Advanced MR Techniques for Preoperative Glioma Characterization: Part 1
Jazyk angličtina Země Spojené státy americké Médium print-electronic
Typ dokumentu časopisecké články, přehledy, práce podpořená grantem, Research Support, N.I.H., Extramural
Grantová podpora
R01 CA264992
NCI NIH HHS - United States
Wellcome Trust - United Kingdom
203148/A/16/Z
Wellcome Trust - United Kingdom
U01 CA176110
NCI NIH HHS - United States
R01 CA255123
NCI NIH HHS - United States
PubMed
36866773
PubMed Central
PMC10946498
DOI
10.1002/jmri.28662
Knihovny.cz E-zdroje
- Klíčová slova
- GliMR 2.0, brain, contrasts, glioma, level of clinical validation, preoperative,
- MeSH
- difuzní magnetická rezonance MeSH
- gliom * diagnostické zobrazování chirurgie patologie MeSH
- lidé MeSH
- magnetická rezonanční spektroskopie metody MeSH
- magnetická rezonanční tomografie metody MeSH
- nádory mozku * diagnostické zobrazování chirurgie patologie MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- přehledy MeSH
- Research Support, N.I.H., Extramural MeSH
Preoperative clinical magnetic resonance imaging (MRI) protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation or lack thereof. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this first part, we discuss dynamic susceptibility contrast and dynamic contrast-enhanced MRI, arterial spin labeling, diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting. The second part of this review addresses magnetic resonance spectroscopy, chemical exchange saturation transfer, susceptibility-weighted imaging, MRI-PET, MR elastography, and MR-based radiomics applications. Evidence Level: 3 Technical Efficacy: Stage 2.
Brain Tumour Centre Erasmus MC Cancer Institute Rotterdam The Netherlands
Cancer Center Amsterdam Amsterdam The Netherlands
Christian Doppler Laboratory for MR Imaging Biomarkers Vienna Austria
Department of Bioengineering Imperial College London London UK
Department of Biophysics Medical College of Wisconsin Milwaukee Wisconsin USA
Department of Diagnostic and Interventional Radiology University Hospital Ulm Ulm Germany
Department of Diagnostic Sciences Ghent University Ghent Belgium
Department of Imaging Physics Delft University of Technology Delft The Netherlands
Department of Medical Imaging Ghent University Hospital Ghent Belgium
Department of Neurology Haaglanden Medical Center The Hague The Netherlands
Department of Neurology Leiden University Medical Center Leiden The Netherlands
Department of Neuroradiology Hospital Garcia de Orta Almada Portugal
Department of Neuroradiology King's College Hospital NHS Foundation Trust London UK
Department of Neurosurgery Medical University of Vienna Vienna Austria
Department of Neurosurgery St Anne's University Hospital Brno Brno Czech Republic
Department of Physics and Computational Radiology Oslo University Hospital Oslo Norway
Department of Physics University of Oslo Oslo Norway
Department of Radiology and Nuclear Medicine Erasmus MC Rotterdam The Netherlands
Department of Radiology Clínica Universidad de Navarra Pamplona Spain
Department of Radiology Leiden University Medical Center Leiden The Netherlands
Department of Radiology Stanford University Stanford California USA
Department of Radiotherapy and Imaging Institute of Cancer Research London UK
Electrical and Electronics Engineering Department Bogazici University Istanbul Istanbul Turkey
Faculty of Engineering and Design Atlantic Technological University Sligo Sligo Ireland
Faculty of Medicine Masaryk University Brno Czech Republic
