Miller, Ok. D. et al. Mind and different central nervous system tumor statistics, 2021. CA Most cancers J. Clin. 71, 381–406 (2021).
Ostrom, Q. T. et al. CBTRUS statistical report: Major mind and different central nervous system tumors identified in the usa in 2015–2019. Neuro Oncol. 24, v1–v95 (2022).
Stupp, R. et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N. Engl. J. Med. 352, 987–996 (2005).
Reardon, D. A. & Wen, P. Y. Glioma in 2014: Unravelling tumour heterogeneity-implications for remedy. Nat. Rev. Clin. Oncol. 12, 69–70 (2015).
Mansouri, A. et al. MGMT promoter methylation standing testing to information remedy for glioblastoma: Refining the method primarily based on rising proof and present challenges. Neuro Oncol. 21, 167–178 (2019).
Hegi, M. E. et al. MGMT gene silencing and profit from temozolomide in glioblastoma. N. Engl. J. Med. 352, 997–1003 (2005).
Riemenschneider, M. J., Hegi, M. E. & Reifenberger, G. MGMT promoter methylation in malignant gliomas. Goal Oncol. 5, 161–165 (2010).
Wick, W. et al. MGMT testing–the challenges for biomarker-based glioma remedy. Nat. Rev. Neurol. 10, 372–385 (2014).
Iliadis, G. et al. Volumetric and MGMT parameters in glioblastoma sufferers: Survival evaluation. BMC Most cancers 12, 3 (2012).
Ellingson, B. M. et al. Probabilistic radiographic atlas of glioblastoma phenotypes. AJNR Am. J. Neuroradiol. 34, 533–540 (2013).
Smits, M. & van den Bent, M. J. Imaging correlates of grownup glioma genotypes. Radiology 284, 316–331 (2017).
Han, Y. et al. Structural and superior imaging in predicting MGMT promoter methylation of major glioblastoma: A area of curiosity primarily based evaluation. BMC Most cancers 18, 215 (2018).
Drabycz, S. et al. An evaluation of picture texture, tumor location, and MGMT promoter methylation in glioblastoma utilizing magnetic resonance imaging. Neuroimage 49, 1398–1405 (2010).
Hong, E. Ok. et al. Comparability of genetic profiles and prognosis of high-grade gliomas utilizing quantitative and qualitative MRI options: A give attention to G3 gliomas. Korean J. Radiol. 22, 233–242 (2021).
Hong, E. Ok. et al. Radiogenomics correlation between MR imaging options and main genetic profiles in glioblastoma. Eur. Radiol. 28, 4350–4361 (2018).
Gillies, R. J., Kinahan, P. E. & Hricak, H. Radiomics: Photos are greater than footage, they’re knowledge. Radiology 278, 563–577 (2016).
Lambin, P. et al. Radiomics: The bridge between medical imaging and personalised medication. Nat. Rev. Clin. Oncol. 14, 749–762 (2017).
Zhong, S. et al. Predicting glioblastoma molecular subtypes and prognosis with a multimodal mannequin integrating convolutional neural community, radiomics, and semantics. J. Neurosurg. https://doi.org/10.3171/2022.10.JNS22801 (2022).
van der Voort, S. R. et al. Mixed molecular subtyping, grading, and segmentation of glioma utilizing multi-task deep studying. Neuro Oncol. 25, 279–289 (2023).
Li, G. et al. An MRI radiomics method to foretell survival and tumour-infiltrating macrophages in gliomas. Mind 145, 1151–1161 (2022).
Li, Z. et al. Mixture of pre-treatment dynamic [(18)F]FET PET radiomics and traditional medical parameters for the survival stratification in sufferers with IDH-wildtype glioblastoma. Eur. J. Nucl. Med. Mol. Imaging 50, 535–545 (2023).
Kickingereder, P. et al. Giant-scale radiomic profiling of recurrent glioblastoma identifies an imaging predictor for stratifying anti-angiogenic remedy response. Clin. Most cancers Res. 22, 5765–5771 (2016).
Tian, Q. et al. Radiomics technique for glioma grading utilizing texture options from multiparametric MRI. J. Magn. Reson. Imaging 48, 1518–1528 (2018).
Artzi, M., Bressler, I. & Ben Bashat, D. Differentiation between glioblastoma, mind metastasis and subtypes utilizing radiomics evaluation. J. Magn. Reson. Imaging 50, 519–528 (2019).
Qian, Z. et al. Machine learning-based evaluation of magnetic resonance radiomics for the classification of gliosarcoma and glioblastoma. Entrance. Oncol. 11, 699789 (2021).
Pati, S. et al. Federated studying allows huge knowledge for uncommon most cancers boundary detection. Nat. Commun. 13, 7346 (2022).
Sloan, A. E. et al. Radiomics-based identification of peritumoral infiltration in de novo glioblastoma imaging presents targets amenable for potential focused prolonged resection: A neurosurgical survey. J. Clin. Oncol. 37, e13573–e13573 (2019).
