Ostrom QT, Gittleman H, Liao P et al (2017) CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2010-2014. Neuro Oncol 19:v1–v88. https://doi.org/10.1093/neuonc/nox158
Wen PY, Kesari S (2008) Malignant gliomas in adults. N Engl J Med 359:492–507. https://doi.org/10.1056/NEJMra0708126
Van Meir EG, Hadjipanayis CG, Norden AD, Shu HK, Wen PY, Olson JJ (2010) Exciting new advances in neuro-oncology: the avenue to a cure for malignant glioma. CA Cancer J Clin 60:166–193. https://doi.org/10.3322/caac.20069
[+]
Ostrom QT, Gittleman H, Liao P et al (2017) CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2010-2014. Neuro Oncol 19:v1–v88. https://doi.org/10.1093/neuonc/nox158
Wen PY, Kesari S (2008) Malignant gliomas in adults. N Engl J Med 359:492–507. https://doi.org/10.1056/NEJMra0708126
Van Meir EG, Hadjipanayis CG, Norden AD, Shu HK, Wen PY, Olson JJ (2010) Exciting new advances in neuro-oncology: the avenue to a cure for malignant glioma. CA Cancer J Clin 60:166–193. https://doi.org/10.3322/caac.20069
Louis DN, Perry A, Reifenberger G et al (2016) The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 131:803–820. https://doi.org/10.1007/s00401-016-1545-1
Stupp R, Hegi ME, Mason WP et al (2009) Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase III study: 5-year analysis of the EORTC-NCIC trial. Lancet Oncol 10:459–466. https://doi.org/10.1016/S1470-2045(09)70025-7
Hegi ME, Diserens A-C, Gorlia T et al (2005) MGMT gene silencing and benefit from temozolomide in glioblastoma. N Engl J Med 352:997–1003. https://doi.org/10.1056/NEJMoa043331
Gorlia T, van den Bent MJ, Hegi ME et al (2008) Nomograms for predicting survival of patients with newly diagnosed glioblastoma: prognostic factor analysis of EORTC and NCIC trial 26981-22981/CE.3. Lancet Oncol 9:29–38. https://doi.org/10.1016/S1470-2045(07)70384-4
Rivera AL, Pelloski CE, Gilbert MR et al (2010) MGMT promoter methylation is predictive of response to radiotherapy and prognostic in the absence of adjuvant alkylating chemotherapy for glioblastoma. Neuro Oncol 12:116–121. https://doi.org/10.1093/neuonc/nop020
Russell SM, Elliott R, Forshaw D, Golfinos JG, Nelson PK, Kelly PJ (2009) Glioma vascularity correlates with reduced patient survival and increased malignancy. Surg Neurol 72:242–246; discussion 246-247. https://doi.org/10.1016/j.surneu.2008.11.012
Batchelor TT, Gerstner ER, Emblem KE et al (2013) Improved tumor oxygenation and survival in glioblastoma patients who show increased blood perfusion after cediranib and chemoradiation. Proc Natl Acad Sci U S A 110:19059–19064. https://doi.org/10.1073/pnas.1318022110
Ulyte A, Katsaros VK, Liouta E et al (2016) Prognostic value of preoperative dynamic contrast-enhanced MRI perfusion parameters for high-grade glioma patients. Neuroradiology 58:1197–1208. https://doi.org/10.1007/s00234-016-1741-7
Yoo R-E, Yun TJ, Hwang I et al (2020) Arterial spin labeling perfusion-weighted imaging aids in prediction of molecular biomarkers and survival in glioblastomas. Eur Radiol 30:1202–1211. https://doi.org/10.1007/s00330-019-06379-2
Fuster-Garcia E, Juan-Albarracín J, García-Ferrando GA et al (2018) Improving the estimation of prognosis for glioblastoma patients by MR based hemodynamic tissue signatures. NMR Biomed 31:e4006. https://doi.org/10.1002/nbm.4006
Hou BL, Wen S, Katsevman GA et al (2018) Magnetic resonance imaging parameters and their impact on survival of patients with glioblastoma: tumor perfusion predicts survival. World Neurosurg. https://doi.org/10.1016/j.wneu.2018.12.085
Juan-Albarracín J, Fuster-Garcia E, Pérez-Girbés A et al (2018) Glioblastoma: vascular habitats detected at preoperative dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging predict survival. Radiology 287:944–954. https://doi.org/10.1148/radiol.2017170845
Chang Y-CC, Ackerstaff E, Tschudi Y et al (2017) Delineation of tumor habitats based on dynamic contrast enhanced MRI. Sci Rep 7:9746. https://doi.org/10.1038/s41598-017-09932-5
Álvarez-Torres MDM, Juan-Albarracín J, Fuster-Garcia E et al (2019) Robust association between vascular habitats and patient prognosis in glioblastoma: an international multicenter study. J Magn Reson Imaging. https://doi.org/10.1002/jmri.26958
Wu H, Tong H, Du X et al (2020) Vascular habitat analysis based on dynamic susceptibility contrast perfusion MRI predicts IDH mutation status and prognosis in high-grade gliomas. Eur Radiol. https://doi.org/10.1007/s00330-020-06702-2
Multicentre validation of how vascular biomarkers from tumor can predict the survival of the patient with glioblastoma - full text view - ClinicalTrials.gov. https://clinicaltrials.gov/ct2/show/NCT03439332. Accessed 24 Apr 2019