Hospital Santa Luzia Rede D'Or São Luiz Brasília Brazil
IdiSNA Instituto de Investigación Sanitaria de Navarra Pamplona Spain
Institute of Biomedical Engineering Bogazici University Istanbul Istanbul Turkey
Institute of Biomedical Engineering Department of Engineering Science University of Oxford Oxford UK
Mathematical Modelling and Intelligent Systems for Health and Environment ATU Sligo Sligo Ireland
Medical Delta Foundation Delft The Netherlands
Medical Imaging Cluster Medical University of Vienna Vienna Austria
School of Biomedical Engineering and Imaging Sciences King's College London London UK
Stanford Cardiovascular Institute Stanford University Stanford California USA
TUM Neuroimaging Center Klinikum rechts der Isar Technical University of Munich Munich Germany
Zobrazit více v PubMed
Miller KD, Ostrom QT, Kruchko C, et al. Brain and other central nervous system tumor statistics, 2021. CA Cancer J Clin 2021;71:381‐406. PubMed
Louis DN, Perry A, Wesseling P, et al. The 2021 WHO classification of tumors of the central nervous system: A summary. Neuro Oncol 2021;23:1231‐1251. PubMed PMC
Horbinski C, Berger T, Packer RJ, Wen PY. Clinical implications of the 2021 edition of the WHO classification of central nervous system tumours. Nat Rev Neurol 2022;18:515‐529. PubMed
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;131:803‐820. PubMed
Ellingson BM, Bendszus M, Boxerman J, et al. Consensus recommendations for a standardized brain tumor imaging protocol in clinical trials. Neuro Oncol 2015;17:1188‐1198. PubMed PMC
Smits M. MRI biomarkers in neuro‐oncology. Nat Rev Neurol 2021;17:486‐500. PubMed
Villanueva‐Meyer JE, Mabray MC, Cha S. Current clinical brain tumor imaging. Neurosurgery 2017;81:397‐415. PubMed PMC
Eckel‐Passow JE, Lachance DH, Molinaro AM, et al. Glioma groups based on 1p/19q, IDH, and TERT promoter mutations in tumors. N Engl J Med 2015;372:2499‐2508. PubMed PMC
Wijnenga MMJ, French PJ, Dubbink HJ, et al. The impact of surgery in molecularly defined low‐grade glioma: An integrated clinical, radiological, and molecular analysis. Neuro Oncol 2018;20:103‐112. PubMed PMC
Clement P, Booth T, Borovečki F, et al. GliMR: Cross‐border collaborations to promote advanced MRI biomarkers for glioma. J Med Biol Eng 2020;41:1‐11. PubMed PMC
Romeo V, Stanzione A, Ugga L, et al. A critical appraisal of the quality of glioma imaging guidelines using the AGREE II tool: A EuroAIM initiative. Front Oncol 2019;9:472. PubMed PMC
deSouza NM, van der Lugt A, Deroose CM, et al. Standardised lesion segmentation for imaging biomarker quantitation: A consensus recommendation from ESR and EORTC. Insights Imaging 2022;13:159. PubMed PMC
O'Connor JPB, Aboagye EO, Adams JE, et al. Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol 2017;14:169‐186. PubMed PMC
Manfrini E, Smits M, Thust S, et al. From research to clinical practice: A European neuroradiological survey on quantitative advanced MRI implementation. Eur Radiol 2021;31:6334‐6341. PubMed PMC
Donahue KM, Krouwer HG, Rand SD, et al. Utility of simultaneously acquired gradient‐echo and spin‐echo cerebral blood volume and morphology maps in brain tumor patients. Magn Reson Med 2000;43:845‐853. PubMed
Boxerman JL, Schmainda KM, Weisskoff RM. 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 2006;27:859‐867. PubMed PMC
Paulson ES, Schmainda KM. Comparison of dynamic susceptibility‐weighted contrast‐enhanced MR methods: Recommendations for measuring relative cerebral blood volume in brain tumors. Radiology 2008;249:601‐613. PubMed PMC
