Magnetic resonance imaging and deoxyribonucleic acid methylation–based radiogenomic models for survival risk stratification of glioblastoma
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Biomedical Engineering
Link
https://link.springer.com/content/pdf/10.1007/s11517-023-02971-3.pdf
Reference29 articles.
1. Beig N, Bera K, Prasanna P, Antunes J, Correa R, Singh S et al (2020) Radiogenomic-based survival risk stratification of tumor habitat on Gd-T1w MRI is associated with biological processes in glioblastoma. Clin Cancer Res 26(8):1866–1876. https://doi.org/10.1158/1078-0432.CCR-19-2556
2. Zhang X, Lu H, Tian Q, Feng N, Yin L, Xu X et al (2019) A radiomics nomogram based on multiparametric MRI might stratify glioblastoma patients according to survival. Eur Radiol 29(10):5528–5538. https://doi.org/10.1007/s00330-019-06069-z
3. X. Jia, Y. Zhai, D. Song, Y. Wang, S. Wei, F. Yang, et al. (2022) A multiparametric MRI-based radiomics nomogram for preoperative prediction of survival stratification in glioblastoma patients with standard treatment. Frontiers in Oncology 12. https://doi.org/10.3389/fonc.2022.758622
4. J. Lao, Y. Chen, Z.-C. Li, Q. Li, J. Zhang, J. Liu, et al. (2017) A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme. Scientific Reports 7(1). https://doi.org/10.1038/s41598-017-10649-8
5. Louis DN, Perry A, Wesseling P, Brat DJ, Cree IA, Figarella-Branger D et al (2021) The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro Oncol 23(8):1231–1251. https://doi.org/10.1093/neuonc/noab106
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