Magnetic resonance imaging techniques for monitoring glioma response to chemoradiotherapy

Wen PY, Kesari S (2008) Malignant gliomas in adults. N Engl J Med 359:492–507. https://doi.org/10.1056/NEJMra0708126

Article  PubMed  Google Scholar 

Louis DN et al (2021) Aug., The 2021 WHO Classification of Tumors of the Central Nervous System: a summary, Neuro-Oncol., vol. 23, no. 8, pp. 1231–1251, https://doi.org/10.1093/neuonc/noab106

Forst DA, Nahed BV, Loeffler JS, Batchelor TT (Apr. 2014) Low-Grade Gliomas. Oncologist 19(4):403–413. https://doi.org/10.1634/theoncologist.2013-0345

Rudà R, Horbinski C, van den Bent M, Preusser M, Soffietti R (May 2024) IDH inhibition in gliomas: from preclinical models to clinical trials. Nat Rev Neurol. https://doi.org/10.1038/s41582-024-00967-7

Tseng C-L et al (2024) Mar., Evolving concepts in margin strategies and adaptive radiotherapy for glioblastoma: A new future is on the horizon, Neuro-Oncol., vol. 26, no. 12 Suppl 2, pp. S3–S16, https://doi.org/10.1093/neuonc/noad258

Wen PY et al (Apr. 2010) Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol 28(11):1963–1972. https://doi.org/10.1200/JCO.2009.26.3541

van den Bent MJ et al (Jun. 2011) Response assessment in neuro-oncology (a report of the RANO group): assessment of outcome in trials of diffuse low-grade gliomas. Lancet Oncol 12(6):583–593. https://doi.org/10.1016/S1470-2045(11)70057-2

Sagiyama K et al (2014) Mar., In vivo chemical exchange saturation transfer imaging allows early detection of a therapeutic response in glioblastoma, Proc. Natl. Acad. Sci. U. S. A., vol. 111, no. 12, pp. 4542–4547, https://doi.org/10.1073/pnas.1323855111

Chenevert TL et al (2000) Diffusion magnetic resonance imaging: an early surrogate marker of therapeutic efficacy in brain tumors. J Natl Cancer Inst 92(24):2029–2036. https://doi.org/10.1093/jnci/92.24.2029

Article  PubMed  Google Scholar 

Brandsma D, Stalpers L, Taal W, Sminia P, van den Bent MJ (May 2008) Clinical features, mechanisms, and management of pseudoprogression in malignant gliomas. Lancet Oncol 9(5):453–461. https://doi.org/10.1016/S1470-2045(08)70125-6

Jhaveri N, Chen TC, Hofman FM (Oct. 2016) Tumor vasculature and glioma stem cells: contributions to glioma progression. Cancer Lett 380(2):545–551. https://doi.org/10.1016/j.canlet.2014.12.028

Sugahara T et al (2000) Posttherapeutic intraaxial brain tumor: the value of perfusion-sensitive contrast-enhanced MR imaging for differentiating tumor recurrence from nonneoplastic contrast-enhancing tissue. AJNR Am J Neuroradiol 21(5):901–909. http://www.ncbi.nlm.nih.gov/pubmed/10815666

Jahng G-H, Li K-L, Ostergaard L, Calamante F (2014) Perfusion magnetic resonance imaging: a Comprehensive Update on principles and techniques. Korean J Radiol 15(5):554. https://doi.org/10.3348/kjr.2014.15.5.554

Article  PubMed  PubMed Central  Google Scholar 

Aronen HJ et al (Apr. 1994) Cerebral blood volume maps of gliomas: comparison with tumor grade and histologic findings. Radiology 191(1):41–51. https://doi.org/10.1148/radiology.191.1.8134596

Hu LS et al (2009) Mar., Relative Cerebral Blood Volume Values to Differentiate High-Grade Glioma Recurrence from Posttreatment Radiation Effect: Direct Correlation between Image-Guided Tissue Histopathology and Localized Dynamic Susceptibility-Weighted Contrast-Enhanced Perfusion MR Imaging Measurements, Am. J. Neuroradiol., vol. 30, no. 3, pp. 552–558, https://doi.org/10.3174/ajnr.A1377

Barajas RF et al (2009) Nov., Differentiation of Recurrent Glioblastoma Multiforme from Radiation Necrosis after External Beam Radiation Therapy with Dynamic Susceptibility-weighted Contrast-enhanced Perfusion MR Imaging, Radiology, vol. 253, no. 2, pp. 486–496, https://doi.org/10.1148/radiol.2532090007

