AIMC Topic: Glioma
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A data-centric machine learning approach to improve prediction of glioma grades using low-imbalance TCGA data.
Scientific reports
Jul 26, 2024
Accurate prediction and grading of gliomas play a crucial role in evaluating brain tumor progression, assessing overall prognosis, and treatment planning. In addition to neuroimaging techniques, identifying molecular biomarkers that can guide the dia...
Deep learning-based IDH1 gene mutation prediction using histopathological imaging and clinical data.
Computers in biology and medicine
Jul 21, 2024
In the field of histopathology, many studies on the classification of whole slide images (WSIs) using artificial intelligence (AI) technology have been reported. We have studied the disease progression assessment of glioma. Adult-type diffuse gliomas...
An XAI-enhanced efficientNetB0 framework for precision brain tumor detection in MRI imaging.
Journal of neuroscience methods
Jul 20, 2024
BACKGROUND: Accurately diagnosing brain tumors from MRI scans is crucial for effective treatment planning. While traditional methods heavily rely on radiologist expertise, the integration of AI, particularly Convolutional Neural Networks (CNNs), has ...
Applications of machine learning to MR imaging of pediatric low-grade gliomas.
Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery
Jul 8, 2024
INTRODUCTION: Machine learning (ML) shows promise for the automation of routine tasks related to the treatment of pediatric low-grade gliomas (pLGG) such as tumor grading, typing, and segmentation. Moreover, it has been shown that ML can identify cru...
Detection and Segmentation of Glioma Tumors Utilizing a UNet Convolutional Neural Network Approach with Non-Subsampled Shearlet Transform.
Journal of computational biology : a journal of computational molecular cell biology
Jun 27, 2024
The prompt and precise identification and delineation of tumor regions within glioma brain images are critical for mitigating the risks associated with this life-threatening ailment. In this study, we employ the UNet convolutional neural network (CNN...
Breaking new ground: can artificial intelligence and machine learning transform papillary glioneuronal tumor diagnosis?
Neurosurgical review
Jun 7, 2024
Papillary glioneuronal tumors (PGNTs), classified as Grade I by the WHO in 2016, present diagnostic challenges due to their rarity and potential for malignancy. Xiaodan Du et al.'s recent study of 36 confirmed PGNT cases provides critical insights in...
An effective ensemble learning approach for classification of glioma grades based on novel MRI features.
Scientific reports
May 25, 2024
The preoperative diagnosis of brain tumors is important for therapeutic planning as it contributes to the tumors' prognosis. In the last few years, the development in the field of artificial intelligence and machine learning has contributed greatly t...
Predicting post-surgical functional status in high-grade glioma with resting state fMRI and machine learning.
Journal of neuro-oncology
May 24, 2024
PURPOSE: High-grade glioma (HGG) is the most common and deadly malignant glioma of the central nervous system. The current standard of care includes surgical resection of the tumor, which can lead to functional and cognitive deficits. The aim of this...
Meta-Analysis of the Efficacy of Raman Spectroscopy and Machine-Learning-Based Identification of Glioma Tissue.
World neurosurgery
May 23, 2024
Intraoperative Raman spectroscopy (RS) has been identified as a potential tool for surgeons to rapidly and noninvasively differentiate between diseased and normal tissue. Since the previous meta-analysis on the subject was published in 2016, improvem...
Deformable registration of magnetic resonance images using unsupervised deep learning in neuro-/radiation oncology.
Radiation oncology (London, England)
May 21, 2024
PURPOSE: Accurate deformable registration of magnetic resonance imaging (MRI) scans containing pathologies is challenging due to changes in tissue appearance. In this paper, we developed a novel automated three-dimensional (3D) convolutional U-Net ba...
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