Assessment of brain tumors by magnetic resonance dynamic susceptibility contrast perfusion-weighted imaging and computed tomography perfusion: a comparison study.
Agreement
Brain tumors
Computed tomography perfusion
Magnetic resonance perfusion-weighted Imaging
Journal
La Radiologia medica
ISSN: 1826-6983
Titre abrégé: Radiol Med
Pays: Italy
ID NLM: 0177625
Informations de publication
Date de publication:
Jun 2022
Jun 2022
Historique:
received:
21
09
2021
accepted:
11
02
2022
pubmed:
21
4
2022
medline:
27
5
2022
entrez:
20
4
2022
Statut:
ppublish
Résumé
To investigate the association and agreement between magnetic resonance dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) and computed tomography perfusion (CTP) in determining vascularity and permeability of primary and secondary brain tumors. DSC-PWI and CTP studies from 97 patients with high-grade glioma, low-grade glioma and solitary brain metastasis were retrospectively reviewed. Normalized cerebral blood flow (nCBF), cerebral blood volume (nCBV), capillary transfer constant (nK2) and permeability surface area product (nPS) values were obtained. Variables among groups were compared, and correlation and agreement between DSC-PWI and CTP were tested. All DSC-PWI and CTP parameters were higher in high-grade than in low-grade gliomas (p < 0.01 and p < 0.001). Metastases had greater DSC-PWI nCBV (p < 0.05), nCTP-CBF (p < 0.05), nCTP-CBV (p < 0.01) and nCTP-PS (p < 0.0001) than low-grade gliomas and more elevated nCTP-PS (p < 0.01) than high-grade gliomas. The correlation was strong between DSC-PWI nCBF and CTP nCBF (r = 0.79; p < 0.00001) and between DSC-PWI nCBV and CTP nCBV (r = 0.83; p < 0.00001), weaker between DSC-PWI nK2 and CTP nPS (r = 0.29; p < 0.01). Bland-Altman plots indicated that the agreement was strong between DSC-PWI nCBF and CTP nCBF, good between DSC-PWI nCBV and CTP nCBV and poorer between DSC-PWI nK2 and CTP nPS. DSC-PWI and CTP CBF and CBV maps were comparable and interchangeable in the assessment of tumor vascularity, unlike DSC-PWI K2 and CTP PS maps that were more discordant in the analysis of tumor permeability. CTP could be an alternative method to quantify tumor neoangiogenesis when MRI is not available or when the patient does not tolerate it.
Identifiants
pubmed: 35441970
doi: 10.1007/s11547-022-01470-z
pii: 10.1007/s11547-022-01470-z
doi:
Substances chimiques
Contrast Media
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
664-672Informations de copyright
© 2022. Italian Society of Medical Radiology.
Références
Griffith B, Jain R (2016) Perfusion imaging in neuro-oncology: basic techniques and clinical applications. Magn Reson Imaging Clin N Am 24:765–779
pubmed: 27742116
doi: 10.1016/j.mric.2016.07.004
Eilaghi A, Yeung T, d’Esterre C et al (2016) Quantitative perfusion and permeability biomarkers in brain cancer from tomographic CT and MR images. Biomark Cancer 8(Suppl 2):47–59
pubmed: 27398030
pmcid: 4933536
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
pubmed: 32516388
pmcid: 7523451
doi: 10.1093/neuonc/noaa141
Thompson G, Mills SJ, Stivaros SM et al (2010) Imaging of brain tumors: perfusion/permeability. Neuroimaging Clin N Am 20:337–353
pubmed: 20708550
doi: 10.1016/j.nic.2010.04.008
Geer CP, Simonds J, Anvery A et al (2012) Does MR perfusion imaging impact management decisions for patients with brain tumors? A prospective study. AJNR Am J Neuroradiol 33:556–562
pubmed: 22116105
pmcid: 7966417
doi: 10.3174/ajnr.A2811
Skinner JT, Moots PL, Ayers GD et al (2016) On the Use of DSC-MRI for measuring vascular permeability. AJNR Am J Neuroradiol 37:80–87