Zhang, J. et al. Diffusion-weighted imaging and arterial spin labeling radiomics options could enhance differentiation between radiation-induced mind damage and glioma recurrence. Eur. Radiol. 33, 3332–3342 (2023).
Elshafeey, N. et al. Multicenter research demonstrates radiomic options derived from magnetic resonance perfusion photographs determine pseudoprogression in glioblastoma. Nat. Commun. 10, 3170 (2019).
Nicholson, J. G. & Wonderful, H. A. Diffuse glioma heterogeneity and its therapeutic implications. Most cancers Discov. 11, 575–590 (2021).
Li, Z. C. et al. Multiregional radiomics profiling from multiparametric MRI: Figuring out an imaging predictor of IDH1 mutation standing in glioblastoma. Most cancers Med. 7, 5999–6009 (2018).
Eoli, M. et al. Methylation of O6-methylguanine DNA methyltransferase and lack of heterozygosity on 19q and/or 17p are overlapping options of secondary glioblastomas with extended survival. Clin. Most cancers Res. 13, 2606–2613 (2007).
Kanas, V. G. et al. Studying MRI-based classification fashions for MGMT methylation standing prediction in glioblastoma. Comput. Strategies Applications Biomed. 140, 249–257 (2017).
Hu, L. S., Hawkins-Daarud, A., Wang, L., Li, J. & Swanson, Ok. R. Imaging of intratumoral heterogeneity in high-grade glioma. Most cancers Lett. 477, 97–106 (2020).
Kandalgaonkar, P. et al. Predicting IDH subtype of grade 4 astrocytoma and glioblastoma from tumor radiomic patterns extracted from multiparametric magnetic resonance photographs utilizing a machine studying method. Entrance. Oncol. 12, 879376 (2022).
Li, Z. C. et al. Multiregional radiomics options from multiparametric MRI for prediction of MGMT methylation standing in glioblastoma multiforme: A multicentre research. Eur. Radiol. 28, 3640–3650 (2018).
Wei, J. et al. A multi-sequence and habitat-based MRI radiomics signature for preoperative prediction of MGMT promoter methylation in astrocytomas with prognostic implication. Eur. Radiol. 29, 877–888 (2019).
Yogananda, C. G. B. et al. MRI-based deep-learning methodology for figuring out glioma MGMT promoter methylation standing. AJNR Am. J. Neuroradiol. 42, 845–852 (2021).
Baid, U. et al. The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Mind Tumor Segmentation and Radiogenomic Classification. https://arxiv.org/abs/2107.02314 (2021).
Kim, B. H. et al. Validation of MRI-based fashions to foretell MGMT promoter methylation in gliomas: BraTS 2021 radiogenomics problem. Cancers (Basel) 14, 4827 (2022).
Capuozzo, S., Gravina, M., Gatta, G., Marrone, S. & Sansone, C. A multimodal knowledge-based deep studying method for MGMT promoter methylation identification. J. Imaging 8, 321 (2022).
Faghani, S., Khosravi, B., Moassefi, M., Conte, G. M. & Erickson, B. J. A comparability of three totally different deep learning-based fashions to foretell the MGMT promoter methylation standing in glioblastoma utilizing mind MRI. J. Digit. Imaging 36, 837–846 (2023).
Saxena, S. et al. Fused deep studying paradigm for the prediction of o6-methylguanine-DNA methyltransferase genotype in glioblastoma sufferers: A neuro-oncological investigation. Comput. Biol. Med. 153, 106492 (2023).
He, J. et al. Multiparametric MR radiomics in mind glioma: Fashions comparation to foretell biomarker standing. BMC Med. Imaging 22, 137 (2022).
Huang, W. Y. et al. Radiological mannequin primarily based on the usual magnetic resonance sequences for detecting methylguanine methyltransferase methylation in glioma utilizing texture evaluation. Most cancers Sci. 112, 2835–2844 (2021).
Chen, X. et al. Automated prediction of MGMT standing in glioblastoma by way of deep learning-based MR picture evaluation. Biomed. Res. Int. 2020, 9258649 (2020).
Tan, Y. et al. Entire-tumor radiomics evaluation of DKI and DTI could enhance the prediction of genotypes for astrocytomas: A preliminary research. Eur. J. Radiol. 124, 108785 (2020).
Moon, W. J., Choi, J. W., Roh, H. G., Lim, S. D. & Koh, Y. C. Imaging parameters of excessive grade gliomas in relation to the MGMT promoter methylation standing: The CT, diffusion tensor imaging, and perfusion MR imaging. Neuroradiology 54, 555–563 (2012).
Louis, D. N. et al. The 2021 WHO classification of tumors of the central nervous system: A abstract. Neuro Oncol. 23, 1231–1251 (2021).
