References
Ostrom QT, Gittleman H, Fulop J et al (2015) CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2008–2012. Neuro-Oncol 17(Suppl4):iv1–iv62. https://doi.org/10.1093/neuonc/nov189
Louis DN, Perry A, Wesseling P 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
Pafundi DH, Laack NN, Youland RS et al (2013) Biopsy validation of 18F-DOPA PET and biodistribution in gliomas for neurosurgical planning and radiotherapy target delineation: results of a prospective pilot study. Neuro Oncol 15(8):1058–1067. https://doi.org/10.1093/neuonc/not002
Ullrich R, Backes H, Li H et al (2008) Glioma proliferation as assessed by 3’-fluoro-3’-deoxy-L-thymidine positron emission tomography in patients with newly diagnosed high-grade glioma. Clin Cancer Res 14(7):2049–2055. https://doi.org/10.1158/1078-0432.CCR-07-1553
Backes H, Ullrich R, Neumaier B, Kracht L, Wienhard K, Jacobs AH (2009) Noninvasive quantification of 18F-FLT human brain PET for the assessment of tumour proliferation in patients with high-grade glioma. Eur J Nucl Med Mol Imaging 36(12):1960–1967. https://doi.org/10.1007/s00259-009-1244-4
Berzero G, Bellu L, Baldini C et al (2021) Sustained tumor control with MAPK inhibition in BRAF v600-mutant adult glial and glioneuronal tumors. Neurology 97(7):e673–e683. https://doi.org/10.1212/WNL.0000000000012330
Galldiks N, Niyazi M, Grosu AL et al (2021) Contribution of PET imaging to radiotherapy planning and monitoring in glioma patients—a report of the PET/RANO group. Neuro Oncol 23(6):881–893. https://doi.org/10.1093/neuonc/noab013
Law I, Albert NL, Arbizu J et al (2019) Joint EANM/EANO/RANO practice guidelines/SNMMI procedure standards for imaging of gliomas using PET with radiolabelled amino acids and [18F]FDG: version 1.0. Eur J Nucl Med Mol Imaging 46(3):540–557. https://doi.org/10.1007/s00259-018-4207-9
Niemeijer AN, Leung D, Huisman MC et al (2018) Whole body PD-1 and PD-L1 positron emission tomography in patients with non-small-cell lung cancer. Nat Commun 9(1):4664. https://doi.org/10.1038/s41467-018-07131-y
Herhaus P, Lipkova J, Lammer F et al (2020) CXCR4-targeted PET imaging of central nervous system B-cell lymphoma. J Nucl Med 61(12):1765–1771. https://doi.org/10.2967/jnumed.120.241703
Nowosielski M, Galldiks N, Iglseder S et al (2017) Diagnostic challenges in meningioma. Neuro Oncol 19(12):1588–1598. https://doi.org/10.1093/neuonc/nox101
Kalinina J, Carroll A, Wang L et al (2012) Detection of “oncometabolite” 2-hydroxyglutarate by magnetic resonance analysis as a biomarker of IDH1/2 mutations in glioma. J Mol Med 90(10):1161–1171. https://doi.org/10.1007/s00109-012-0888-x
Andronesi OC, Kim GS, Gerstner E et al (2012) Detection of 2-hydroxyglutarate in IDH-mutated glioma patients by in vivo spectral-editing and 2D correlation magnetic resonance spectroscopy. Sci Transl Med 4(116):116ra4. https://doi.org/10.1126/scitranslmed.3002693
Choi C, Ganji SK, DeBerardinis RJ et al (2012) 2-hydroxyglutarate detection by magnetic resonance spectroscopy in IDH-mutated patients with gliomas. Nat Med 18(4):624–629. https://doi.org/10.1038/nm.2682
Choi C, Raisanen JM, Ganji SK et al (2016) Prospective longitudinal analysis of 2-hydroxyglutarate magnetic resonance spectroscopy identifies broad clinical utility for the management of patients with IDH-mutant glioma. J Clin Oncol 34(33):4030–4039. https://doi.org/10.1200/JCO.2016.67.1222
Branzoli F, Di Stefano AL, Capelle L et al (2018) Highly specific determination of IDH status using edited in vivo magnetic resonance spectroscopy. Neuro Oncol 20(7):907–916. https://doi.org/10.1093/neuonc/nox214
de la Fuente MI, Young RJ, Rubel J et al (2016) Integration of 2-hydroxyglutarate-proton magnetic resonance spectroscopy into clinical practice for disease monitoring in isocitrate dehydrogenase-mutant glioma. Neuro Oncol 18(2):283–290. https://doi.org/10.1093/neuonc/nov307
Aerts HJWL, Velazquez ER, Leijenaar RTH et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006. https://doi.org/10.1038/ncomms5006
Kumar V, Gu Y, Basu S et al (2012) Radiomics: the process and the challenges. Magn Reson Imaging 30(9):1234–1248. https://doi.org/10.1016/j.mri.2012.06.010
Zhou M, Scott J, Chaudhury B et al (2018) Radiomics in brain tumor: image assessment, quantitative feature descriptors, and machine-learning approaches. AJNR Am J Neuroradiol 39(2):208–216. https://doi.org/10.3174/ajnr.A5391
Kickingereder P, Bonekamp D, Nowosielski M et al (2016) Radiogenomics of glioblastoma: machine learning-based classification of molecular characteristics by using multiparametric and multiregional MR imaging features. Radiology 281(3):907–918. https://doi.org/10.1148/radiol.2016161382
Tiwari P, Prasanna P, Wolansky L et al (2016) Computer-extracted texture features to distinguish cerebral radionecrosis from recurrent brain tumors on multiparametric MRI: a feasibility study. AJNR Am J Neuroradiol 37(12):2231–2236. https://doi.org/10.3174/ajnr.A4931
Akbari H, Macyszyn L, Da X et al (2016) Imaging surrogates of infiltration obtained via multiparametric imaging pattern analysis predict subsequent location of recurrence of glioblastoma. Neurosurgery 78(4):572–580. https://doi.org/10.1227/NEU.0000000000001202
Bakas S, Akbari H, Pisapia J et al (2017) In vivo detection of EGFRvIII in glioblastoma via perfusion magnetic resonance imaging signature consistent with deep peritumoral infiltration: the φ-index. Clin Cancer Res 23(16):4724–4734. https://doi.org/10.1158/1078-0432.CCR-16-1871
Han L, Wang S, Miao Y et al (2019) MRI texture analysis based on 3D tumor measurement reflects the IDH1 mutations in gliomas—a preliminary study. Eur J Radiol 112:169–179. https://doi.org/10.1016/j.ejrad.2019.01.025
Di Stefano AL, Picca A, Saragoussi E et al (2020) Clinical, molecular and radiomic profile of gliomas with FGFR3-TACC3 fusions. Neuro Oncol. https://doi.org/10.1093/neuonc/noaa121 (Published online May 15)
Sheller MJ, Edwards B, Reina GA et al (2020) Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Sci Rep 10(1):12598. https://doi.org/10.1038/s41598-020-69250-1
Funding
This study was not funded. Dr. Anna Luisa Di Stefano was supported by Premio Carla Russo 2021.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
Dr Di Stefano reports no disclosures. Pr Mansi reports no disclosures. Pr. Sanson reports no disclosures.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Di Stefano, A.L., Mansi, L. & Sanson, M. Non-invasive molecular diagnosis in gliomas with advanced imaging. Clin Transl Imaging 10, 567–569 (2022). https://doi.org/10.1007/s40336-022-00501-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40336-022-00501-z