Simultaneous quantification of perfusion, permeability, and leakage effects in brain gliomas using dynamic spin-and-gradient-echo echoplanar imaging MRI.
Blood–brain barrier
Glioblastoma
Magnetic resonance imaging
Perfusion imaging
Vascular permeability
Journal
European radiology
ISSN: 1432-1084
Titre abrégé: Eur Radiol
Pays: Germany
ID NLM: 9114774
Informations de publication
Date de publication:
26 Oct 2023
26 Oct 2023
Historique:
received:
13
04
2023
accepted:
27
07
2023
revised:
05
07
2023
pubmed:
26
10
2023
medline:
26
10
2023
entrez:
26
10
2023
Statut:
aheadofprint
Résumé
To determine the feasibility and biologic correlations of dynamic susceptibility contrast (DSC), dynamic contrast enhanced (DCE), and quantitative maps derived from contrast leakage effects obtained simultaneously in gliomas using dynamic spin-and-gradient-echo echoplanar imaging (dynamic SAGE-EPI) during a single contrast injection. Thirty-eight patients with enhancing brain gliomas were prospectively imaged with dynamic SAGE-EPI, which was processed to compute traditional DSC metrics (normalized relative cerebral blood flow [nrCBV], percentage of signal recovery [PSR]), DCE metrics (volume transfer constant [K In IDH wild-type gliomas (IDH Dynamic SAGE-EPI enables simultaneous quantification of brain tumor perfusion and permeability, as well as mapping of novel metrics related to cytoarchitecture (TRATE) and blood-brain barrier disruption (ΔR Simultaneous DSC and DCE analysis with dynamic SAGE-EPI reduces scanning time and contrast dose, respectively alleviating concerns about imaging protocol length and gadolinium adverse effects and accumulation, while providing novel leakage effect metrics reflecting blood-brain barrier disruption and tumor tissue cytoarchitecture. • Traditionally, perfusion and permeability imaging for brain tumors requires two separate contrast injections and acquisitions. • Dynamic spin-and-gradient-echo echoplanar imaging enables simultaneous perfusion and permeability imaging. • Dynamic spin-and-gradient-echo echoplanar imaging provides new image contrasts reflecting blood-brain barrier disruption and cytoarchitecture characteristics.
Identifiants
pubmed: 37882836
doi: 10.1007/s00330-023-10215-z
pii: 10.1007/s00330-023-10215-z
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : NCI NIH HHS
ID : R01 CA270027
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA279984
Pays : United States
Organisme : NCI NIH HHS
ID : R21 CA223757
Pays : United States
Commentaires et corrections
Type : CommentIn
Informations de copyright
© 2023. The Author(s).
Références
Louis DN, Perry A, Wesseling P et al (2021) The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro Oncol 23:1231–1251. https://doi.org/10.1093/neuonc/noab106
doi: 10.1093/neuonc/noab106
pubmed: 34185076
pmcid: 8328013
Tabouret E, Nguyen AT, Dehais C et al (2016) Prognostic impact of the 2016 WHO classification of diffuse gliomas in the French POLA cohort. Acta Neuropathol 132:625–634. https://doi.org/10.1007/s00401-016-1611-8
doi: 10.1007/s00401-016-1611-8
pubmed: 27573687
Carabenciov ID, Buckner JC (2019) Controversies in the therapy of low-grade gliomas. Curr Treat Options Oncol 20:25. https://doi.org/10.1007/s11864-019-0625-6
doi: 10.1007/s11864-019-0625-6
pubmed: 30874903
