Comparative study of pulsed-continuous arterial spin labeling and dynamic susceptibility contrast imaging by histogram analysis in evaluation of glial tumors
Arterial spin labeling (ASL) is a non-invasive perfusion technique that may be an alternative to dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) for assessment of brain tumors. To our knowledge, there have been no reports on histogram analysis of ASL. The purpose of this study was to determine whether ASL is comparable with DSC-MRI in terms of differentiating high-grade and low-grade gliomas by evaluating the histogram analysis of cerebral blood flow (CBF) in the entire tumor.
Thirty-four patients with pathologically proven glioma underwent ASL and DSC-MRI. High-signal areas on contrast-enhanced T1-weighted images or high-intensity areas on fluid-attenuated inversion recovery images were designated as the volumes of interest (VOIs). ASL-CBF, DSC-CBF, and DSC-cerebral blood volume maps were constructed and co-registered to the VOI. Perfusion histogram analyses of the whole VOI and statistical analyses were performed to compare the ASL and DSC images.
There was no significant difference in the mean values for any of the histogram metrics in both of the low-grade gliomas (n = 15) and the high-grade gliomas (n = 19). Strong correlations were seen in the 75th percentile, mean, median, and standard deviation values between the ASL and DSC images. The area under the curve values tended to be greater for the DSC images than for the ASL images.
DSC-MRI is superior to ASL for distinguishing high-grade from low-grade glioma. ASL could be an alternative evaluation method when DSC-MRI cannot be used, e.g., in patients with renal failure, those in whom repeated examination is required, and in children.
KeywordsDynamic susceptibility contrast imaging Glial tumors Histogram analysis Pulsed-continuous arterial spin labeling
Compliance with ethical standards
No funding was received for this study.
Conflict of interest
The authors declare that they have no conflict of interest.
All procedures performed in the studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
For this type of study formal consent is not required.
- 1.Law M, Young R, Babb J, Rad M, Sasaki T, Zagzag D, Johnson G (2006) Comparing perfusion metrics obtained from a single compartment versus pharmacokinetic modeling methods using dynamic susceptibility contrast-enhanced perfusion MR imaging with glioma grade. AJNR Am J Neuroradiol 27:1975–1982PubMedGoogle Scholar
- 2.Tietze A, Mouridsen K, Lassen-Ramshad Y, Ostergaard L (2015) Perfusion MRI derived indices of microvascular shunting and flow control correlate with tumor grade and outcome in patients with cerebral glioma. PLoS One 10:e0123044. https://doi.org/10.1371/journal.pone.0123044 CrossRefPubMedPubMedCentralGoogle Scholar
- 4.Wintermark M, Sesay M, Barbier E, Borbely K, Dillon WP, Eastwood JD, Glenn TC, Grandin CB, Pedraza S, Soustiel JF, Nariai T, Zaharchuk G, Caille JM, Dousset V, Yonas H (2005) Comparative overview of brain perfusion imaging techniques. Stroke 36:e83–e99. https://doi.org/10.1161/01.STR.0000177884.72657.8b CrossRefPubMedGoogle Scholar
- 6.Young R, Babb J, Law M, Pollack E, Johnson G (2007) Comparison of region-of-interest analysis with three different histogram analysis methods in the determination of perfusion metrics in patients with brain gliomas. J Magn Reson Imaging 26:1053–1063. https://doi.org/10.1002/jmri.21064 CrossRefPubMedGoogle Scholar
- 7.Rau MK, Braun C, Skardelly M, Schittenhelm J, Paulsen F, Bender B, Ernemann U, Bisdas S (2014) Prognostic value of blood flow estimated by arterial spin labeling and dynamic susceptibility contrast-enhanced MR imaging in high-grade gliomas. J Neuro-Oncol 120:557–566. https://doi.org/10.1007/s11060-014-1586-z CrossRefGoogle Scholar