Kong D-S et al (2011) Feb., Diagnostic Dilemma of Pseudoprogression in the Treatment of Newly Diagnosed Glioblastomas: The Role of Assessing Relative Cerebral Blood Flow Volume and Oxygen-6-Methylguanine-DNA Methyltransferase Promoter Methylation Status, Am. J. Neuroradiol., vol. 32, no. 2, pp. 382–387, https://doi.org/10.3174/ajnr.A2286

Prager AJ, Martinez N, Beal K, Omuro A, Zhang Z, Young RJ (May 2015) Diffusion and perfusion MRI to Differentiate Treatment-related changes including pseudoprogression from recurrent tumors in high-Grade Gliomas with histopathologic evidence. Am J Neuroradiol 36(5):877–885. https://doi.org/10.3174/ajnr.A4218

Bisdas S et al (May 2011) Distinguishing recurrent high-grade gliomas from Radiation Injury. Acad Radiol 18(5):575–583. https://doi.org/10.1016/j.acra.2011.01.018

Zakhari N et al (2019) Aug., Prospective comparative diagnostic accuracy evaluation of dynamic contrast-enhanced (DCE) vs. dynamic susceptibility contrast (DSC) MR perfusion in differentiating tumor recurrence from radiation necrosis in treated high‐grade gliomas, J. Magn. Reson. Imaging, vol. 50, no. 2, pp. 573–582, https://doi.org/10.1002/jmri.26621

Boxerman JL et al (2020) Sep., Consensus recommendations for a dynamic susceptibility contrast MRI protocol for use in high-grade gliomas, Neuro-Oncol., vol. 22, no. 9, pp. 1262–1275, https://doi.org/10.1093/neuonc/noaa141

Giese A, Westphal M (1996) Glioma Invasion in the Central Nervous System, Neurosurgery, vol. 39, no. 2, [Online]. Available: https://journals.lww.com/neurosurgery/fulltext/1996/08000/glioma_invasion_in_the_central_nervous_system.1.aspx

Stejskal EO, Tanner JE (1965) Spin Diffusion Measurements: Spin Echoes in the Presence of a Time-Dependent Field Gradient, J. Chem. Phys., vol. 42, no. 1, pp. 288–292, Jan. https://doi.org/10.1063/1.1695690

Bihan DL (1995) Molecular diffusion, tissue microdynamics and microstructure, NMR Biomed., vol. 8, no. 7, pp. 375–386, Nov. https://doi.org/10.1002/nbm.1940080711

Ross BD, Chenevert TL, Kim B, Ben-Yoseph O (1994) Magnetic Resonance Imaging and spectroscopy: application to experimental neuro-oncology. Q Magn Reson Biol Med 1(2):89–106. https://pubmed.ncbi.nlm.nih.gov/26550608/

Sugahara T et al (1999) Usefulness of diffusion-weighted MRI with echo-planar technique in the evaluation of cellularity in gliomas. J Magn Reson Imaging 9(1):53–60. https://doi.org/10.1002/(SICI)1522-2586(199901)9:1%3C;53::AID-JMRI7%3E;3.0.CO;2-2

Article  PubMed  Google Scholar 

Ellingson BM et al (2010) Mar., Validation of functional diffusion maps (fDMs) as a biomarker for human glioma cellularity, J. Magn. Reson. Imaging, vol. 31, no. 3, pp. 538–548, https://doi.org/10.1002/jmri.22068

Hamstra DA et al (2008) Functional diffusion map as an early imaging biomarker for high-Grade Glioma: correlation with conventional radiologic response and overall survival. J Clin Oncol 26(20):3387–3394. https://doi.org/10.1200/JCO.2007.15.2363

Article  PubMed  PubMed Central  Google Scholar 

Chenevert T et al (Mar. 2019) Comparison of Voxel-wise and Histogram Analyses of Glioma ADC maps for prediction of early therapeutic change. Tomography 5(1):7–14. https://doi.org/10.18383/j.tom.2018.00049

Lawrence LSP et al (Aug. 2023) Diffusion-weighted imaging on an MRI-linear accelerator to identify adversely prognostic tumour regions in glioblastoma during chemoradiation. Radiother Oncol p. 109873. https://doi.org/10.1016/j.radonc.2023.109873