Cloughesy TF, Mochizuki AY, Orpilla JR et al (2019) Neoadjuvant anti-PD-1 immunotherapy promotes a survival benefit with intratumoral and systemic immune responses in recurrent glioblastoma. Nat Med 25:477–486. https://doi.org/10.1038/s41591-018-0337-7
doi: 10.1038/s41591-018-0337-7
pubmed: 30742122
pmcid: 6408961
Ellingson BM, Kim HJ, Woodworth DC et al (2014) Recurrent glioblastoma treated with bevacizumab: contrast-enhanced T1-weighted subtraction maps improve tumor delineation and aid prediction of survival in a multicenter clinical trial. Radiology 271:200–210. https://doi.org/10.1148/radiol.13131305
doi: 10.1148/radiol.13131305
pubmed: 24475840
Hanahan D, Weinberg RA (2000) The hallmarks of cancer. Cell 100:57–70. https://doi.org/10.1016/s0092-8674(00)81683-9
doi: 10.1016/s0092-8674(00)81683-9
pubmed: 10647931
Domènech M, Hernández A, Plaja A, et al (2021) Hypoxia: the cornerstone of glioblastoma. Int J Mol Sci 22: https://doi.org/10.3390/ijms222212608
Kane JR (2019) The role of brain vasculature in glioblastoma. Mol Neurobiol 56:6645–6653. https://doi.org/10.1007/s12035-019-1561-y
doi: 10.1007/s12035-019-1561-y
pubmed: 30911935
Das S, Marsden PA (2013) Angiogenesis in glioblastoma. N Engl J Med 369:1561–1563. https://doi.org/10.1056/NEJMcibr1309402
doi: 10.1056/NEJMcibr1309402
pubmed: 24131182
pmcid: 5378489
Zhang J, Liu H, Tong H, et al (2017) Clinical applications of contrast-enhanced perfusion MRI techniques in gliomas: recent advances and current challenges. Contrast Media Mol Imaging 2017: https://doi.org/10.1155/2017/7064120
Chakhoyan A, Yao J, Leu K et al (2019) Validation of vessel size imaging (VSI) in high-grade human gliomas using magnetic resonance imaging, image-guided biopsies, and quantitative immunohistochemistry. Sci Rep 9:2846. https://doi.org/10.1038/s41598-018-37564-w
doi: 10.1038/s41598-018-37564-w
pubmed: 30808879
pmcid: 6391482
Tofts PS, Brix G, Buckley DL et al (1999) Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging 10:223–232. https://doi.org/10.1002/(sici)1522-2586(199909)10:3%3c223::aid-jmri2%3e3.0.co;2-s
doi: 10.1002/(sici)1522-2586(199909)10:3<223::aid-jmri2>3.0.co;2-s
pubmed: 10508281
Brendle C, Hempel JM, Schittenhelm J et al (2018) Glioma grading and determination of IDH mutation status and ATRX loss by DCE and ASL perfusion. Clin Neuroradiol 28:421–428. https://doi.org/10.1007/s00062-017-0590-z
doi: 10.1007/s00062-017-0590-z
pubmed: 28488024
Anzalone N, Castellano A, Cadioli M et al (2018) Brain gliomas: multicenter standardized assessment of dynamic contrast-enhanced and dynamic susceptibility contrast MR images. Radiology 287:933–943. https://doi.org/10.1148/radiol.2017170362
doi: 10.1148/radiol.2017170362
pubmed: 29361245
Delgado AF, Delgado AF (2017) Discrimination between glioma grades II and III using dynamic susceptibility perfusion MRI: a meta-analysis. AJNR Am J Neuroradiol 38:1348–1355. https://doi.org/10.3174/ajnr.A5218
doi: 10.3174/ajnr.A5218
pubmed: 28522666
pmcid: 7959917
Zhang HW, lyu GW, He WJ, et al (2020) DSC and DCE histogram analyses of glioma biomarkers, including IDH, MGMT, and TERT, on differentiation and survival. Acad Radiol 1–9. https://doi.org/10.1016/j.acra.2019.12.010
Sanvito F, Castellano A, Falini A (2021) Advancements in neuroimaging to unravel biological and molecular features of brain tumors. Cancers (Basel) 13:1–25. https://doi.org/10.3390/cancers13030424
doi: 10.3390/cancers13030424
