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Brain Neoplasms

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Classification of speech arrests and speech impairments during awake craniotomy: a multi-databases analysis.

International journal of computer assisted radiology and surgery
PURPOSE: Awake craniotomy presents a unique opportunity to map and preserve critical brain functions, particularly speech, during tumor resection. The ability to accurately assess linguistic functions in real-time not only enhances surgical precision...

Enhancing brain tumor classification by integrating radiomics and deep learning features: A comprehensive study utilizing ensemble methods on MRI scans.

Journal of X-ray science and technology
BACKGROUND AND OBJECTIVE: This study aims to assess the effectiveness of combining radiomics features (RFs) with deep learning features (DFs) for classifying brain tumors-specifically Glioma, Meningioma, and Pituitary Tumor-using MRI scans and advanc...

UDA-GS: A cross-center multimodal unsupervised domain adaptation framework for Glioma segmentation.

Computers in biology and medicine
Gliomas are the most common and malignant form of primary brain tumors. Accurate segmentation and measurement from MRI are crucial for diagnosis and treatment. Due to the infiltrative growth pattern of gliomas, their labeling is very difficult. In tu...

Deep learning-based overall survival prediction in patients with glioblastoma: An automatic end-to-end workflow using pre-resection basic structural multiparametric MRIs.

Computers in biology and medicine
PURPOSE: Accurate and automated early survival prediction is critical for patients with glioblastoma (GBM) as their poor prognosis requires timely treatment decision-making. To address this need, we developed a deep learning (DL)-based end-to-end wor...

Prediction of Brain Cancer Occurrence and Risk Assessment of Brain Hemorrhage Using Hybrid Deep Learning Technique.

Cancer investigation
The prediction of brain cancer occurrence and risk assessment of brain hemorrhage using a hybrid deep learning (DL) technique is a critical area of research in medical imaging analysis. One prominent challenge in this field is the accurate identifica...

Medical Transformer: Universal Encoder for 3-D Brain MRI Analysis.

IEEE transactions on neural networks and learning systems
Transfer learning has attracted considerable attention in medical image analysis because of the limited number of annotated 3-D medical datasets available for training data-driven deep learning models in the real world. We propose Medical Transformer...

The G Protein-Coupled Receptor-Related Gene Signatures for Diagnosis and Prognosis in Glioblastoma: A Deep Learning Model Using RNA-Seq Data.

Asian Pacific journal of cancer prevention : APJCP
BACKGROUND: Glioblastoma (GBM) is the most aggressive cancer in the central nervous system in glial cells. Finding novel biomarkers in GBM offers numerous advantages that can contribute to early detection, personalized treatment, improved patient out...

A unique unsupervised enhanced intuitionistic fuzzy C-means for MR brain tissue segmentation.

Scientific reports
The human-brain is a vital and complicated organ within the body. Identifying brain-related diseases can be challenging. Typically, Magnetic Resonance Imaging (MRI) scanning methods are used to gain insights of the protected regions in the body. Brai...

Quality assessment of critical and non-critical domains of systematic reviews on artificial intelligence in gliomas using AMSTAR II: A systematic review.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
INTRODUCTION: Gliomas are the most common primary malignant intraparenchymal brain tumors with a dismal prognosis. With growing advances in artificial intelligence, machine learning and deep learning models are being utilized for preoperative, intrao...

A comprehensive neuroimaging review of the primary and metastatic brain tumors treated with immunotherapy: current status, and the application of advanced imaging approaches and artificial intelligence.

Frontiers in immunology
Cancer immunotherapy has emerged as a novel clinical therapeutic option for a variety of solid tumors over the past decades. The application of immunotherapy in primary and metastatic brain tumors continues to grow despite limitations due to the phys...