Boxerman JL, Quarles CC, Hu LS, et al. Consensus recommendations for a dynamic susceptibility contrast MRI protocol for use in high‐grade gliomas. Neuro Oncol 2020;22:1262‐1275. PubMed PMC
Quarles CC, Ward BD, Schmainda KM. Improving the reliability of obtaining tumor hemodynamic parameters in the presence of contrast agent extravasation. Magn Reson Med 2005;53:1307‐1316. PubMed
Bjornerud A, Sorensen AG, Mouridsen K, Emblem KE. T1‐ and T2*‐dominant extravasation correction in DSC‐MRI: Part I‐‐theoretical considerations and implications for assessment of tumor hemodynamic properties. J Cereb Blood Flow Metab 2011;31:2041‐2053. PubMed PMC
Leu K, Boxerman JL, Cloughesy TF, et al. Improved leakage correction for single‐Echo dynamic susceptibility contrast perfusion MRI estimates of relative cerebral blood volume in high‐grade gliomas by accounting for bidirectional contrast agent exchange. AJNR Am J Neuroradiol 2016;37:1440‐1446. PubMed PMC
Sugahara T, Korogi Y, Kochi M, et al. Correlation of MR imaging‐determined cerebral blood volume maps with histologic and angiographic determination of vascularity of gliomas. AJR Am J Roentgenol 1998;171:1479‐1486. PubMed
Lev MH, Ozsunar Y, Henson JW, et al. Glial tumor grading and outcome prediction using dynamic spin‐echo MR susceptibility mapping compared with conventional contrast‐enhanced MR: Confounding effect of elevated rCBV of oligodendrogliomas [corrected]. AJNR Am J Neuroradiol 2004;25:214‐221. PubMed PMC
Schmainda KM, Rand SD, Joseph AM, et al. Characterization of a first‐pass gradient‐echo spin‐echo method to predict brain tumor grade and angiogenesis. AJNR Am J Neuroradiol 2004;25:1524‐1532. PubMed PMC
McCullough BJ, Ader V, Aguedan B, et al. Preoperative relative cerebral blood volume analysis in gliomas predicts survival and mitigates risk of biopsy sampling error. J Neurooncol 2018;136:181‐188. PubMed
Soliman RK, Gamal SA, Essa A‐HA, Othman MH. Preoperative grading of glioma using dynamic susceptibility contrast MRI: Relative cerebral blood volume analysis of intra‐tumoural and peri‐tumoural tissue. Clin Neurol Neurosurg 2018;167:86‐92. PubMed
Juan‐Albarracín J, Fuster‐Garcia E, Pérez‐Girbés A, et al. Glioblastoma: Vascular habitats detected at preoperative dynamic susceptibility‐weighted contrast‐enhanced perfusion MR imaging predict survival. Radiology 2018;287:944‐954. PubMed
Maeda M, Itoh S, Kimura H, et al. Tumor vascularity in the brain: Evaluation with dynamic susceptibility‐contrast MR imaging. Radiology 1993;189:233‐238. PubMed
Connelly JM, Prah MA, Santos‐Pinheiro F, Mueller W, Cochran E, Schmainda KM. Magnetic resonance imaging mapping of brain tumor burden: Clinical implications for neurosurgical management: Case report. Neurosurg Open 2021;2:okab029. PubMed PMC
Parker NR, Khong P, Parkinson JF, Howell VM, Wheeler HR. Molecular heterogeneity in glioblastoma: Potential clinical implications. Front Oncol 2015;5:55. PubMed PMC
Lu J, Li X, Li H. Perfusion parameters derived from MRI for preoperative prediction of IDH mutation and MGMT promoter methylation status in glioblastomas. Magn Reson Imaging 2021;83:189‐195. PubMed
Pedeutour‐Braccini Z, Burel‐Vandenbos F, Gozé C, et al. Microfoci of malignant progression in diffuse low‐grade gliomas: Towards the creation of an intermediate grade in glioma classification? Virchows Arch 2015;466:433‐444. PubMed
Patel P, Baradaran H, Delgado D, et al. MR perfusion‐weighted imaging in the evaluation of high‐grade gliomas after treatment: A systematic review and meta‐analysis. Neuro Oncol 2017;19:118‐127. PubMed PMC
