Andreas Stadlbauer1, Max Zimmermann1, Karl Rössler1, Stefan Oberndorfer2, Arnd Dörfler3, Michael Buchfelder1, and Gertraud Heinz4
1Department of Neurosurgery, University of Erlangen, Erlangen, Germany, 2Department of Neurology, University Clinic of St. Pölten, St. Pölten, Austria, 3Department of Neuroradiology, University of Erlangen, Erlangen, Germany, 4Department of Radiology, University Clinic of St. Pölten, St. Pölten, Austria
Synopsis
Reprogramming energy metabolism and inducing
angiogenesis are part of the hallmarks of cancer. Thirty-five patients with untreated
or recurrent glioma were examined using vascular architecture mapping (VAM) and
the multiparametric quantitative BOLD (mp-qBOLD) technique for combined
exanimation of oxygen metabolism and angiogenesis in gliomas. Maps of oxygen
extraction fraction (OEF) and cerebral metabolic rate of oxygen (CMRO2)
as well as of the vascular architecture MRI biomarkers microvessel radius (RU), density (NU),
and type indicator (MTI) were calculated. Low-grade glioma showed increased OEF. Glioblastomas showed significantly increased CMRO2
and NU. MTI
demonstrated widespread areas draining venous microvasculature in high-grade gliomas.Purpose
Reprogramming energy metabolism and inducing angiogenesis
are part of the hallmarks of cancer [1]. The WHO classification distinguishes
low grade from high grade gliomas by the presence of microvascular
proliferation as a diagnostic criterion and an independent prognostic parameter
[2]. A biological link between hypoxia and angiogenesis is generally accepted [3].
However, to date the non-invasive in-vivo assessment of angiogenic activity and
oxygen metabolism is still challenging and thus not part of clinical
diagnostics of brain tumors. In this study, we introduce a multiparametric magnetic
resonance imaging (MRI) approach that enables the combined exanimation of
oxygen metabolism and angiogenesis in gliomas.
Methods
Thirty-five patients with untreated or recurrent glioma
(6 WHO°II, 10 WHO°III, 19 glioblastoma) were examined at 3 Tesla (Trio,
Siemens) using vascular architecture mapping (VAM) [4,5] and multiparametric quantitative
BOLD (mp-qBOLD) [6] as part of the routine MRI protocol. For VAM a dual
contrast agent injections approach was used to obtain GE- and SE-EPI DSC
perfusion MRI data [7]. To minimize patient motions the head of the patients
were fixated as well as clear and repeated patient instructions before and
during the MRI examination were provided. To prevent differences in the time to
first-pass peak between the two DSC examinations, a peripheral pulse unit (PPU)
which was fitted to a patient’s finger to monitor heart rate and cardiac cycle
was used. Special attention was paid to perform the two injections at the same
heart rate and exactly at the same phase of the cardiac cycle, i.e. at PPU’s
peak systole signal. For mp-qBOLD, additional T2*- and T2-mapping sequences
were performed. Geometric parameters (in-plane resolution: 1.8 x 1.8 mm, slice
thickness: 4 mm; 29 slices) were identical for VAM and mp-qBOLD sequences.
Custom-made in-house MatLab software was used for VAM and mp-qBOLD data
postprocessing and comprising the following 5 main steps: 1) calculation of CBV and CBF maps from GE-EPI DSC data; 2) calculation of T2* and T2 maps; 3) calculation of maps of the oxygen
metabolism MRI biomarkers oxygen extraction fraction (OEF) and cerebral
metabolic rate of oxygen (CMRO
2); 4) calculation of
ΔR
2,GE versus (ΔR
2,SE)
3/2
diagrams (vascular hysteresis loops, VHLs) from GE- and SE-EPI DSC data; and 5)
calculation of maps of the vascular
architecture MRI biomarkers microvessel radius
(R
U), density (N
U) [8], and type indicator (MTI).
Results and Discussion
Low-grade glioma (LGGs, WHO°II) showed increased
OEF compared to contralateral normal-appearing brain (CNB; p=0.027),
peritumoral (predom. edematous) regions (p=0.028; Fig. 1C, Tab. 1), and
high-grade gliomas (HGGs, WHO°III and IV, p<0.001) (Figs. 2C and 3C). No microvascular
changes due to tumor-induced angiogenic activity within LGGs and their peritumoral
regions (Figs. 1F and 1H), but in
fact a lower microvessel density N
U (Fig. 1G) compared to cNB/peritumoral (p=0.046) and HGGs (p=0.011 and
p<0.001) were detected. The increased OEF values in LGGs might be associated
with an only slightly increased tumor oxygen demand as well as with a lower N
U.
This higher OEF is sufficient to cover the oxygen demand of LGGs. Further
increase in oxygen metabolism due to dedifferentiation of the lesion will
initiate angiogenic processes. HGGs (WHO°III and IV) demonstrated significantly
decreased OEF (Figs. 2C and 3C) and
increased NU values (Figs. 2G
and 3G) compared
to cNB (p=0.005 and p<0.001) (Tab. 1).
Glioblastomas showed large areas with significantly increased CMRO
2
(p<0.001) (Fig 3D, Tab. 1). Areas with highest CMRO
2 values were
positively correlated with high NU (Fig 3G). These
tumor areas with severely increased microvessel density N
U showed, in
turn, only mildly increased to normal microvessel radius RU and vice
versa, i.e. maps of R
U and N
U provided
complementary and inversely correlated information. Maps of MTI allowed
differentiation between supplying arterial (small areas with warm colors) and
draining venous microvasculature (widespread areas, especially in glioblastoma
with cool colors) (Figs. 2H and 3H). These
findings can be interpreted that the excessive oxygen demand in HGGs
(especially in glioblastoma) is covered and even oversupplied by microvascular
changes induced by angiogenesis. The OEF in HGGs is of only the half of that in
cNB sufficient to ensure the increased oxygen demand (detected by an increased
CMRO
2) of the lesions.
Conclusions
Combined assessment of tumor oxygen metabolism
and angiogenesis provide insight into tumor biology and thus may be beneficial
for grading and therapy monitoring of gliomas. However, investigations in more
well-defined patient populations and histological validations are necessary.
Acknowledgements
No acknowledgement found.References
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