Brett M, Johnsrude IS, Owen AM (2002) The problem of functional localization in the human brain. Nat Rev Neurosci 3:243–249. https://doi.org/10.1038/nrn756
Manjón JV, Coupé P, Martí-Bonmatí L, Collins DL, Robles M (2010) Adaptive non-local means denoising of MR images with spatially varying noise levels. J Magn Reson Imaging 31:192–203. https://doi.org/10.1002/jmri.22003
Avants BB, Tustison NJ, Song G, Cook PA, Klein A, Gee JC (2011) A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 54:2033–2044. https://doi.org/10.1016/j.neuroimage.2010.09.025
Tustison NJ, Avants BB, Cook PA et al (2010) N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 29:1310–1320. https://doi.org/10.1109/TMI.2010.2046908
Juan-Albarracín J, Fuster-Garcia E, García-Ferrando GA, García-Gómez JM (2019) ONCOhabitats: a system for glioblastoma heterogeneity assessment through MRI. Int J Med Inform 128:53–61. https://doi.org/10.1016/j.ijmedinf.2019.05.002
Menze BH, Jakab A, Bauer S et al (2015) The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Trans Med Imaging 34:1993–2024. https://doi.org/10.1109/TMI.2014.2377694
Bakas S, Akbari H, Sotiras A et al (2017) Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci Data 4:170117. https://doi.org/10.1038/sdata.2017.117
Bakas S, Reyes M, Jakab A, et al (2018) Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. ArXiv181102629
Juan-Albarracín J, Fuster-Garcia E, del Mar Álvarez-Torres M et al (2020) ONCOhabitats glioma segmentation model. In: Crimi A, Bakas S (eds) Brain lesion: glioma, multiple sclerosis, stroke and traumatic brain injuries. Springer International Publishing, Cham, pp 295–303
Knutsson L, Ståhlberg F, Wirestam R (2010) Absolute quantification of perfusion using dynamic susceptibility contrast MRI: pitfalls and possibilities. MAGMA 23:1–21. https://doi.org/10.1007/s10334-009-0190-2
Boxerman JL, Schmainda KM, Weisskoff RM (2006) Relative cerebral blood volume maps corrected for contrast agent extravasation significantly correlate with glioma tumor grade, whereas uncorrected maps do not. AJNR Am J Neuroradiol 27:859–867
Little RJA, Rubin DB (2002) Statistical analysis with missing data. John Wiley & Sons, New York [etc.]
Harrell FE, Lee KL, Mark DB (1996) Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 15:361–387. https://doi.org/10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4
R Core Team (v3.6.0) R: a language and environment for statistical computing
Hempel J-M, Schittenhelm J, Klose U et al (2018) In vivo molecular profiling of human glioma. Clin Neuroradiol. https://doi.org/10.1007/s00062-018-0676-2
Chahal M, Xu Y, Lesniak D et al (2010) MGMT modulates glioblastoma angiogenesis and response to the tyrosine kinase inhibitor sunitinib. Neuro Oncol 12:822–833. https://doi.org/10.1093/neuonc/noq017
Moon W-J, Choi JW, Roh HG, Lim SD, Koh YC (2012) Imaging parameters of high grade gliomas in relation to the MGMT promoter methylation status: the CT, diffusion tensor imaging, and perfusion MR imaging. Neuroradiology 54:555–563. https://doi.org/10.1007/s00234-011-0947-y
Li Q, Guo J, Wang W, Wang D (2017) Relationship between MGMT gene expression and treatment effectiveness and prognosis in glioma. Oncol Lett 14:229–233. https://doi.org/10.3892/ol.2017.6123
Nguyen HN, Lie A, Li T et al (2017) Human TERT promoter mutation enables survival advantage from MGMT promoter methylation in IDH1 wild-type primary glioblastoma treated by standard chemoradiotherapy. Neuro Oncol 19:394–404. https://doi.org/10.1093/neuonc/now189
Li S, Jiang T, Li G, Wang Z (2008) Impact of p53 status to response of temozolomide in low MGMT expression glioblastomas: preliminary results. Neurol Res 30:567–570. https://doi.org/10.1179/174313208X297913
Molenaar RJ, Verbaan D, Lamba S et al (2014) The combination of IDH1 mutations and MGMT methylation status predicts survival in glioblastoma better than either IDH1 or MGMT alone. Neuro Oncol 16:1263–1273. https://doi.org/10.1093/neuonc/nou005
Li Z-C, Bai H, Sun Q et al (2018) Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: a multicentre study. Eur Radiol 28:3640–3650. https://doi.org/10.1007/s00330-017-5302-1
Jiang S, Rui Q, Wang Y et al (2018) Discriminating MGMT promoter methylation status in patients with glioblastoma employing amide proton transfer-weighted MRI metrics. Eur Radiol 28:2115–2123. https://doi.org/10.1007/s00330-017-5182-4
Emblem KE, Mouridsen K, Bjornerud A et al (2013) Vessel architectural imaging identifies cancer patient responders to anti-angiogenic therapy. Nat Med 19:1178–1183. https://doi.org/10.1038/nm.3289
[-]