Prah MA, Stufflebeam SM, Paulson ES, et al. Repeatability of standardized and normalized relative CBV in patients with newly diagnosed glioblastoma. AJNR Am J Neuroradiol 2015;36:1654‐1661. PubMed PMC
Schmainda KM, Prah MA, Marques H, Kim E, Barboriak DP, Boxerman JL. Value of dynamic contrast perfusion MRI to predict early response to bevacizumab in newly diagnosed glioblastoma: Results from ACRIN 6686 multicenter trial. Neuro Oncol 2021;23:314‐323. PubMed PMC
Schmainda KM, Prah MA, Rand SD, et al. Multisite concordance of DSC‐MRI analysis for brain tumors: Results of a National Cancer Institute Quantitative Imaging Network Collaborative Project. AJNR Am J Neuroradiol 2018;39:1008‐1016. PubMed PMC
Hu LS, Eschbacher JM, Heiserman JE, et al. Reevaluating the imaging definition of tumor progression: Perfusion MRI quantifies recurrent glioblastoma tumor fraction, pseudoprogression, and radiation necrosis to predict survival. Neuro Oncol 2012;14:919‐930. PubMed PMC
Prah MA, Al‐Gizawiy MM, Mueller WM, et al. Spatial discrimination of glioblastoma and treatment effect with histologically‐validated perfusion and diffusion magnetic resonance imaging metrics. J Neurooncol 2018;136:13‐21. PubMed PMC
Hu LS, Kelm Z, Korfiatis P, et al. Impact of software modeling on the accuracy of perfusion MRI in glioma. AJNR Am J Neuroradiol 2015;36:2242‐2249. PubMed PMC
Milchenko MV, Rajderkar D, LaMontagne P, et al. Comparison of perfusion‐ and diffusion‐weighted imaging parameters in brain tumor studies processed using different software platforms. Acad Radiol 2014;21:1294‐1303. PubMed PMC
Schmainda KM, Zhang Z, Prah M, et al. Dynamic susceptibility contrast MRI measures of relative cerebral blood volume as a prognostic marker for overall survival in recurrent glioblastoma: Results from the ACRIN 6677/RTOG 0625 multicenter trial. Neuro Oncol 2015;17:1148‐1156. PubMed PMC
Gerstner ER, Zhang Z, Fink JR, et al. ACRIN 6684: Assessment of tumor hypoxia in newly diagnosed glioblastoma using 18F‐FMISO PET and MRI. Clin Cancer Res 2016;22:5079‐5086. PubMed PMC
Stokes AM, Skinner JT, Quarles CC. Assessment of a combined spin‐ and gradient‐echo (SAGE) DSC‐MRI method for preclinical neuroimaging. Magn Reson Imaging 2014;32:1181‐1190. PubMed PMC
Skinner JT, Robison RK, Elder CP, Newton AT, Damon BM, Quarles CC. Evaluation of a multiple spin‐ and gradient‐echo (SAGE) EPI acquisition with SENSE acceleration: Applications for perfusion imaging in and outside the brain. Magn Reson Imaging 2014;32:1171‐1180. PubMed PMC
Khalifa F, Soliman A, El‐Baz A, et al. Models and methods for analyzing DCE‐MRI: A review. Med Phys 2014;41:124301. PubMed
Tofts PS, Brix G, Buckley DL, et al. Estimating kinetic parameters from dynamic contrast‐enhanced t1‐weighted MRI of a diffusable tracer: Standardized quantities and symbols. J Magn Reson Imaging 1999;10:223‐232. PubMed
Yun TJ, Park C‐K, Kim TM, et al. Glioblastoma treated with concurrent radiation therapy and temozolomide chemotherapy: Differentiation of true progression from pseudoprogression with quantitative dynamic contrast‐enhanced MR imaging. Radiology 2015;274:830‐840. PubMed
Zhang J, Liu H, Tong H, et al. Clinical applications of contrast‐enhanced perfusion MRI techniques in gliomas: Recent advances and current challenges. Contrast Media Mol Imaging 2017;2017:7064120‐7064127. PubMed PMC
Leach MO, Morgan B, Tofts PS, et al. Imaging vascular function for early stage clinical trials using dynamic contrast‐enhanced magnetic resonance imaging. Eur Radiol 2012;22:1451‐1464. PubMed
