A review of radiomics and deep predictive modeling in glioma characterization
Recent developments in glioma categorization based on biological genotypes and
application of computational machine learning or deep learning based predictive models …
application of computational machine learning or deep learning based predictive models …
Radiomics for precision medicine in glioblastoma
Introduction Being the most common primary brain tumor, glioblastoma presents as an
extremely challenging malignancy to treat with dismal outcomes despite treatment. Varying …
extremely challenging malignancy to treat with dismal outcomes despite treatment. Varying …
MRI-based radiomics and radiogenomics in the management of low-grade gliomas: evaluating the evidence for a paradigm shift
Low-grade gliomas (LGGs) are tumors that affect mostly adults. These neoplasms are
comprised mainly of oligodendrogliomas and diffuse astrocytomas. LGGs remain vexing to …
comprised mainly of oligodendrogliomas and diffuse astrocytomas. LGGs remain vexing to …
Applications of radiomics and radiogenomics in high-grade gliomas in the era of precision medicine
Simple Summary Radiomics and radiogenomics offer new insight into high-grade glioma
biology, as well as into glioma behavior in response to standard therapies. In this article, we …
biology, as well as into glioma behavior in response to standard therapies. In this article, we …
Glioma radiogenomics and artificial intelligence: road to precision cancer medicine
A Mahajan, A Sahu, R Ashtekar, T Kulkarni, S Shukla… - Clinical radiology, 2023 - Elsevier
Radiogenomics refers to the study of the relationship between imaging phenotypes and
gene expression patterns/molecular characteristics, which might allow improved diagnosis …
gene expression patterns/molecular characteristics, which might allow improved diagnosis …
Machine learning–based radiomics for molecular subtyping of gliomas
Purpose: The new classification announced by the World Health Organization in 2016
recognized five molecular subtypes of diffuse gliomas based on isocitrate dehydrogenase …
recognized five molecular subtypes of diffuse gliomas based on isocitrate dehydrogenase …
Artificial intelligence for radiomics; diagnostic biomarkers for neuro-oncology
Recent advances in medical image analysis have been made to improve our understanding
of how disease develops, behaves, and responds to treatment. Magnetic resonance imaging …
of how disease develops, behaves, and responds to treatment. Magnetic resonance imaging …
Machine learning assisted DSC-MRI radiomics as a tool for glioma classification by grade and mutation status
CH Sudre, J Panovska-Griffiths, E Sanverdi… - BMC medical informatics …, 2020 - Springer
Background Combining MRI techniques with machine learning methodology is rapidly
gaining attention as a promising method for staging of brain gliomas. This study assesses …
gaining attention as a promising method for staging of brain gliomas. This study assesses …
A fully automated artificial intelligence method for non-invasive, imaging-based identification of genetic alterations in glioblastomas
E Calabrese, JE Villanueva-Meyer, S Cha - Scientific reports, 2020 - nature.com
Glioblastoma is the most common malignant brain parenchymal tumor yet remains
challenging to treat. The current standard of care—resection and chemoradiation—is limited …
challenging to treat. The current standard of care—resection and chemoradiation—is limited …
Quantitative magnetic resonance imaging and radiogenomic biomarkers for glioma characterisation: a systematic review
Objective: The diversity of tumour characteristics among glioma patients, even within same
tumour grade, is a big challenge for disease outcome prediction. A possible approach for …
tumour grade, is a big challenge for disease outcome prediction. A possible approach for …