How machine learning is powering neuroimaging to improve brain health
This report presents an overview of how machine learning is rapidly advancing clinical
translational imaging in ways that will aid in the early detection, prediction, and treatment of …
translational imaging in ways that will aid in the early detection, prediction, and treatment of …
Radiomics-based machine learning model for efficiently classifying transcriptome subtypes in glioblastoma patients from MRI
Background In the field of glioma, transcriptome subtypes have been considered as an
important diagnostic and prognostic biomarker that may help improve the treatment efficacy …
important diagnostic and prognostic biomarker that may help improve the treatment efficacy …
Machine learning application in glioma classification: review and comparison analysis
KR Bhatele, SS Bhadauria - Archives of Computational Methods in …, 2022 - Springer
This paper simply presents a state of the art survey among the machine learning based
approaches for the Glioma classification. As Glioma classification is a very challenging task …
approaches for the Glioma classification. As Glioma classification is a very challenging task …
Combining radiomics and deep convolutional neural network features from preoperative MRI for predicting clinically relevant genetic biomarkers in glioblastoma
Background Glioblastoma is the most common primary brain malignancy, yet treatment
options are limited, and prognosis remains guarded. Individualized tumor genetic …
options are limited, and prognosis remains guarded. Individualized tumor genetic …
Radiomics-based method for predicting the glioma subtype as defined by tumor grade, IDH mutation, and 1p/19q codeletion
Simple Summary In 2016, the World Health Organization (WHO) recommended the
incorporation of molecular parameters, in addition to histology, for an optimal definition of …
incorporation of molecular parameters, in addition to histology, for an optimal definition of …
Multimodal disentangled variational autoencoder with game theoretic interpretability for glioma grading
Effective fusion of multimodal magnetic resonance imaging (MRI) is of great significance to
boost the accuracy of glioma grading thanks to the complementary information provided by …
boost the accuracy of glioma grading thanks to the complementary information provided by …
Observing deep radiomics for the classification of glioma grades
K Kobayashi, M Miyake, M Takahashi, R Hamamoto - Scientific Reports, 2021 - nature.com
Deep learning is a promising method for medical image analysis because it can
automatically acquire meaningful representations from raw data. However, a technical …
automatically acquire meaningful representations from raw data. However, a technical …
Prediction of glioma grade using intratumoral and peritumoral radiomic features from multiparametric MRI images
The accurate prediction of glioma grade before surgery is essential for treatment planning
and prognosis. Since the gold standard (ie, biopsy) for grading gliomas is both highly …
and prognosis. Since the gold standard (ie, biopsy) for grading gliomas is both highly …
[HTML][HTML] Developing and validating a deep learning and radiomic model for glioma grading using multiplanar reconstructed magnetic resonance contrast-enhanced T1 …
J Ding, R Zhao, Q Qiu, J Chen, J Duan… - Quantitative imaging in …, 2022 - ncbi.nlm.nih.gov
Background Although surgical pathology or biopsy are considered the gold standard for
glioma grading, these procedures have limitations. This study set out to evaluate and …
glioma grading, these procedures have limitations. This study set out to evaluate and …
Reproducibility analysis of multi‐institutional paired expert annotations and radiomic features of the Ivy Glioblastoma Atlas Project (Ivy GAP) dataset
Purpose The availability of radiographic magnetic resonance imaging (MRI) scans for the Ivy
Glioblastoma Atlas Project (Ivy GAP) has opened up opportunities for development of …
Glioblastoma Atlas Project (Ivy GAP) has opened up opportunities for development of …