Petralia G, Summers PE, Agostini A, et al. Dynamic contrast‐enhanced MRI in oncology: How we do it. Radiol Med 2020;125:1288‐1300. PubMed
Harrer JU, Parker GJM, Haroon HA, et al. Comparative study of methods for determining vascular permeability and blood volume in human gliomas. J Magn Reson Imaging 2004;20:748‐757. PubMed
Okuchi S, Rojas‐Garcia A, Ulyte A, et al. Diagnostic accuracy of dynamic contrast‐enhanced perfusion MRI in stratifying gliomas: A systematic review and meta‐analysis. Cancer Med 2019;8:5564‐5573. PubMed PMC
Hu Y, Chen Y, Wang J, Kang JJ, Shen DD, Jia ZZ. Non‐invasive estimation of glioma IDH1 mutation and VEGF expression by histogram analysis of dynamic contrast‐enhanced MRI. Front Oncol 2020;10:593102. PubMed PMC
Ulyte A, Katsaros VK, Liouta E, et al. Prognostic value of preoperative dynamic contrast‐enhanced MRI perfusion parameters for high‐grade glioma patients. Neuroradiology 2016;58:1197‐1208. PubMed PMC
Kim H. Variability in quantitative DCE‐MRI: Sources and solutions. J Nat Sci 2018;4:e484. PubMed PMC
Duan C, Kallehauge JF, Bretthorst GL, Tanderup K, Ackerman JJH, Garbow JR. Are complex DCE‐MRI models supported by clinical data? Magn Reson Med 2017;77:1329‐1339. PubMed PMC
Alsop DC, Detre JA, Golay X, et al. Recommended implementation of arterial spin‐labeled perfusion MRI for clinical applications: A consensus of the ISMRM perfusion study group and the European consortium for ASL in dementia. Magn Reson Med 2015;73:102‐116. PubMed PMC
Falk Delgado A, De Luca F, van Westen D, Falk Delgado A. Arterial spin labeling MR imaging for differentiation between high‐ and low‐grade glioma‐a meta‐analysis. Neuro Oncol 2018;20:1450‐1461. PubMed PMC
Soldozy S, Galindo J, Snyder H, et al. Clinical utility of arterial spin labeling imaging in disorders of the nervous system. Neurosurg Focus 2019;47:E5. PubMed
Abdel Razek AAK, Talaat M, El‐Serougy L, Gaballa G, Abdelsalam M. Clinical applications of arterial spin labeling in brain tumors. J Comput Assist Tomogr 2019;43:525‐532. PubMed
Kong L, Chen H, Yang Y, Chen L. A meta‐analysis of arterial spin labelling perfusion values for the prediction of glioma grade. Clin Radiol 2017;72:255‐261. PubMed
Alsaedi A, Doniselli F, Jäger HR, et al. The value of arterial spin labelling in adults glioma grading: Systematic review and meta‐analysis. Oncotarget 2019;10:1589‐1601. PubMed PMC
Mao J, Deng D, Yang Z, et al. Pretreatment structural and arterial spin labeling MRI is predictive for p53 mutation in high‐grade gliomas. Br J Radiol 2020;93:20200661. PubMed PMC
Yoo R‐E, Yun TJ, Hwang I, et al. Arterial spin labeling perfusion‐weighted imaging aids in prediction of molecular biomarkers and survival in glioblastomas. Eur Radiol 2020;30:1202‐1211. PubMed
Dangouloff‐Ros V, Deroulers C, Foissac F, et al. Arterial spin labeling to predict brain tumor grading in children: Correlations between histopathologic vascular density and perfusion MR imaging. Radiology 2016;281:553‐566. PubMed
Pang H, Dang X, Ren Y, et al. 3D‐ASL perfusion correlates with VEGF expression and overall survival in glioma patients: Comparison of quantitative perfusion and pathology on accurate spatial location‐matched basis. J Magn Reson Imaging 2019;50:209‐220. PubMed
Flies CM, Snijders TJ, Van Seeters T, et al. Perfusion imaging with arterial spin labeling (ASL)‐MRI predicts malignant progression in low‐grade (WHO grade II) gliomas. Neuroradiology 2021;63:2023‐2033. PubMed PMC