- 10.Kanda T, Fukusato T, Matsuda M, Toyoda K, Oba H, Kotoku J, Haruyama T, Kitajima K, Furui S (2015) Gadolinium-based contrast agent accumulates in the brain even in subjects without severe renal dysfunction: evaluation of autopsy brain specimens with inductively coupled plasma mass spectroscopy. Radiology 276:228–232. https://doi.org/10.1148/radiol.2015142690 CrossRefPubMedGoogle Scholar
- 11.Murata N, Gonzalez-Cuyar LF, Murata K, Fligner C, Dills R, Hippe D, Maravilla KR (2016) Macrocyclic and other non-group 1 gadolinium contrast agents deposit low levels of gadolinium in brain and bone tissue: preliminary results from 9 patients with normal renal function. Investig Radiol 51:447–453. https://doi.org/10.1097/RLI.0000000000000252 CrossRefGoogle Scholar
- 12.Weber MA, Gunther M, Lichy MP, Delorme S, Bongers A, Thilmann C, Essig M, Zuna I, Schad LR, Debus J, Schlemmer HP (2003) Comparison of arterial spin-labeling techniques and dynamic susceptibility-weighted contrast-enhanced MRI in perfusion imaging of normal brain tissue. Investig Radiol 38:712–718. https://doi.org/10.1097/01.rli.0000084890.57197.54 CrossRefGoogle Scholar
- 13.Hirai T, Kitajima M, Nakamura H, Okuda T, Sasao A, Shigematsu Y, Utsunomiya D, Oda S, Uetani H, Morioka M, Yamashita Y (2011) Quantitative blood flow measurements in gliomas using arterial spin-labeling at 3T: intermodality agreement and inter- and intraobserver reproducibility study. AJNR Am J Neuroradiol 32:2073–2079. https://doi.org/10.3174/ajnr.A2725 CrossRefPubMedGoogle Scholar
- 14.Lehmann P, Monet P, de Marco G, Saliou G, Perrin M, Stoquart-Elsankari S, Bruniau A, Vallee JN (2010) A comparative study of perfusion measurement in brain tumours at 3 tesla MR: arterial spin labeling versus dynamic susceptibility contrast-enhanced MRI. Eur Neurol 64:21–26. https://doi.org/10.1159/000311520 CrossRefPubMedGoogle Scholar
- 15.Cebeci H, Aydin O, Ozturk-Isik E, Gumus C, Inecikli F, Bekar A, Kocaeli H, Hakyemez B (2014) Assesment of perfusion in glial tumors with arterial spin labeling; comparison with dynamic susceptibility contrast method. Eur J Radiol 83:1914–1919. https://doi.org/10.1016/j.ejrad.2014.07.002 CrossRefPubMedGoogle Scholar
- 18.Abe T, Mizobuchi Y, Sako W, Irahara S, Otomi Y, Obama Y, Nakajima K, Khashbat D, Majigsuren M, Kageji T, Nagahiro S, Harada M (2015) Clinical significance of discrepancy between arterial spin labeling images and contrast-enhanced images in the diagnosis of brain tumors. Magn Reson Med Sci 14:313–319. https://doi.org/10.2463/mrms.2014-0083 CrossRefPubMedGoogle Scholar
- 20.Roy B, Awasthi R, Bindal A, Sahoo P, Kumar R, Behari S, Ojha B, Husain N, Pandey C, Rathore R, Gupta R (2013) Comparative evaluation of 3-dimensional pseudocontinuous arterial spin labeling with dynamic contrast-enhanced perfusion magnetic resonance imaging in grading of human glioma. J Comput Assist Tomogr 37:321–326. https://doi.org/10.1097/RCT.0b013e318282d7e2 CrossRefPubMedGoogle Scholar
- 25.Arisawa A, Watanabe Y, Tanaka H, Takahashi H, Matsuo C, Fujiwara T, Fujimoto Y, Yamamoto K, Tomiyama N (2017) Vessel-masked perfusion magnetic resonance imaging with histogram analysis improves diagnostic accuracy for the grading of glioma. J Comput Assist Tomogr 41:910–915. https://doi.org/10.1097/RCT.0000000000000614 CrossRefPubMedGoogle Scholar
- 28.Jung SC, Choi SH, Yeom JA, Kim JH, Ryoo I, Kim SC, Shin H, Lee AL, Yun TJ, Park CK, Sohn CH, Park SH (2013) Cerebral blood volume analysis in glioblastomas using dynamic susceptibility contrast-enhanced perfusion MRI: a comparison of manual and semiautomatic segmentation methods. PLoS One 8:e69323. https://doi.org/10.1371/journal.pone.0069323 CrossRefPubMedPubMedCentralGoogle Scholar
- 31.Reishofer G, Koschutnig K, Enzinger C, Ischebeck A, Keeling S, Stollberger R, Ebner F (2011) Automated macrovessel artifact correction in dynamic susceptibility contrast magnetic resonance imaging using independent component analysis. Magn Reson Med 65:848–857. https://doi.org/10.1002/mrm.22660 CrossRefPubMedGoogle Scholar