pubmed: 26427833
pmcid: 4713276
doi: 10.3174/ajnr.A4478
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
pubmed: 16611779
pmcid: 8134002
Fainardi E, Di Biase F, Borrelli M et al (2010) Potential role of CT perfusion parameters in the identification of solitary intra-axial brain tumor grading. Acta Neurochir Suppl 106:283–287
pubmed: 19812965
doi: 10.1007/978-3-211-98811-4_53
Jain R (2011) Perfusion CT imaging of brain tumors: an overview. AJNR Am J Neuroradiol 32:1570–1577
pubmed: 21051510
pmcid: 7965371
doi: 10.3174/ajnr.A2263
Yeung TP, Bauman G, Yartsev S et al (2015) Dynamic perfusion CT in brain tumors. Eur J Radiol 84:2386–2392
pubmed: 25796424
doi: 10.1016/j.ejrad.2015.02.012
Yeung TP, Wang Y, He W et al (2015) Survival prediction in high-grade gliomas using CT perfusion imaging. J Neurooncol 123:93–102
pubmed: 25862005
doi: 10.1007/s11060-015-1766-5
Gadda D, Simonelli P, Villa G et al (2011) Intracranial masses with perilesional edema: differential diagnosis with Perfusion CT. Neuroradiol J 24:345–349
pubmed: 24059656
doi: 10.1177/197140091102400302
De Simone M, Muccio CF, Pagnotta SM et al (2013) Comparison between CT and MR in perfusion imaging assessment of high-grade gliomas. Radiol Med 118:140–151
pubmed: 22430675
doi: 10.1007/s11547-012-0801-5
Coolens C, Driscoll B, Foltz W et al (2016) Comparison of voxel-wise tumor perfusion changes measured with dynamic contrast-enhanced (DCE) MRI and volumetric DCE CT in patients with metastatic brain cancer treated with radiosurgery. Tomography 2:325–333
pubmed: 30042966
pmcid: 6037934
doi: 10.18383/j.tom.2016.00178
Jia ZZ, Shi W, Shi JL et al (2017) Comparison between perfusion computed tomography and dynamic contrast-enhanced magnetic resonance imaging in assessing glioblastoma microvasculature. Eur J Radiol 87:120–124
pubmed: 28034567
doi: 10.1016/j.ejrad.2016.12.016
Østergaard L, Sorensen A, Kwong K et al (1996) High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part II: experimental comparison and preliminary results. Magnet Reson Med 36:726–736
doi: 10.1002/mrm.1910360511
Cenic A, Nabavi DG, Craen RA et al (2000) A CT method to measure hemodynamics in brain tumors: validation and application of cerebral blood flow maps. AJNR Am J Neuroradiol 21:462–470
pubmed: 10730636
pmcid: 8174983
Yeung TPC, Yartsev Y, Lee TY et al (2014) Relationship of computed tomography perfusion and positron emission tomography to tumour progression in malignant glioma. J Med Radiat Sci 61:4–13
pubmed: 26229630
pmcid: 4175825
doi: 10.1002/jmrs.37
Luan J, Wu M, Wang X et al (2020) The diagnostic value of quantitative analysis of ASL, DSC-MRI and DKI in the grading of cerebral gliomas: a meta-analysis. Radiat Oncol 15:204
pubmed: 32831106
pmcid: 7444047
doi: 10.1186/s13014-020-01643-y
Hakyemez B, Erdogan C, Ercan I et al (2005) High-grade and low-grade gliomas: differentiation by using perfusion MR imaging. Clin Radiol 60:493–502
pubmed: 15767107
doi: 10.1016/j.crad.2004.09.009
Server A, Graff BA, Orheim TE et al (2011) Measurements of diagnostic examination performance and correlation analysis using microvascular leakage, cerebral blood volume, and blood flow derived from 3T dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging in glial tumor grading. Neuroradiology 53:435–447
pubmed: 20857284
doi: 10.1007/s00234-010-0770-x
Waqar M, Lewis D, Agushi E et al (2021) Cerebral and tumoral blood flow in adult gliomas: a systematic review of results from magnetic resonance imaging. Br J Radiol 194:20201450
doi: 10.1259/bjr.20201450