Yang S, Zhao B, Wang G, et al. Improving the grading accuracy of astrocytic neoplasms noninvasively by combining timing information with cerebral blood flow: A multi‐TI arterial spin‐labeling MR imaging study. AJNR Am J Neuroradiol 2016;37:2209‐2216. PubMed PMC
Xiao H‐F, Chen Z‐Y, Lou X, et al. Astrocytic tumour grading: A comparative study of three‐dimensional pseudocontinuous arterial spin labelling, dynamic susceptibility contrast‐enhanced perfusion‐weighted imaging, and diffusion‐weighted imaging. Eur Radiol 2015;25:3423‐3430. PubMed PMC
Qu Y, Kong D, Wen H, et al. Perfusion measurement in brain gliomas using velocity‐selective arterial spin labeling: Comparison with pseudo‐continuous arterial spin labeling and dynamic susceptibility contrast MRI. Eur Radiol 2022;32:2976‐2987. PubMed
Zhang L, Min Z, Tang M, Chen S, Lei X, Zhang X. The utility of diffusion MRI with quantitative ADC measurements for differentiating high‐grade from low‐grade cerebral gliomas: Evidence from a meta‐analysis. J Neurol Sci 2017;373:9‐15. PubMed
Wang C, Xu Z, Wang S, et al. Clinical importance of ADC in the prediction of 125I in the treatment for gliomas. J Cancer 2021;12:1945‐1951. PubMed PMC
Leu K, Ott GA, Lai A, et al. Perfusion and diffusion MRI signatures in histologic and genetic subtypes of WHO grade II‐III diffuse gliomas. J Neurooncol 2017;134:177‐188. PubMed PMC
Park YW, Park JE, Ahn SS, et al. Magnetic resonance imaging parameters for noninvasive prediction of epidermal growth factor receptor amplification in isocitrate dehydrogenase‐wild‐type lower‐grade gliomas: A multicenter study. Neurosurgery 2021;89:257‐265. PubMed
Fujita Y, Nagashima H, Tanaka K, et al. The histopathologic and radiologic features of T2‐FLAIR mismatch sign in IDH‐mutant 1p/19q non‐codeleted Astrocytomas. World Neurosurg 2021;149:e253‐e260. PubMed
Thust S, Micallef C, Okuchi S, et al. Imaging characteristics of H3 K27M histone‐mutant diffuse midline glioma in teenagers and adults. Quant Imaging Med Surg 2021;11:43‐56. PubMed PMC
Seong M, Kim ST, Noh JH, Kim YK, Kim H‐J. Radiologic findings and the molecular expression profile of diffuse midline glioma H3 K27M mutant. Acta Radiol 2021;62:1404‐1411. PubMed
White ML, Zhang Y, Yu F, Jaffar Kazmi SA. Diffusion tensor MR imaging of cerebral gliomas: Evaluating fractional anisotropy characteristics. AJNR Am J Neuroradiol 2011;32:374‐381. PubMed PMC
Luks TL, McKnight TR, Jalbert LE, et al. Relationship of In vivo MR parameters to histopathological and molecular characteristics of newly diagnosed, nonenhancing lower‐grade gliomas. Transl Oncol 2018;11:941‐949. PubMed PMC
Xiong J, Tan W‐L, Pan J‐W, et al. Detecting isocitrate dehydrogenase gene mutations in oligodendroglial tumors using diffusion tensor imaging metrics and their correlations with proliferation and microvascular density. J Magn Reson Imaging 2016;43:45‐54. PubMed
Augelli R, Ciceri E, Ghimenton C, et al. Magnetic resonance diffusion‐tensor imaging metrics in high grade gliomas: Correlation with IDH1 gene status in WHO 2016 era. Eur J Radiol 2019;116:174‐179. PubMed
Park YW, Han K, Ahn SS, et al. Whole‐tumor histogram and texture analyses of DTI for evaluation of IDH1‐mutation and 1p/19q‐codeletion status in World Health Organization grade II gliomas. AJNR Am J Neuroradiol 2018;39:693‐698. PubMed PMC
Bette S, Huber T, Gempt J, et al. Local fractional anisotropy is reduced in areas with tumor recurrence in glioblastoma. Radiology 2017;283:499‐507. PubMed