Kang BK, Na DG, Ryoo JW, Byun HS, Roh HG, Pyeun YS (2001) Diffusion-weighted MR Imaging of Intracerebral Hemorrhage. Korean J Radiol 2(4):183. https://doi.org/10.3348/kjr.2001.2.4.183

Article  PubMed  PubMed Central  Google Scholar 

Kucharczyk W, Macdonald PM, Stanisz GJ, Henkelman RM (1994) Relaxivity and magnetization transfer of white matter lipids at MR imaging: importance of cerebrosides and pH., Radiology, vol. 192, no. 2, pp. 521–529, Aug. https://doi.org/10.1148/radiology.192.2.8029426

Wolff SD, Balaban RS (1989) Magnetization transfer contrast (MTC) and tissue water proton relaxation in vivo, Magn. Reson. Med., vol. 10, no. 1, pp. 135–144, Apr. https://doi.org/10.1002/mrm.1910100113

Henkelman RM, Huang X, Xiang Q, Stanisz GJ, Swanson SD, Bronskill MJ (1993) Quantitative interpretation of magnetization transfer, Magn. Reson. Med., vol. 29, no. 6, pp. 759–766, Jun. https://doi.org/10.1002/mrm.1910290607

Underhill HR (2011) Fast bound pool fraction imaging of the in vivo rat brain: Association with myelin content and validation in the C6 glioma model. NeuroImage 54:2052–2065. https://doi.org/10.1016/j.neuroimage.2010.10.065

Lundbom N (Dec. 1992) Determination of magnetization transfer contrast in tissue: an MR imaging study of brain tumors. Am J Roentgenol 159(6):1279–1285. https://doi.org/10.2214/ajr.159.6.1442402

Kurki T, Lundbom N, Kalimo H, Valtonen S (Jan. 1995) MR classification of brain gliomas: value of magnetization transfer and conventional imaging. Magn Reson Imaging 13(4):501–511. https://doi.org/10.1016/0730-725X(95)00006-3

Quesson B, Bouzier A, Thiaudiere E, Delalande C, Merle M, Canioni P (1997) Magnetization transfer fast imaging of implanted glioma in the rat brain at 4.7 T: Interpretation using a binary spin-bath model, J. Magn. Reson. Imaging, vol. 7, no. 6, pp. 1076–1083, Nov. https://doi.org/10.1002/jmri.1880070621

Mehrabian H, Myrehaug S, Soliman H, Sahgal A, Stanisz GJ (Dec. 2018) Quantitative magnetization transfer in monitoring Glioblastoma (GBM) response to Therapy. Sci Rep 8(1):2475. https://doi.org/10.1038/s41598-018-20624-6

Chan RW et al (Nov. 2020) Quantitative CEST and MT at 1.5T for monitoring treatment response in glioblastoma: early and late tumor progression during chemoradiation. J Neurooncol. https://doi.org/10.1007/s11060-020-03661-y

Kroh F et al (2023) Oct., Semi-solid MT and APTw CEST‐MRI predict clinical outcome of patients with glioma early after radiotherapy, Magn. Reson. Med., vol. 90, no. 4, pp. 1569–1581, https://doi.org/10.1002/mrm.29746

Hsu Y-Y, Chen M-C, Lim K-E, Chang C (Feb. 2001) Reproducibility of hippocampal single-Voxel Proton MR Spectroscopy and Chemical Shift Imaging. Am J Roentgenol 176(2):529–536. https://doi.org/10.2214/ajr.176.2.1760529

Maudsley AA et al (May 2021) Advanced magnetic resonance spectroscopic neuroimaging: experts’ consensus recommendations. NMR Biomed 34(5):e4309. https://doi.org/10.1002/nbm.4309

Zhou J et al (2022) Aug., Review and consensus recommendations on clinical APT-weighted imaging approaches at 3T: Application to brain tumors, Magn. Reson. Med., vol. 88, no. 2, pp. 546–574, https://doi.org/10.1002/mrm.29241

Zhou J, Lal B, Wilson DA, Laterra J, van Zijl PCM (2003) Amide proton transfer (APT) contrast for imaging of brain tumors, Magn. Reson. Med., vol. 50, no. 6, pp. 1120–1126, Dec. https://doi.org/10.1002/mrm.10651

Jones CK, Schlosser MJ, Van Zijl PCM, Pomper MG, Golay X, Zhou J (2006) Amide proton transfer imaging of human brain tumors at 3T, Magn. Reson. Med., vol. 56, no. 3, pp. 585–592, Sep. https://doi.org/10.1002/mrm.20989

