Deep learning approaches to non-invasively assess molecular features of gliomas
- PMID: 35029684
- PMCID: PMC8972261
- DOI: 10.1093/neuonc/noab304
Deep learning approaches to non-invasively assess molecular features of gliomas
Comment on
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Improving the noninvasive classification of glioma genetic subtype with deep learning and diffusion-weighted imaging.Neuro Oncol. 2022 Apr 1;24(4):639-652. doi: 10.1093/neuonc/noab238. Neuro Oncol. 2022. PMID: 34653254 Free PMC article.
References
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- Louis DN, Perry A, Reifenberger G, et al. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol. 2016;131(6):803–820. - PubMed
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- van der Voort SR, Incekara F, Wijnenga MMJ, et al. Predicting the 1p/19q codeletion status of presumed low-grade glioma with an externally validated machine learning algorithm. Clin Cancer Res. 2019;25(24):7455–7462. - PubMed
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