Leu K, Ott GA, Lai A et al (2017) Perfusion and diffusion MRI signatures in histologic and genetic subtypes of WHO grade II–III diffuse gliomas. J Neurooncol 134:177–188. https://doi.org/10.1007/s11060-017-2506-9
doi: 10.1007/s11060-017-2506-9
pubmed: 28547590
pmcid: 7927357
Kickingereder P, Sahm F, Radbruch A et al (2015) IDH mutation status is associated with a distinct hypoxia/angiogenesis transcriptome signature which is non-invasively predictable with rCBV imaging in human glioma. Sci Rep 5:1–9. https://doi.org/10.1038/srep16238
doi: 10.1038/srep16238
Zhao J, Yang Z, Luo B et al (2015) Quantitative evaluation of diffusion and dynamic contrast-enhanced MR in tumor parenchyma and peritumoral area for distinction of brain tumors. PLoS One 10:e0138573. https://doi.org/10.1371/journal.pone.0138573
doi: 10.1371/journal.pone.0138573
pubmed: 26384329
pmcid: 4575081
Kickingereder P, Sahm F, Wiestler B et al (2014) Evaluation of microvascular permeability with dynamic contrast-enhanced MRI for the differentiation of primary CNS lymphoma and glioblastoma: radiologic-pathologic correlation. AJNR Am J Neuroradiol 35:1503–1508. https://doi.org/10.3174/ajnr.A3915
doi: 10.3174/ajnr.A3915
pubmed: 24722313
pmcid: 7964431
Chaganti J, Taylor M, Woodford H, Steel T (2021) Differentiation of primary central nervous system lymphoma and high-grade glioma with dynamic susceptibility contrast-derived metrics: pilot study. World Neurosurg 151:e979–e987. https://doi.org/10.1016/j.wneu.2021.05.026
doi: 10.1016/j.wneu.2021.05.026
pubmed: 34020062
Pons-Escoda A, Garcia-Ruiz A, Naval-Baudin P et al (2020) Presurgical identification of primary central nervous system lymphoma with normalized time-intensity curve: a pilot study of a new method to analyze DSC-PWI. AJNR Am J Neuroradiol 41:1816–1824. https://doi.org/10.3174/ajnr.A6761
doi: 10.3174/ajnr.A6761
pubmed: 32943424
pmcid: 7661072
Pons-Escoda A, Smits M (2023) Dynamic-susceptibility-contrast perfusion-weighted-imaging (DSC-PWI) in brain tumors: a brief up-to-date overview for clinical neuroradiologists. Eur Radiol https://doi.org/10.1007/s00330-023-09729-3
Strauss SB, Meng A, Ebani EJ, Chiang GC (2019) Imaging glioblastoma posttreatment: progression, pseudoprogression, pseudoresponse, radiation necrosis. Radiol Clin North Am. 57:1199–1216
doi: 10.1016/j.rcl.2019.07.003
pubmed: 31582045
Muto M, Frauenfelder G, Senese R et al (2018) Dynamic susceptibility contrast (DSC) perfusion MRI in differential diagnosis between radionecrosis and neoangiogenesis in cerebral metastases using rCBV, rCBF and K2. Radiol Med 123:545–552. https://doi.org/10.1007/s11547-018-0866-7
doi: 10.1007/s11547-018-0866-7
pubmed: 29508242
Shin KE, Ahn KJ, Choi HS et al (2014) DCE and DSC MR perfusion imaging in the differentiation of recurrent tumour from treatment-related changes in patients with glioma. Clin Radiol 69:e264-72. https://doi.org/10.1016/j.crad.2014.01.016
doi: 10.1016/j.crad.2014.01.016
pubmed: 24594379
Manfrini E, Smits M, Thust S et al (2021) From research to clinical practice: a European neuroradiological survey on quantitative advanced MRI implementation. Eur Radiol 31:6334–6341. https://doi.org/10.1007/s00330-020-07582-2
doi: 10.1007/s00330-020-07582-2
pubmed: 33481098
pmcid: 8270851
Gulani V, Calamante F, Shellock FG et al (2017) Gadolinium deposition in the brain: summary of evidence and recommendations. Lancet Neurol 16:564–570. https://doi.org/10.1016/S1474-4422(17)30158-8
