MRI-based deep learning techniques for the prediction of isocitrate dehydrogenase and 1p/19q status in grade 2-4 adult gliomas
Details
Publication Year 2023-08,Volume 67,Issue #5,Page 492-498
Journal Title
Journal of Medical Imaging and Radiation Oncology
Publication Type
Review
Abstract
Molecular biomarkers are becoming increasingly important in the classification of intracranial gliomas. While tissue sampling remains the gold standard, there is growing interest in the use of deep learning (DL) techniques to predict these markers. This narrative review with a systematic approach identifies and synthesises the current published data on DL techniques using conventional MRI sequences for predicting isocitrate dehydrogenase (IDH) and 1p/19q-codeletion status in World Health Organisation grade 2-4 gliomas. Three databases were searched for relevant studies. In all, 13 studies met the inclusion criteria after exclusions. Key results, limitations and discrepancies between studies were synthesised. High accuracy has been reported in some studies, but the existing literature has several limitations, including generally small cohort sizes, a paucity of studies with independent testing cohorts and a lack of studies assessing IDH and 1p/19q together. While DL shows promise as a non-invasive means of predicting glioma genotype, addressing these limitations in future research will be important for facilitating clinical translation.
Publisher
Wiley
Keywords
Adult; Humans; Isocitrate Dehydrogenase/genetics; *Brain Neoplasms/diagnostic imaging/genetics; *Deep Learning; Mutation; *Glioma/diagnostic imaging/genetics; Magnetic Resonance Imaging/methods; 1p/19q; glioma; isocitrate dehydrogenase; magnetic resonance imaging; radiogenomics
Department(s)
Cancer Imaging
PubMed ID
36919468
Open Access at Publisher's Site
https://doi.org/10.1111/1754-9485.13522
Terms of Use/Rights Notice
Refer to copyright notice on published article.


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