Provenzale JM, Wang GR, Brenner T et al (2002) Comparison of permeability in high-grade and low-grade brain tumors using dynamic susceptibility contrast MR imaging. AJR Am J Roentgenol 178:711–716
pubmed: 11856703
doi: 10.2214/ajr.178.3.1780711
Jain R, Griffith B, Alotaibi F et al (2015) Glioma angiogenesis and perfusion imaging: understanding the relationship between tumor blood volume and leakiness with increasing glioma grade. AJNR Am J Neuroradiol 36:2030–5203
pubmed: 26206809
pmcid: 7964882
doi: 10.3174/ajnr.A4405
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
pubmed: 32821962
doi: 10.1007/s00234-020-02522-9
Lee YJ, Ahn KJ, Kim BS et al (2012) Role of perfusion CT in differentiating between various cerebral masses using normalized permeability surface area product and cerebral blood volume. Clin Imaging 36:680–687
pubmed: 23153995
doi: 10.1016/j.clinimag.2012.01.029
Onishi S, Kajiwara Y, Takayasu T et al (2018) Perfusion computed tomography parameters are useful for differentiating glioblastoma, lymphoma, and metastasis. World Neurosurg 119:e890–e897
pubmed: 30099179
doi: 10.1016/j.wneu.2018.07.291
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
pubmed: 21511863
pmcid: 8013151
doi: 10.3174/ajnr.A2441
Lu S, Gao Q, Yu J et al (2016) Utility of dynamic contrast-enhanced magnetic resonance imaging for differentiating glioblastoma, primary central nervous system lymphoma and brain metastatic tumor. Eur J Radiol 85:1722–1727
pubmed: 27666608
doi: 10.1016/j.ejrad.2016.07.005
Server A, Orheim TE, Graff BA et al (2011) Diagnostic examination performance by using microvascular leakage, cerebral blood volume, and blood flow derived from 3-T dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging in the differentiation of glioblastoma multiforme and brain metastasis. Neuroradiology 53:319–330
pubmed: 20625709
doi: 10.1007/s00234-010-0740-3
Xyda A, Haberland U, Klotz E et al (2012) Diagnostic performance of whole brain volume perfusion CT in intra-axial brain tumors: preoperative classification accuracy and histopathologic correlation. Eur J Radiol 81:4105–4111
pubmed: 22959826
doi: 10.1016/j.ejrad.2012.08.005
Jain RK, di Tomaso E, Duda DG et al (2007) Angiogenesis in brain tumours. Nat Rev Neurosci 8:610–622
pubmed: 17643088
doi: 10.1038/nrn2175
Cacho-Díaz B, García-Botello DR, Wegman-Ostrosky T et al (2020) Tumor microenvironment differences between primary tumor and brain metastases. J Transl Med 18:1
pubmed: 31900168
pmcid: 6941297
doi: 10.1186/s12967-019-02189-8
Paulson ES, Schmainda KM (2008) Comparison of dynamic susceptibility-weighted contrast-enhanced MR methods: recommendations for measuring relative cerebral blood volume in brain tumors. Radiology 249:601–613
pubmed: 18780827
pmcid: 2657863
doi: 10.1148/radiol.2492071659
Choyke PL (2005) Contrast agents for imaging tumor angiogenesis: is bigger better? Radiology 235:1–2
pubmed: 15798159
doi: 10.1148/radiol.2351041773
Heye AK, Culling RD, Valdés Hernández MDC, Thrippleton MJ, Wardlaw JM (2014) Assessment of blood-brain barrier disruption using dynamic contrast-enhanced MRI. A systematic review. Neuroimage Clin 6:262–274
pubmed: 25379439
pmcid: 4215461
doi: 10.1016/j.nicl.2014.09.002
Rimner A, Holodny AI, Hochberg FH (2006) Perfusion magnetic resonance imaging to assess brain tumor responses to new therapies. US Neurol Dis 1:1–6
Cha S, Yang L, Johnson G et al (2006) Comparison of microvascular permeability measurements, Ktrans, determined with conventional steady-state T1-Weighted and first-pass T2*-Weighted MR imaging methods in gliomas and meningiomas. AJNR Am J Neuroradiol 27:409–417
pubmed: 16484420
pmcid: 8148770