doi: 10.1016/S1474-4422(17)30158-8
pubmed: 28653648
Weinreb JC, Rodby RA, Yee J et al (2021) Use of intravenous gadolinium-based contrast media in patients with kidney disease: consensus statements from the American College of Radiology and the National Kidney Foundation. Radiology 298:28–35. https://doi.org/10.1148/radiol.2020202903
doi: 10.1148/radiol.2020202903
pubmed: 33170103
Stokes AM, Bergamino M, Alhilali L et al (2021) Evaluation of single bolus, dual-echo dynamic susceptibility contrast MRI protocols in brain tumor patients. J Cereb Blood Flow Metab 41:3378–3390. https://doi.org/10.1177/0271678X211039597
doi: 10.1177/0271678X211039597
pubmed: 34415211
pmcid: 8669280
Quarles CC, Gochberg DF, Gore JC, Yankeelov TE (2009) A theoretical framework to model DSC-MRI data acquired in the presence of contrast agent extravasation. Phys Med Biol 54:5749–5766. https://doi.org/10.1088/0031-9155/54/19/006
doi: 10.1088/0031-9155/54/19/006
pubmed: 19729712
pmcid: 2767268
Semmineh NB, Xu J, Skinner JT et al (2015) Assessing tumor cytoarchitecture using multiecho DSC-MRI derived measures of the transverse relaxivity at tracer equilibrium (TRATE). Magn Reson Med 74:772–784. https://doi.org/10.1002/mrm.25435
doi: 10.1002/mrm.25435
pubmed: 25227668
Mangla R, Kolar B, Zhu T et al (2011) Percentage signal recovery derived from MR dynamic susceptibility contrast imaging is useful to differentiate common enhancing malignant lesions of the brain. AJNR Am J Neuroradiol 32:1004–1010. https://doi.org/10.3174/ajnr.A2441
doi: 10.3174/ajnr.A2441
pubmed: 21511863
pmcid: 8013151
Lee MD, Baird GL, Bell LC et al (2019) Utility of percentage signal recovery and baseline signal in DSC-MRI optimized for relative CBV measurement for differentiating glioblastoma, lymphoma, metastasis, and meningioma. AJNR Am J Neuroradiol 40:1445–1450. https://doi.org/10.3174/ajnr.A6153
doi: 10.3174/ajnr.A6153
pubmed: 31371360
pmcid: 6728173
Boxerman JL, Paulson ES, Prah MA, Schmainda KM (2013) The effect of pulse sequence parameters and contrast agent dose on percentage signal recovery in DSC-MRI: implications for clinical applications. AJNR Am J Neuroradiol 34:1364–1369. https://doi.org/10.3174/ajnr.A3477
doi: 10.3174/ajnr.A3477
pubmed: 23413249
pmcid: 4316677
Ellingson BM, Bendszus M, Boxerman J et al (2015) Consensus recommendations for a standardized Brain Tumor Imaging Protocol in clinical trials. Neuro Oncol 17:1188–1198. https://doi.org/10.1093/neuonc/nov095
doi: 10.1093/neuonc/nov095
pubmed: 26250565
pmcid: 4588759
Schmiedeskamp H, Straka M, Newbould RD et al (2012) Combined spin- and gradient-echo perfusion-weighted imaging. Magn Reson Med 68:30–40. https://doi.org/10.1002/mrm.23195
doi: 10.1002/mrm.23195
pubmed: 22114040
Schmiedeskamp H, Straka M, Bammer R (2012) Compensation of slice profile mismatch in combined spin- and gradient-echo echo-planar imaging pulse sequences. Magn Reson Med 67:378–388. https://doi.org/10.1002/mrm.23012
doi: 10.1002/mrm.23012
pubmed: 21858858
Welker K, Boxerman J, Kalnin A et al (2015) ASFNR recommendations for clinical performance of MR dynamic susceptibility contrast perfusion imaging of the brain. AJNR Am J Neuroradiol 36:E41–E51. https://doi.org/10.3174/ajnr.A4341
doi: 10.3174/ajnr.A4341
pubmed: 25907520
pmcid: 5074767
Conte GM, Altabella L, Castellano A et al (2019) Comparison of T1 mapping and fixed T1 method for dynamic contrast-enhanced MRI perfusion in brain gliomas. Eur Radiol 29:3467–3479. https://doi.org/10.1007/s00330-019-06122-x
