Abstract
The structure and organization of blood vessels within tumor tissue is very different from that seen in normal tissues. Tumor blood vessels show abnormalities in microstructure and hierarchical organization, which result from multiple factors including local tumor characteristics, angiogenic drive, and the ability of the angiogenic process to keep pace with tumor growth. Tumor microvasculature is inefficient compared to that seen in normal tissues, and, particularly in rapidly growing tumors, blood flow is often inadequate to meet the demands for oxygen and nutrient delivery and clearance of waste material. Understanding the microvascular environment and its variation between and within tumors is critical for an understanding of tumor behavior and therapeutic response. A wide range of quantitative imaging techniques have been developed in an attempt to provide noninvasive, repeatable assays of microvascular characteristics which can then be studied in terms of their spatial variability and change over time. This chapter reviews the currently available imaging biomarkers and their current clinical application.
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References
Hanahan D, Weinberg RA (2000) The hallmarks of cancer. Cell 100(1):57–70
Patan S (2004) Vasculogenesis and angiogenesis. Cancer Treat Res 117:3–32
Carmeliet P, Jain RK (2011) Molecular mechanisms and clinical applications of angiogenesis. Nature 473(7347):298–307
Cheng SY, Huang HJ, Nagane M, Ji XD, Wang D, Shih CC et al (1996) Suppression of glioblastoma angiogenicity and tumorigenicity by inhibition of endogenous expression of vascular endothelial growth factor. Proc Natl Acad Sci U S A 93(16):8502–8507
Chen HX, Cleck JN (2009) Adverse effects of anticancer agents that target the VEGF pathway. Nat Rev Clin Oncol 6(8):465–477
Nico B, Benagiano V, Mangieri D, Maruotti N, Vacca A, Ribatti D (2008) Evaluation of microvascular density in tumors: pro and contra. Histol Histopathol 23(5):601–607
Yao WW, Zhang H, Ding B, Fu T, Jia H, Pang L et al (2011) Rectal cancer: 3D dynamic contrast-enhanced MRI; correlation with microvascular density and clinicopathological features. Radiol Med 116(3):366–374
Jackson A, Kassner A, Annesley-Williams D, Reid H, Zhu XP, Li KL (2002) Abnormalities in the recirculation phase of contrast agent bolus passage in cerebral gliomas: comparison with relative blood volume and tumor grade. AJNR Am J Neuroradiol 23(1):7–14
Cha S, Lupo JM, Chen MH, Lamborn KR, McDermott MW, Berger MS et al (2007) 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 28(6):1078–1084
Thacker NA, Scott ML, Jackson A (2003) Can dynamic susceptibility contrast magnetic resonance imaging perfusion data be analyzed using a model based on directional flow? J Magn Reson Imaging 17(2):241–255
Jackson A, Li KL, Zhu X (2014) Semi-quantitative parameter analysis of DCE-MRI revisited: monte-carlo simulation, clinical comparisons, and clinical validation of measurement errors in patients with type 2 neurofibromatosis. PLoS One 9(3):e90300
Jackson A (2004) Analysis of dynamic contrast enhanced MRI. Br J Radiol 77(Spec No 2):S154–S166
O’Connor JP, Jackson A, Parker GJ, Roberts C, Jayson GC (2012) Dynamic contrast-enhanced MRI in clinical trials of antivascular therapies. Nat Rev Clin Oncol 9(3):167–177
Tofts PS, Kermode AG (1991) Measurement of the blood-brain barrier permeability and leakage space using dynamic MR imaging. 1. Fundamental concepts. Magn Reson Med 17(2):357–367