Raab P, Hattingen E, Franz K, Zanella FE, Lanfermann H. Cerebral gliomas: Diffusional kurtosis imaging analysis of microstructural differences. Radiology 2010;254:876‐881. PubMed
Van Cauter S, De Keyzer F, Sima DM, et al. Integrating diffusion kurtosis imaging, dynamic susceptibility‐weighted contrast‐enhanced MRI, and short echo time chemical shift imaging for grading gliomas. Neuro Oncol 2014;16:1010‐1021. PubMed PMC
Tietze A, Hansen MB, Østergaard L, et al. Mean diffusional kurtosis in patients with glioma: Initial results with a fast imaging method in a clinical setting. AJNR Am J Neuroradiol 2015;36:1472‐1478. PubMed PMC
Bisdas S, Shen H, Thust S, et al. Texture analysis‐ and support vector machine‐assisted diffusional kurtosis imaging may allow in vivo gliomas grading and IDH‐mutation status prediction: A preliminary study. Sci Rep 2018;8:6108. PubMed PMC
Kikuchi K, Hiwatashi A, Togao O, et al. Intravoxel incoherent motion MR imaging of pediatric intracranial tumors: Correlation with histology and diagnostic utility. AJNR Am J Neuroradiol 2019;40:878‐884. PubMed PMC
Puig J, Sánchez‐González J, Blasco G, et al. Intravoxel incoherent motion metrics as potential biomarkers for survival in glioblastoma. PLoS One 2016;11:e0158887. PubMed PMC
Kiselev VG, Strecker R, Ziyeh S, Speck O, Hennig J. Vessel size imaging in humans. Magn Reson Med 2005;53:553‐563. PubMed
Boxerman JL, Hamberg LM, Rosen BR, Weisskoff RM. MR contrast due to intravascular magnetic susceptibility perturbations. Magn Reson Med 1995;34:555‐566. PubMed
Emblem KE, Mouridsen K, Bjornerud A, et al. Vessel architectural imaging identifies cancer patient responders to anti‐angiogenic therapy. Nat Med 2013;19:1178‐1183. PubMed PMC
Kadota T, Nakagawa H, Kuroda C. Malignant glioma. Evaluation with 3D time‐of‐flight MR angiography. Acta Radiol 1998;39:227‐232. PubMed
Radbruch A, Eidel O, Wiestler B, et al. Quantification of tumor vessels in glioblastoma patients using time‐of‐flight angiography at 7 Tesla: A feasibility study. PLoS One 2014;9:e110727. PubMed PMC
Strumia M, Reichardt W, Staszewski O, et al. Glioma vessel abnormality quantification using time‐of‐flight MR angiography. Magma 2016;29:765‐775. PubMed
Puig J, Blasco G, Daunis‐I‐Estadella J, et al. High‐resolution blood‐pool‐contrast‐enhanced MR angiography in glioblastoma: Tumor‐associated neovascularization as a biomarker for patient survival. A preliminary study. Neuroradiology 2016;58:17‐26. PubMed
Kang H‐Y, Xiao H‐L, Chen J‐H, et al. Comparison of the effect of vessel size imaging and cerebral blood volume derived from perfusion MR imaging on glioma grading. AJNR Am J Neuroradiol 2016;37:51‐57. PubMed PMC
Stadlbauer A, Zimmermann M, Kitzwögerer M, et al. MR imaging‐derived oxygen metabolism and neovascularization characterization for grading and IDH gene mutation detection of gliomas. Radiology 2017;283:799‐809. PubMed
Zhang K, Yun SD, Triphan SMF, et al. Vessel architecture imaging using multiband gradient‐echo/spin‐echo EPI. PLoS One 2019;14:e0220939. PubMed PMC
Stadlbauer A, Zimmermann M, Heinz G, et al. Magnetic resonance imaging biomarkers for clinical routine assessment of microvascular architecture in glioma. J Cereb Blood Flow Metab 2017;37:632‐643. PubMed PMC
Ozsarlak O, Van Goethem JW, Maes M, Parizel PM. MR angiography of the intracranial vessels: Technical aspects and clinical applications. Neuroradiology 2004;46:955‐972. PubMed
Kang C‐K, Park C‐W, Han J‐Y, et al. Imaging and analysis of lenticulostriate arteries using 7.0‐Tesla magnetic resonance angiography. Magn Reson Med 2009;61:136‐144. PubMed