doi: 10.1007/s00330-019-06122-x
pubmed: 30972545
Rohrer M, Bauer H, Mintorovitch J et al (2005) Comparison of magnetic properties of MRI contrast media solutions at different magnetic field strengths. Invest Radiol 40:715–724. https://doi.org/10.1097/01.rli.0000184756.66360.d3
doi: 10.1097/01.rli.0000184756.66360.d3
pubmed: 16230904
Leu K, Boxerman JL, Cloughesy TF et al (2016) 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 37:1440–1446. https://doi.org/10.3174/ajnr.A4759
doi: 10.3174/ajnr.A4759
pubmed: 27079371
pmcid: 4983482
Evans JD (1996) Straightforward statistics for the behavioral sciences. Thomson Brooks/Cole Publishing Co
Sanvito F, Palesi F, Rognone E et al (2022) Impact of the inversion time on regional brain perfusion estimation with clinical arterial spin labeling protocols. MAGMA 35:349–363. https://doi.org/10.1007/s10334-021-00964-7
doi: 10.1007/s10334-021-00964-7
pubmed: 34643853
Zhang Z, Gu W, Hu M et al (2022) Based on clinical Ki-67 expression and serum infiltrating lymphocytes related nomogram for predicting the diagnosis of glioma-grading. Front Oncol 12:696037. https://doi.org/10.3389/fonc.2022.696037
doi: 10.3389/fonc.2022.696037
pubmed: 36147928
pmcid: 9488114
Skjulsvik AJ, Mørk JN, Torp MO, Torp SH (2014) Ki-67/MIB-1 immunostaining in a cohort of human gliomas. Int J Clin Exp Pathol 7:8905–8910
pubmed: 25674263
pmcid: 4313958
Castellano A, Falini A (2016) Progress in neuro-imaging of brain tumors. Curr Opin Oncol 28:484–493. https://doi.org/10.1097/CCO.0000000000000328
doi: 10.1097/CCO.0000000000000328
pubmed: 27649026
Blethen KE, Sprowls SA, Arsiwala TA et al (2023) Effects of whole-brain radiation therapy on the blood-brain barrier in immunocompetent and immunocompromised mouse models. Radiat Oncol 18:22. https://doi.org/10.1186/s13014-023-02215-6
doi: 10.1186/s13014-023-02215-6
pubmed: 36732754
pmcid: 9896731
Wilson CM, Gaber MW, Sabek OM et al (2009) Radiation-induced astrogliosis and blood-brain barrier damage can be abrogated using anti-TNF treatment. Int J Radiat Oncol Biol Phys 74:934–941. https://doi.org/10.1016/j.ijrobp.2009.02.035
doi: 10.1016/j.ijrobp.2009.02.035
pubmed: 19480972
van Vulpen M, Kal HB, Taphoorn MJB, El-Sharouni SY (2002) Changes in blood-brain barrier permeability induced by radiotherapy: implications for timing of chemotherapy? (Review). Oncol Rep 9:683–688
pubmed: 12066192
Woodall RT, Sahoo P, Cui Y, et al (2021) Repeatability of tumor perfusion kinetics from dynamic contrast-enhanced MRI in glioblastoma. Neurooncol Adv 3:vdab174. https://doi.org/10.1093/noajnl/vdab174
Cetinkaya E, Aralasmak A, Atasoy B et al (2022) Dynamic contrast-enhanced MR perfusion in differentiation of benign and malignant brain lesions. Curr Med imaging 18:1099–1105. https://doi.org/10.2174/1573405618666220324112457
doi: 10.2174/1573405618666220324112457
pubmed: 35331119
Ahn SS, Shin NY, Chang JH et al (2014) Prediction of methylguanine methyltransferase promoter methylation in glioblastoma using dynamic contrast-enhanced magnetic resonance and diffusion tensor imaging: clinical article. J Neurosurg 121:367–373. https://doi.org/10.3171/2014.5.JNS132279
doi: 10.3171/2014.5.JNS132279
pubmed: 24949678
Aparici-Robles F, Davidhi A, Carot-Sierra JM et al (2022) Glioblastoma versus solitary brain metastasis: MRI differentiation using the edema perfusion gradient. J Neuroimaging 32:127–133. https://doi.org/10.1111/jon.12920