Desmond KL, Moosvi F, Stanisz GJ (May 2014) Mapping of amide, amine, and aliphatic peaks in the CEST spectra of murine xenografts at 7 T: CEST mapping in Murine Cancer xenografts at 7T. Magn Reson Med 71(5):1841–1853. https://doi.org/10.1002/mrm.24822

Ray KJ et al (Apr. 2019) Tumor pH and Protein Concentration Contribute to the Signal of Amide Proton Transfer Magnetic Resonance Imaging. Cancer Res 79(7):1343–1352. https://doi.org/10.1158/0008-5472.CAN-18-2168

Xu J et al (2014) Apr., On the origins of chemical exchange saturation transfer (CEST) contrast in tumors at 9.4 T, NMR Biomed., vol. 27, no. 4, pp. 406–416, https://doi.org/10.1002/nbm.3075

Zhou J et al (2011) Jan., Differentiation between glioma and radiation necrosis using molecular magnetic resonance imaging of endogenous proteins and peptides, Nat. Med., vol. 17, no. 1, pp. 130–134, https://doi.org/10.1038/nm.2268

Mehrabian H, Myrehaug S, Soliman H, Sahgal A, Stanisz GJ (Jul. 2018) Evaluation of Glioblastoma Response to Therapy with Chemical Exchange Saturation transfer. Int J Radiat Oncol 101(3):713–723. https://doi.org/10.1016/j.ijrobp.2018.03.057

Meissner J et al (2019) Oct., Early response assessment of glioma patients to definitive chemoradiotherapy using chemical exchange saturation transfer imaging at 7 T, J. Magn. Reson. Imaging, vol. 50, no. 4, pp. 1268–1277, https://doi.org/10.1002/jmri.26702

Perlman O et al (May 2022) Quantitative imaging of apoptosis following oncolytic virotherapy by magnetic resonance fingerprinting aided by deep learning. Nat Biomed Eng 6(5):648–657. https://doi.org/10.1038/s41551-021-00809-7

Zaiss M et al (2014) Mar., Inverse Z -spectrum analysis for spillover-, MT-, and T1 -corrected steady-state pulsed CEST-MRI - application to pH-weighted MRI of acute stroke: SIMPLE SPILLOVER-, MT-, AND T1 - CORRECTED CEST-MRI, NMR Biomed., vol. 27, no. 3, pp. 240–252, https://doi.org/10.1002/nbm.3054

Hagiwara A, Fujita S, Kurokawa R, Andica C, Kamagata K, Aoki S (Feb. 2023) Multiparametric MRI: from Simultaneous Rapid Acquisition Methods and Analysis techniques using Scoring, Machine Learning, Radiomics, and Deep Learning to the Generation of Novel Metrics. Invest Radiol. https://doi.org/10.1097/RLI.0000000000000962

Reporting and Data Systems| American College of Radiology Accessed: Oct. 08, 2024. [Online]. Available: https://www.acr.org/Clinical-Resources/Reporting-and-Data-Systems

Hagiwara A, Fujita S, Ohno Y, Aoki S (2020) Variability and Standardization of Quantitative Imaging: Monoparametric to Multiparametric Quantification, Radiomics, and Artificial Intelligence, Invest. Radiol., vol. 55, no. 9, pp. 601–616, Sep. https://doi.org/10.1097/RLI.0000000000000666

Ellingson BM et al (2015) Sep., Consensus recommendations for a standardized Brain Tumor Imaging Protocol in clinical trials, Neuro-Oncol., vol. 17, no. 9, pp. 1188–1198, https://doi.org/10.1093/neuonc/nov095

Leao DJ, Craig PG, Godoy LF, Leite CC, Policeni B (Jan. 2020) Response Assessment in Neuro-Oncology Criteria for Gliomas: practical Approach using Conventional and Advanced techniques. Am J Neuroradiol 41(1):10–20. https://doi.org/10.3174/ajnr.A6358

Tseng C-L et al (Nov. 2022) High grade glioma radiation therapy on a high field 1.5 Tesla MR-Linac - Workflow and initial experience with daily adapt-to-position (ATP) MR guidance: a first report. Front Oncol 12:1060098. https://doi.org/10.3389/fonc.2022.1060098

留言 (0)

沒有登入
gif