Cheng H‐LM, Stikov N, Ghugre NR, Wright GA. Practical medical applications of quantitative MR relaxometry. J Magn Reson Imaging 2012;36:805‐824. PubMed
Tanenbaum LN, Tsiouris AJ, Johnson AN, et al. Synthetic MRI for clinical neuroimaging: Results of the magnetic resonance image compilation (MAGiC) prospective, multicenter, multireader trial. AJNR Am J Neuroradiol 2017;38:1103‐1110. PubMed PMC
Ma D, Gulani V, Seiberlich N, et al. Magnetic resonance fingerprinting. Nature 2013;495:187‐192. PubMed PMC
Jiang Y, Ma D, Seiberlich N, Gulani V, Griswold MA. MR fingerprinting using fast imaging with steady state precession (FISP) with spiral readout. Magn Reson Med 2015;74:1621‐1631. PubMed PMC
Badve C, Yu A, Dastmalchian S, et al. MR fingerprinting of adult brain tumors: Initial experience. AJNR Am J Neuroradiol 2017;38:492‐499. PubMed PMC
de Blank P, Badve C, Gold DR, et al. Magnetic resonance fingerprinting to characterize childhood and young adult brain tumors. Pediatr Neurosurg 2019;54:310‐318. PubMed
Kern M, Auer TA, Picht T, Misch M, Wiener E. T2 mapping of molecular subtypes of WHO grade II/III gliomas. BMC Neurol 2020;20:8. PubMed PMC
Mariappan Y, McGee K, Ehman R. Quantitative MRI of tumors. Quantifying morphology and physiology of the human body using MRI. Boca Raton, FL: CRC Press; 2013. p 283‐342.
Dastmalchian S, Kilinc O, Onyewadume L, et al. Radiomic analysis of magnetic resonance fingerprinting in adult brain tumors. Eur J Nucl Med Mol Imaging 2021;48:683‐693. PubMed
Springer E, Cardoso PL, Strasser B, et al. MR fingerprinting—A radiogenomic marker for diffuse gliomas. Cancer 2022;14:723. PubMed PMC
Blystad I, Warntjes JBM, Smedby Ö, Lundberg P, Larsson E‐M, Tisell A. Quantitative MRI for analysis of peritumoral edema in malignant gliomas. PLoS One 2017;12:e0177135. PubMed PMC
Blystad I, Warntjes JBM, Smedby Ö, Lundberg P, Larsson E‐M, Tisell A. Quantitative MRI using relaxometry in malignant gliomas detects contrast enhancement in peritumoral oedema. Sci Rep 2020;10:17986. PubMed PMC
Nöth U, Tichy J, Tritt S, Bähr O, Deichmann R, Hattingen E. Quantitative T1 mapping indicates tumor infiltration beyond the enhancing part of glioblastomas. NMR Biomed 2020;33:e4242. PubMed
Pirkl CM, Nunez‐Gonzalez L, Kofler F, et al. Accelerated 3D whole‐brain T1, T2, and proton density mapping: Feasibility for clinical glioma MR imaging. Neuroradiology 2021;63:1831‐1851. PubMed PMC
Körzdörfer G, Kirsch R, Liu K, et al. Reproducibility and repeatability of MR fingerprinting relaxometry in the human brain. Radiology 2019;292:429‐437. PubMed
McGivney DF, Boyacıoğlu R, Jiang Y, et al. Magnetic resonance fingerprinting review part 2: Technique and directions. J Magn Reson Imaging 2020;51:993‐1007. PubMed PMC
Menze BH, Jakab A, Bauer S, et al. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging 2015;34:1993‐2024. PubMed PMC
Bell L, Mutsaerts H‐J, Fedorov A, et al. Open source initiative for perfusion imaging (OSIPI): A profile on a community led initiative. 2021 ISMRM & SMRT Annual Meeting & Exhibition. Concord, CA: International Society for Magnetic Resonance in Medicine; 2021.
Shukla‐Dave A, Obuchowski NA, Chenevert TL, et al. Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE‐MRI derived biomarkers in multicenter oncology trials. J Magn Reson Imaging 2019;49:e101‐e121. PubMed PMC
Mahroo A, Buck MA, Huber J, et al. Robust multi‐TE ASL‐based blood‐brain barrier integrity measurements. Front Neurosci 2021;15:719676. PubMed PMC