[HTML][HTML] Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology
Medical image processing and analysis (also known as Radiomics) is a rapidly growing
discipline that maps digital medical images into quantitative data, with the end goal of …
discipline that maps digital medical images into quantitative data, with the end goal of …
Emerging applications of artificial intelligence in neuro-oncology
Due to the exponential growth of computational algorithms, artificial intelligence (AI)
methods are poised to improve the precision of diagnostic and therapeutic methods in …
methods are poised to improve the precision of diagnostic and therapeutic methods in …
Deep-learning convolutional neural networks accurately classify genetic mutations in gliomas
P Chang, J Grinband, BD Weinberg… - American Journal …, 2018 - Am Soc Neuroradiology
BACKGROUND AND PURPOSE: The World Health Organization has recently placed new
emphasis on the integration of genetic information for gliomas. While tissue sampling …
emphasis on the integration of genetic information for gliomas. While tissue sampling …
Radiogenomics: a key component of precision cancer medicine
Z Liu, T Duan, Y Zhang, S Weng, H Xu, Y Ren… - British Journal of …, 2023 - nature.com
Radiogenomics, focusing on the relationship between genomics and imaging phenotypes,
has been widely applied to address tumour heterogeneity and predict immune …
has been widely applied to address tumour heterogeneity and predict immune …
Residual deep convolutional neural network predicts MGMT methylation status
Predicting methylation of the O6-methylguanine methyltransferase (MGMT) gene status
utilizing MRI imaging is of high importance since it is a predictor of response and prognosis …
utilizing MRI imaging is of high importance since it is a predictor of response and prognosis …
MRI-based deep-learning method for determining glioma MGMT promoter methylation status
CGB Yogananda, BR Shah… - American Journal …, 2021 - Am Soc Neuroradiology
Editorial expression of concern: In the May 2021 edition, the American Journal of
Neuroradilogy published the article “MRI-Based Deep-Learning Method for Determining …
Neuroradilogy published the article “MRI-Based Deep-Learning Method for Determining …
Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review
Glioma is the most common primary intraparenchymal tumor of the brain and the 5-year
survival rate of high-grade glioma is poor. Magnetic resonance imaging (MRI) is essential for …
survival rate of high-grade glioma is poor. Magnetic resonance imaging (MRI) is essential for …
Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: a multicentre study
Objectives To build a reliable radiomics model from multiregional and multiparametric
magnetic resonance imaging (MRI) for pretreatment prediction of O 6-methylguanine-DNA …
magnetic resonance imaging (MRI) for pretreatment prediction of O 6-methylguanine-DNA …
Fused deep learning paradigm for the prediction of o6-methylguanine-DNA methyltransferase genotype in glioblastoma patients: a neuro-oncological investigation
Abstract Background The O6-methylguanine-DNA methyltransferase (MGMT) is a
deoxyribonucleic acid (DNA) repairing enzyme that has been established as an essential …
deoxyribonucleic acid (DNA) repairing enzyme that has been established as an essential …
Impact of image preprocessing on the scanner dependence of multi-parametric MRI radiomic features and covariate shift in multi-institutional glioblastoma datasets
Recent advances in radiomics have enhanced the value of medical imaging in various
aspects of clinical practice, but a crucial component that remains to be investigated further is …
aspects of clinical practice, but a crucial component that remains to be investigated further is …