Leach MO, Brindle KM, Evelhoch JL, Griffiths JR, Horsman MR, Jackson A et al (2005) The assessment of antiangiogenic and antivascular therapies in early-stage clinical trials using magnetic resonance imaging: issues and recommendations. Br J Cancer 92(9):1599–1610
Naish JH, McGrath DM, Bains LJ, Passera K, Roberts C, Watson Y et al (2011) Comparison of dynamic contrast-enhanced MRI and dynamic contrast-enhanced CT biomarkers in bladder cancer. Magn Reson Med 66(1):219–226
Jain RK (2001) Normalizing tumor vasculature with anti-angiogenic therapy: a new paradigm for combination therapy. Nat Med 7(9):987–989
Schmainda KM, Rand SD, Joseph AM, Lund R, Ward BD, Pathak AP et al (2004) Characterization of a first-pass gradient-echo spin-echo method to predict brain tumor grade and angiogenesis. AJNR Am J Neuroradiol 25(9):1524–1532
Batchelor TT, Sorensen AG, di Tomaso E, Zhang WT, Duda DG, Cohen KS et al (2007) AZD2171, a pan-VEGF receptor tyrosine kinase inhibitor, normalizes tumor vasculature and alleviates edema in glioblastoma patients. Cancer Cell 11(1):83–95
Sorensen AG, Batchelor TT, Zhang WT, Chen PJ, Yeo P, Wang M et al (2009) A “vascular normalization index” as potential mechanistic biomarker to predict survival after a single dose of cediranib in recurrent glioblastoma patients. Cancer Res 69(13):5296–5300
Emblem KE, Mouridsen K, Bjornerud A, Farrar CT, Jennings D, Borra RJ et al (2013) Vessel architectural imaging identifies cancer patient responders to anti-angiogenic therapy. Nat Med 19(9):1178–1183
St Lawrence K, Verdecchia K, Elliott J, Tichauer K, Diop M, Hoffman L et al (2013) Kinetic model optimization for characterizing tumour physiology by dynamic contrast-enhanced near-infrared spectroscopy. Phys Med Biol 58(5):1591–1604
Jensen RL, Mumert ML, Gillespie DL, Kinney AY, Schabel MC, Salzman KL (2014) Preoperative dynamic contrast-enhanced MRI correlates with molecular markers of hypoxia and vascularity in specific areas of intratumoral microenvironment and is predictive of patient outcome. Neuro Oncol 16(2):280–291
LaViolette PS, Cohen AD, Prah MA, Rand SD, Connelly J, Malkin MG et al (2013) Vascular change measured with independent component analysis of dynamic susceptibility contrast MRI predicts bevacizumab response in high-grade glioma. Neuro Oncol 15(4):442–450
LaViolette PS, Daun MK, Paulson ES, Schmainda KM (2014) Effect of contrast leakage on the detection of abnormal brain tumor vasculature in high-grade glioma. J Neurooncol 116(3):543–549
Liu Z, Liao H, Yin J, Li Y (2014) Using R2* values to evaluate brain tumours on magnetic resonance imaging: preliminary results. Eur Radiol 24(3):693–702
Linnik IV, Scott ML, Holliday KF, Woodhouse N, Waterton JC, O’Connor JP et al (2013) Noninvasive tumor hypoxia measurement using magnetic resonance imaging in murine U87 glioma xenografts and in patients with glioblastoma. Magn Reson Med
Bruehlmeier M, Roelcke U, Schubiger PA, Ametamey SM (2004) Assessment of hypoxia and perfusion in human brain tumors using PET with 18F-fluoromisonidazole and 15O-H2O. J Nucl Med 45(11):1851–1859
Takada Y, Ye X, Simon S (2007) The integrins. Genome Biol 8(5):215
Desgrosellier JS, Cheresh DA (2010) Integrins in cancer: biological implications and therapeutic opportunities. Nat Rev Cancer 10(1):9–22
Haubner R, Kuhnast B, Mang C, Weber WA, Kessler H, Wester HJ et al (2004) [18F]Galacto-RGD: synthesis, radiolabeling, metabolic stability, and radiation dose estimates. Bioconjug Chem 15(1):61–69
Schittenhelm J, Schwab EI, Sperveslage J, Tatagiba M, Meyermann R, Fend F et al (2013) Longitudinal expression analysis of alpha v integrins in human gliomas reveals upregulation of integrin alpha v beta 3 as a negative prognostic factor. J Neuropathol Exp Neurol 72(3):194–210
