Arterial spin labeling perfusion: Prospective MR imaging in differentiating neoplastic from non-neoplastic intra-axial brain lesions

Abstract

Purpose

The purpose of this article is to assess the diagnostic performance of arterial spin-labeling (ASL) magnetic resonance perfusion imaging to differentiate neoplastic from non-neoplastic brain lesions.

Material and methods

This prospective study included 60 consecutive, newly diagnosed, untreated patients with intra-axial lesions with perilesional edema (PE) who underwent clinical magnetic resonance imaging including ASL sequences at 3T. Region of interest analysis was performed to obtain mean cerebral blood flow (CBF) values from lesion (L), PE and normal contralateral white matter (CWM). Normalized (n) CBF ratio was obtained by dividing the mean CBF value of L and PE by mean CBF value of CWM. Discriminant analyses were performed to determine the best cutoff value of nCBFL and nCBFPE in differentiating neoplastic from non-neoplastic lesions.

Results

Thirty patients were in the neoplastic group (15 high-grade gliomas (HGGs), 15 metastases) and 30 in the non-neoplastic group (12 tuberculomas, 10 neurocysticercosis, four abscesses, two fungal granulomas and two tumefactive demyelination) based on final histopathology and clincoradiological diagnosis. We found higher nCBFL (6.65 ± 4.07 vs 1.68 ± 0.80, p < 0.001) and nCBFPE (1.86 ± 1.43 vs 0.74 ± 0.21, p < 0.001) values in the neoplastic group than non-neoplastic. For predicting neoplastic lesions, we found an nCBFL cutoff value of 1.89 (AUC 0.917; 95% CI 0.854 to 0.980; sensitivity 90%; specificity 73%) and nCBFPE value of 0.76 (AUC 0.783; 95% CI 0.675 to 0.891; sensitivity 80%; specificity 58%). Mean nCBFL was higher in HGGs (8.70 ± 4.16) compared to tuberculomas (1.98 ± 0.87); and nCBFPE was higher in HGGs (3.06 ± 1.53) compared to metastases (0.86 ± 0.34) and tuberculomas (0.73 ± 0.22) (p < 0.001).

Conclusion

ASL perfusion may help in distinguishing neoplastic from non-neoplastic brain lesions.

Keywords

Three-dimensional pseudocontinuous arterial spin labeling dynamic susceptibility contrast normalized cerebral blood flow lesion normalized cerebral blood flow peri-lesional edema

Get full access to this article

View all access options for this article.

References

Omuro

Leite

Mokhtari

et al.

Pitfalls in the diagnosis of brain tumours. Lancet Neurol 2006; 5: 937–948.

Law

Yang

Babb

et al.

Comparison of cerebral blood volume and vascular permeability from dynamic susceptibility contrast-enhanced perfusion MR imaging with glioma grade. AJNR Am J Neuroradiol 2004; 25: 746–755.

Wolf

Detre

. Clinical neuroimaging using arterial spin-labeled perfusion magnetic resonance imaging. Neurotherapeutics 2007; 4: 346–359.

Grade

Hernandez Tamames

Pizzini

et al.

A neuroradiologist’s guide to arterial spin labeling MRI in clinical practice. Neuroradiology 2015; 57: 1181–1202.

Soni

Dhanota

DPS

Kumar

et al.

Perfusion MR imaging of enhancing brain tumors: Comparison of arterial spin labeling technique with dynamic susceptibility contrast technique. Neurol India 2017; 65: 1046–1052.

Jiang

Zhao

Zhang

et al.

Comparative analysis of arterial spin labeling and dynamic susceptibility contrast perfusion imaging for quantitative perfusion measurements of brain tumors. Int J Clin Exp Pathol 2014; 7: 2790–2799.

Ata

Turgut

Eraslan

et al.

Comparison between dynamic susceptibility contrast magnetic resonance imaging and arterial spin labeling techniques in distinguishing malignant from benign brain tumors. Eur J Radiol 2016; 85: 1545–1553.

Cha

Lupo

Chen

et al.

Differentiation of glioblastoma multiforme and single brain metastasis by peak height and percentage of signal intensity recovery derived from dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. AJNR Am J Neuroradiol 2007; 28: 1078–1084.

Law

Cha

Knopp

et al.

High-grade gliomas and solitary metastases: Differentiation by using perfusion and proton spectroscopic MR imaging. Radiology 2002; 222: 715–721.

10.

Weber

Zoubaa

Schlieter

et al.

Diagnostic performance of spectroscopic and perfusion MRI for distinction of brain tumors. Neurology 2006; 66: 1899–1906.

11.

Bai Y, Shi D, Wang M, et al. Differentiation between glioblastomas and solitary brain metastases using pseudo-continuous arterial spin labeling perfusion MRI in peritumoral edema. European Congress of Radiology 2013, poster number C-2569.

12.

Tourdias

Rodrigo

Oppenheim

et al.

Pulsed arterial spin labeling applications in brain tumors: Practical review. J Neuroradiol 2008; 35: 79–89.

13.

Gupta

Haris

Husain

et al.

Relative cerebral blood volume is a measure of angiogenesis in brain tuberculoma. J Comput Assist Tomogr 2007; 31: 335–341.

14.

Sankhe

Baheti

Ihare

et al.

Perfusion magnetic resonance imaging characteristics of intracerebral tuberculomas and its role in differentiating tuberculomas from metastases. Acta Radiol 2013; 54: 307–312.

15.

Chatterjee

Saini

Kesavadas

et al.

Differentiation of tubercular infection and metastases presenting as ring enhancing lesion by diffusion and perfusion magnetic resonance imaging. J Neuroradiol 2010; 37: 167–171.

16.