- 33.van Westen D, Petersen ET, Wirestam R, Siemund R, Bloch KM, Stahlberg F, Bjorkman-Burtscher IM, Knutsson L (2011) Correlation between arterial blood volume obtained by arterial spin labelling and cerebral blood volume in intracranial tumours. MAGMA 24:211–223. https://doi.org/10.1007/s10334-011-0255-x CrossRefPubMedGoogle Scholar
- 35.White CM, Pope WB, Zaw T, Qiao J, Naeini KM, Lai A, Nghiemphu PL, Wang JJ, Cloughesy TF, Ellingson BM (2014) Regional and voxel-wise comparisons of blood flow measurements between dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) and arterial spin labeling (ASL) in brain tumors. J Neuroimaging 24:23–30. https://doi.org/10.1111/j.1552-6569.2012.00703.x CrossRefPubMedGoogle Scholar
- 36.Dangouloff-Ros V, Deroulers C, Foissac F, Badoual M, Shotar E, Grévent D, Calmon R, Pagès M, Grill J, Dufour C, Blauwblomme T, Puget S, Zerah M, Sainte-Rose C, Brunelle F, Varlet P, Boddaert N (2016) Arterial spin labeling to predict brain tumor grading in children: correlations between histopathologic vascular density and perfusion MR imaging. Radiology 281:553–566CrossRefPubMedGoogle Scholar
- 38.Jarnum H, Steffensen EG, Knutsson L, Frund ET, Simonsen CW, Lundbye-Christensen S, Shankaranarayanan A, Alsop DC, Jensen FT, Larsson EM (2010) Perfusion MRI of brain tumours: a comparative study of pseudo-continuous arterial spin labelling and dynamic susceptibility contrast imaging. Neuroradiology 52:307–317. https://doi.org/10.1007/s00234-009-0616-6 CrossRefPubMedGoogle Scholar
- 40.Noguchi T, Yoshiura T, Hiwatashi A, Togao O, Yamashita K, Nagao E, Shono T, Mizoguchi M, Nagata S, Sasaki T, Suzuki SO, Iwaki T, Kobayashi K, Mihara F, Honda H (2008) Perfusion imaging of brain tumors using arterial spin-labeling: correlation with histopathologic vascular density. AJNR Am J Neuroradiol 29:688–693. https://doi.org/10.3174/ajnr.A0903 CrossRefPubMedGoogle Scholar
- 41.Ningning D, Haopeng P, Xuefei D, Wenna C, Yan R, Jingsong W, Chengjun Y, Zhenwei Y, Xiaoyuan F (2017) Perfusion imaging of brain gliomas using arterial spin labeling: correlation with histopathological vascular density in MRI-guided biopsies. Neuroradiology 59:51–59. https://doi.org/10.1007/s00234-016-1756-0 CrossRefPubMedGoogle Scholar
- 42.Rogers TW, Toor G, Drummond K, Love C, Field K, Asher R, Tsui A, Buckland M, Gonzales M (2018) The 2016 revision of the WHO classification of central nervous system tumours: retrospective application to a cohort of diffuse gliomas. J Neuro-Oncol 137:181–189. https://doi.org/10.1007/s11060-017-2710-7 CrossRefGoogle Scholar
- 43.Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, Ohgaki H, Wiestler OD, Kleihues P, Ellison DW (2016) The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 131:803–820. https://doi.org/10.1007/s00401-016-1545-1 CrossRefPubMedGoogle Scholar
- 44.Lin Y, Xing Z, She D, Yang X, Zheng Y, Xiao Z, Wang X, Cao D (2017) IDH mutant and 1p/19q co-deleted oligodendrogliomas: tumor grade stratification using diffusion-, susceptibility-, and perfusion-weighted MRI. Neuroradiology 59:555–562. https://doi.org/10.1007/s00234-017-1839-6 CrossRefPubMedPubMedCentralGoogle Scholar
- 45.Xing Z, Yang X, She D, Lin Y, Zhang Y, Cao D (2017) Noninvasive assessment of IDH mutational status in World Health Organization grade II and III astrocytomas using DWI and DSC-PWI combined with conventional MR imaging. AJNR Am J Neuroradiol 38:1138–1144. https://doi.org/10.3174/ajnr.A5171 CrossRefPubMedGoogle Scholar
- 46.Brendle C, Hempel JM, Schittenhelm J, Skardelly M, Tabatabai G, Bender B, Ernemann U, Klose U (2017) Glioma grading and determination of IDH mutation status and ATRX loss by DCE and ASL perfusion. Clin Neuroradiol. https://doi.org/10.1007/s00062-017-0590-z