post-operative glioblastoma are of profound importance for clinical applications like...
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Statistical and Bioinformatics Model to Identify the Influential Genes and Comorbidities of Glioblastoma
Glioblastoma (GBM) is the most common fatal cancer whose median survival time is estimated to be 12 to 18 months. GBM occurs in the frontal and...
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A Deep Learning Approach to Glioblastoma Radiogenomic Classification Using Brain MRI
A malignant brain tumor known as a glioblastoma is an extremely life-threatening condition. It has been proven that the existence of a specific...
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Multi-plane UNet++ Ensemble for Glioblastoma Segmentation
Glioblastoma multiforme (grade four glioma, GBM) is the most aggressive malignant tumor in the brain and usually treated by combined surgery, chemo-...
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Adaptive Unsupervised Learning with Enhanced Feature Representation for Intra-tumor Partitioning and Survival Prediction for Glioblastoma
Glioblastoma is profoundly heterogeneous in regional microstructure and vasculature. Characterizing the spatial heterogeneity of glioblastoma could...
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Synthesis of Glioblastoma Segmentation Data Using Generative Adversarial Network
Background: The application of machine learning and deep learning techniques in medical imaging encounters a significant limitation due to the...
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Overall Survival Time Prediction of Glioblastoma on Preoperative MRI Using Lesion Network Map**
Glioblastoma (GBM) is the most aggressive malignant brain tumor. Its poor survival rate highlights the pressing need to adopt easily accessible,...
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DeepDepth: Prediction of O(6)-methylguanine-DNA methyltransferase genotype in glioblastoma patients using multimodal representation learning based on deep feature fusion
Representation learning aims to extract meaningful features from medical images that are often multimodal, i.e., captured using multiple imaging...
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Regularized Weight Aggregation in Networked Federated Learning for Glioblastoma Segmentation
In federated learning (FL), the global model at the server requires an efficient mechanism for weight aggregation and a systematic strategy for...
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Extracting Radiomic features from pre-operative and segmented MRI scans improved survival prognosis of glioblastoma Multiforme patients through machine learning: a retrospective study
The combination of radiomics and artificial intelligence has emerged as a strong technique for building predictive models in radiology. This study...
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Tumor antigens and immune subtypes of glioblastoma: the fundamentals of mRNA vaccine and individualized immunotherapy development
PurposeGlioblastoma (GBM) is the most common primary brain tumor in adults and is notorious for its lethality. Given its limited therapeutic measures...
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GLIMPSE: a glioblastoma prognostication model using ensemble learning—a surveillance, epidemiology, and end results study
PurposeGlioblastoma is one of the most common and aggressive brain tumors in the world with a poor prognosis. A glioblastoma prognostication model...
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Estimating Glioblastoma Biophysical Growth Parameters Using Deep Learning Regression
Glioblastoma (GBM) is arguably the most aggressive, infiltrative, and heterogeneous type of adult brain tumor. Biophysical modeling of GBM growth has...
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Prediction of O-6-methylguanine-DNA methyltransferase and overall survival of the patients suffering from glioblastoma using MRI-based hybrid radiomics signatures in machine and deep learning framework
O-6-methylguanine-DNA methyltransferase (MGMT) is one of the most salient gene promoters that correlates with the effectiveness of standard therapy...
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Prediction of MGMT Methylation Status of Glioblastoma Using Radiomics and Latent Space Shape Features
In this paper we propose a method for predicting the status of MGMT promoter methylation in high-grade gliomas. From the available MR images, we...
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Towards Population-Based Histologic Stain Normalization of Glioblastoma
Glioblastoma (‘GBM’) is the most aggressive type of primary malignant adult brain tumor, with very heterogeneous radiographic, histologic, and...
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Overall Survival Prediction for Glioblastoma on Pre-treatment MRI Using Robust Radiomics and Priors
Patients with Glioblastoma multiforme (GBM) have a very low overall survival (OS) time, due to the rapid growth an invasiveness of this brain tumor....