doi: 10.1111/jon.12920
pubmed: 34468052
Gates EDH, Weinberg JS, Prabhu SS et al (2021) Estimating local cellular density in glioma using MR imaging data. AJNR Am J Neuroradiol 42:102–108. https://doi.org/10.3174/ajnr.A6884
doi: 10.3174/ajnr.A6884
pubmed: 33243897
pmcid: 7814791
Surov A, Meyer HJ, Wienke A (2017) Correlation between apparent diffusion coefficient (ADC) and cellularity is different in several tumors: a meta-analysis. Oncotarget 8:59492–59499. https://doi.org/10.18632/oncotarget.17752
Rahm V, Boxheimer L, Bruehlmeier M et al (2014) Focal changes in diffusivity on apparent diffusion coefficient MR imaging and amino acid uptake on pet do not colocalize in nonenhancing low-grade gliomas. J Nucl Med 55:546–550. https://doi.org/10.2967/jnumed.113.130732
doi: 10.2967/jnumed.113.130732
pubmed: 24566001
Patel KS, Yao J, Raymond C et al (2020) Decorin expression is associated with predictive diffusion MR phenotypes of anti-VEGF efficacy in glioblastoma. Sci Rep 10:14819. https://doi.org/10.1038/s41598-020-71799-w
doi: 10.1038/s41598-020-71799-w
pubmed: 32908231
pmcid: 7481206
Cindil E, Sendur HN, Cerit MN et al (2021) Validation of combined use of DWI and percentage signal recovery-optimized protocol of DSC-MRI in differentiation of high-grade glioma, metastasis, and lymphoma. Neuroradiology 63:331–342. https://doi.org/10.1007/s00234-020-02522-9
doi: 10.1007/s00234-020-02522-9
pubmed: 32821962
Kinoshita M, Uchikoshi M, Tateishi S, et al (2021) Magnetic resonance relaxometry for tumor cell density imaging for glioma: an exploratory study via (11)C-methionine PET and its validation via stereotactic tissue sampling. Cancers (Basel) 13: https://doi.org/10.3390/cancers13164067
Bobholz SA, Lowman AK, Brehler M et al (2022) Radio-pathomic maps of cell density identify brain tumor invasion beyond traditional MRI-defined margins. AJNR Am J Neuroradiol 43:682–688. https://doi.org/10.3174/ajnr.A7477
doi: 10.3174/ajnr.A7477
pubmed: 35422419
pmcid: 9089258
Roberts TA, Hyare H, Agliardi G et al (2020) Noninvasive diffusion magnetic resonance imaging of brain tumour cell size for the early detection of therapeutic response. Sci Rep 10:1–13. https://doi.org/10.1038/s41598-020-65956-4
doi: 10.1038/s41598-020-65956-4
Arevalo-Perez J, Thomas AA, Kaley T et al (2015) T1-weighted dynamic contrast-enhanced MRI as a noninvasive biomarker of epidermal growth factor receptor VIII status. AJNR Am J Neuroradiol 36:2256–2261. https://doi.org/10.3174/ajnr.A4484
doi: 10.3174/ajnr.A4484
pubmed: 26338913
pmcid: 4724408
Jo SW, Choi SH, Lee EJ et al (2021) Prognostic prediction based on dynamic contrast-enhanced MRI and dynamic susceptibility contrast-enhanced MRI parameters from non-enhancing, T2-high-signal-intensity lesions in patients with glioblastoma. Korean J Radiol 22:1369–1378. https://doi.org/10.3348/kjr.2020.1272
doi: 10.3348/kjr.2020.1272
pubmed: 33987994
pmcid: 8316772
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
doi: 10.1038/nm.3289
pubmed: 23955713
pmcid: 3769525
Boxerman JL, Quarles CC, Hu LS et al (2020) Consensus recommendations for a dynamic susceptibility contrast MRI protocol for use in high-grade gliomas. Neuro Oncol 22:1262–1275. https://doi.org/10.1093/neuonc/noaa141
doi: 10.1093/neuonc/noaa141
pubmed: 32516388
pmcid: 7523451