Schnell O, Krebs B, Carlsen J, Miederer I, Goetz C, Goldbrunner RH et al (2009) Imaging of integrin alpha(v)beta(3) expression in patients with malignant glioma by [18F] Galacto-RGD positron emission tomography. Neuro Oncol 11(6):861–870
Liu S, Hsieh WY, Jiang Y, Kim YS, Sreerama SG, Chen X et al (2007) Evaluation of a (99m)Tc-labeled cyclic RGD tetramer for noninvasive imaging integrin alpha(v)beta3-positive breast cancer. Bioconjug Chem 18(2):438–446
Li ZB, Cai W, Cao Q, Chen K, Wu Z, He L et al (2007) (64)Cu-labeled tetrameric and octameric RGD peptides for small-animal PET of tumor alpha(v)beta(3) integrin expression. J Nucl Med 48(7):1162–1171
Battle MR, Goggi JL, Allen L, Barnett J, Morrison MS (2011) Monitoring tumor response to antiangiogenic sunitinib therapy with 18F-fluciclatide, an 18F-labeled alphaVbeta3-integrin and alphaV beta5-integrin imaging agent. J Nucl Med 52(3):424–430
Calli C, Kitis O, Yunten N, Yurtseven T, Islekel S, Akalin T (2006) Perfusion and diffusion MR imaging in enhancing malignant cerebral tumors. Eur J Radiol 58(3):394–403
Erdogan C, Hakyemez B, Yildirim N, Parlak M (2005) Brain abscess and cystic brain tumor: discrimination with dynamic susceptibility contrast perfusion-weighted MRI. J Comput Assist Tomogr 29(5):663–667
Patankar TF, Haroon HA, Mills SJ, Baleriaux D, Buckley DL, Parker GJ et al (2005) Is volume transfer coefficient (K(trans)) related to histologic grade in human gliomas? AJNR Am J Neuroradiol 26(10):2455–2465
Thompson G, Mills SJ, Stivaros SM, Jackson A (2010) Imaging of brain tumors: perfusion/permeability. Neuroimaging Clin N Am 20(3):337–353
Yoon JH, Kim JH, Kang WJ, Sohn CH, Choi SH, Yun TJ et al (2014) Grading of cerebral glioma with multiparametric MR imaging and 18F-FDG-PET: concordance and accuracy. Eur Radiol 24(2):380–389
Law M, Young RJ, Babb JS, Peccerelli N, Chheang S, Gruber ML et al (2008) Gliomas: predicting time to progression or survival with cerebral blood volume measurements at dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. Radiology 247(2):490–498
Di Costanzo A, Scarabino T, Trojsi F, Popolizio T, Bonavita S, de Cristofaro M (2014) Recurrent glioblastoma multiforme versus radiation injury: a multiparametric 3-T MR approach. Radiol Med 119:616–624
Sugahara T, Korogi Y, Tomiguchi S, Shigematsu Y, Ikushima I, Kira T et al (2000) Posttherapeutic intraaxial brain tumor: the value of perfusion-sensitive contrast-enhanced MR imaging for differentiating tumor recurrence from nonneoplastic contrast-enhancing tissue. AJNR Am J Neuroradiol 21(5):901–909
Larsen VA, Simonsen HJ, Law I, Larsson HB, Hansen AE (2013) Evaluation of dynamic contrast-enhanced T1-weighted perfusion MRI in the differentiation of tumor recurrence from radiation necrosis. Neuroradiology 55(3):361–369
Cao Y, Tsien CI, Nagesh V, Junck L, Ten Haken R, Ross BD et al (2006) Survival prediction in high-grade gliomas by MRI perfusion before and during early stage of RT [corrected]. Int J Radiat Oncol Biol Phys 64(3):876–885
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Jackson, A., Djoukhadar, I., Coope, D.J. (2014). Imaging Biomarkers of Angiogenesis and the Microvascular Environment in Cerebral Tumors. In: Saba, L., Raz, E. (eds) Neurovascular Imaging. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-9212-2_18-1
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DOI: https://doi.org/10.1007/978-1-4614-9212-2_18-1
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