Haller

Zaharchuk

Thomas

et al.

Arterial spin labeling perfusion of the brain: Emerging clinical applications. Radiology 2016; 281: 337–356.

17.

Noguchi

Yakushiji

Nishihara

et al.

Arterial spin-labeling in central nervous system infection. Magn Reson Med Sci 2016; 15: 386–394.

18.

Floriano

Torres

Spotti

et al.

The role of dynamic susceptibility contrast-enhanced perfusion MR imaging in differentiating between infectious and neoplastic focal brain lesions: Results from a cohort of 100 consecutive patients. PLoS One 2013; 8: e81509.

19.

Haris

Gupta

Singh

et al.

Differentiation of infective from neoplastic brain lesions by dynamic contrast-enhanced MRI. Neuroradiology 2008; 50: 531–540.

20.

Hourani

Brant

Rizk

et al.

Can proton MR spectroscopic and perfusion imaging differentiate between neoplastic and nonneoplastic brain lesions in adults?

AJNR Am J Neuroradiol 2008; 292: 366–372.

21.

Kong

Chen

Yang

et al.

A meta-analysis of arterial spin labelling perfusion values for the prediction of glioma grade. Clin Radiol 2017; 72: 255–261.

22.

Liang

Wang

et al.

Meta-analysis of peritumoural rCBV values derived from dynamic susceptibility contrast imaging in differentiating high-grade gliomas from intracranial metastases. Int J Clin Exp Med 2014; 7: 2724–2729.

23.

Bauer

Erly

Moser

et al.

Differentiation of solitary brain metastasis from glioblastoma multiforme: A predictive multiparametric approach using combined MR diffusion and perfusion. Neuroradiology 2015; 57: 697–703.

24.

Zhang

Olsson

. Hematogenous metastases of the human brain—Characteristics of peritumoral brain changes: A review. J Neurooncol 1997; 35: 81–89.

25.

Lin

Xue

Duan

et al.

The role of cerebral blood flow gradient in peritumoral edema for differentiation of glioblastomas from solitary metastatic lesions. Oncotarget 2016; 7: 69051–69059.

26.

Sunwoo

Yun

You

et al.

Differentiation of glioblastoma from brain metastasis: Qualitative and quantitative analysis using arterial spin labeling MR imaging. PloS One 2016; 11: e0166662.

27.

Long

. Capillary ultrastructure in human metastatic brain tumors. J Neurosurg 1979; 51: 53–58.

28.

Hossman

Blöink

. Blood flow and regulation of blood flow in experimental peritumoral edema. Stroke 1981; 12: 211–217.

29.

Bulakbasi

Kocaoglu

Farzaliyev

et al.

Assessment of diagnostic accuracy of perfusion MR imaging in primary and metastatic solitary malignant brain tumors. AJNR Am J Neuroradiol 2005; 26: 2187–2199.

30.

Batra

Tripathi

. Perfusion magnetic resonance imaging in intracerebral parenchymal tuberculosis: Preliminary findings. J Comput Assist Tomogr 2003; 27: 882–888.

31.

Gupta

Haris

Husain

et al.

Relative cerebral blood volume is a measure of angiogenesis in brain tuberculoma. J Comput Assist Tomogr 2007; 31: 335–341.

32.

Haris

Gupta

Husain

et al.

Assessment of therapeutic response in brain tuberculomas using serial dynamic contrast-enhanced MRI. Clin Radiol 2008; 63: 562–574.

33.

Gupta

Awasthi

Garg

et al.

T1-weighted dynamic contrast-enhanced MR evaluation of different stages of neurocysticercosis and its relationship with serum MMP-9 expression. AJNR Am J Neuroradiol 2013; 34: 997–1003.

34.

Holmes

Petrella

Provenzale

. Distinction between cerebral abscesses and high-grade neoplasms by dynamic susceptibility contrast perfusion MRI. AJR Am J Roentgenol 2004; 183: 1247–1252.

35.

Erdogan

Hakyemez

Yildirim

et al.

Brain abscess and cystic brain tumor: Discrimination with dynamic susceptibility contrast perfusion-weighted MRI. J Comput Assist Tomogr 2005; 29: 663–667.

36.

Cha

Pierce

Knopp

et al.

Dynamic contrast-enhanced T2*-weighted MR imaging of tumefactive demyelinating lesions. AJNR Am J Neuroradiol 2001; 22: 1109–1116.

37.

Blasel

Pfeilschifter

Jansen

et al.

Metabolism and regional cerebral blood volume in autoimmune inflammatory demyelinating lesions mimicking malignant gliomas. J Neurol 2011; 258: 113–122.

38.

Hiremath

Muraleedharan

Kumar

et al.

Combining diffusion tensor metrics and DSC perfusion imaging: Can it improve the diagnostic accuracy in differentiating tumefactive demyelination from high-grade glioma?

AJNR Am J Neuroradiol 2017; 38: 685–690.

39.

Young

Babb

Law

et al.

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 2007; 26: 1053–1063.

40.

Wetzel

Cha

Johnson

et al.

Relative cerebral blood volume measurements in intracranial mass lesions: Interobserver and intraobserver reproducibility study. Radiology 2002; 224: 797–803.

41.

Emblem

Nedregaard

Nome

et al.

Glioma grading by using histogram analysis of blood volume heterogeneity from MR-derived cerebral blood volume maps. Radiology 2008; 247: 808–817.

42.

Soni

Jain

Kumar

et al.

Arterial spin labeling magnetic resonance perfusion study to evaluate the effects of age and gender on normal cerebral blood flow. Neurol India 2016; 64 (Suppl): S32–S38.

43.

Soni

. Author’s reply: Arterial spin labeling. Neurol India 2018; 66: 285.