MRI-Based Radiomics and Radiogenomics in the Management of Low-Grade Gliomas: Evaluating the Evidence for a Paradigm Shift.
brain tumors
low-grade glioma
machine learning
radiogenomics
radiomics
Journal
Journal of clinical medicine
ISSN: 2077-0383
Titre abrégé: J Clin Med
Pays: Switzerland
ID NLM: 101606588
Informations de publication
Date de publication:
01 Apr 2021
01 Apr 2021
Historique:
received:
28
02
2021
revised:
23
03
2021
accepted:
25
03
2021
entrez:
30
4
2021
pubmed:
1
5
2021
medline:
1
5
2021
Statut:
epublish
Résumé
Low-grade gliomas (LGGs) are tumors that affect mostly adults. These neoplasms are comprised mainly of oligodendrogliomas and diffuse astrocytomas. LGGs remain vexing to current management and therapeutic modalities although they exhibit more favorable survival rates compared with high-grade gliomas (HGGs). The specific genetic subtypes that these tumors exhibit result in variable clinical courses and the need to involve multidisciplinary teams of neurologists, epileptologists, neurooncologists and neurosurgeons. Currently, the diagnosis of an LGG pivots mainly around the preliminary radiological findings and the subsequent definitive surgical diagnosis (via surgical sampling). The introduction of radiomics as a high throughput quantitative imaging technique that allows for improved diagnostic, prognostic and predictive indices has created more interest for such techniques in cancer research and especially in neurooncology (MRI-based classification of LGGs, predicting Isocitrate dehydrogenase (
Identifiants
pubmed: 33915813
pii: jcm10071411
doi: 10.3390/jcm10071411
pmc: PMC8036428
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
Subventions
Organisme : Hillman cancer center
ID : startup funding from The University of Pittsburgh Hillman Cancer Center
Organisme : the Hillman Cancer Center's
ID : NCI Cancer Center Support Grant (CCSG) P30CA047904
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