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Application of Magnetic Resonance Imaging (MRI) and Spectroscopy (MRS) in Preclinical Cancer Models

  • Gigin Lin
  • Yuen-Li Chung
Reference work entry

Abstract

Magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) can offer functional and biochemical information on cancer cells and solid tumors. These imaging modalities may provide markers for tumor diagnosis, prognosis and treatment response, as well as insights into cancer biology and factors that promote tumor growth.

Brief descriptions on the various MRI and MRS techniques used to study tumor biology, physiology, metabolism, and treatment response are included in this chapter. Examples of preclinical MRI applications in studying cellular, physiological, and biomechanical properties of tumors and in assessing treatment response in tumor models are also presented, together with a brief description of MRS applications in examining tumor metabolism and therapies.

Keywords

MRI MRS T1-weighted MRI T2-weighted MRI Proton density MRI T2* MRI R2* BOLD Diffusion-weighted MRI Dynamic contrast-enhanced MRI Arterial spin-labeling MRI Magnetic resonance elastography Magnetic nanoparticle Tumor metabolism Tumor hypoxia Tumor oxygenation Tumor vasculatures Tumor elasticity Tumor viscosity Tumor response to therapy 

Notes

Acknowledgments

GL acknowledges the support received from the Chang Gung Medical Foundation (Taiwan) with Grants CMRPG3B1923, CMRPG3C1872, and CIRPG3E0021. Y-LC is supported by funding received from the CR-UK Cancer Imaging Centre in association with the MRC and Department of Health (England) grant C1060/A10334, C1060/A16464, NHS funding to the NIHR Biomedical Research Centre. We would also like to thank Dr. Yu-Chun Lin, Chang Gung Memorial Hospital, Taiwan, and Drs. Yann Jamin, Jin Li, and Simon P Robinson, The Institute of Cancer Research, United Kingdom, for providing us with figures for this chapter.

References

  1. 1.
    Kauppinen RA, Peet AC. Using magnetic resonance imaging and spectroscopy in cancer diagnostics and monitoring: preclinical and clinical approaches. Cancer Biol Ther. 2011;12:665–79.CrossRefGoogle Scholar
  2. 2.
    Bell LK, Ainsworth NL, Lee SH, Griffiths JR. MRI & MRS assessment of the role of the tumour microenvironment in response to therapy. NMR Biomed. 2011;24:612–35.Google Scholar
  3. 3.
    Chung Y-L, Stubbs M, Griffiths JR. Applications of MRS in cancer in pre-clinical models. In: Webb GA, editor. Modern magnetic resonance. 1st ed. Netherlands: Springer. p. 823–33. Google Scholar
  4. 4.
    Chung Y-L, Griffiths JR. Metabolomic studies on cancer and on anticancer drugs by NMR ex vivo. eMagRes. 2011.Google Scholar
  5. 5.
    Weidensteiner C, Allegrini PR, Sticker-Jantscheff M, Romanet V, Ferretti S, McSheehy PM. Tumour T1 changes in vivo are highly predictive of response to chemotherapy and reflect the number of viable tumour cells – a preclinical MR study in mice. BMC Cancer. 2014;14:88.CrossRefGoogle Scholar
  6. 6.
    O’Connor JP, Jackson A, Parker GJ, Roberts C, Jayson GC. Dynamic contrast-enhanced MRI in clinical trials of antivascular therapies. Nat Rev Clin Oncol. 2012;9:167–77.CrossRefGoogle Scholar
  7. 7.
    Ogawa S, Lee TM, Kay AR, Tank DW. Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc Natl Acad Sci U S A. 1990;87:9868–72.CrossRefGoogle Scholar
  8. 8.
    Buxton RB, Frank LR. A model for the coupling between cerebral blood flow and oxygen metabolism during neural stimulation. J Cereb Blood Flow Metab. 1997;17:64–72.CrossRefGoogle Scholar
  9. 9.
    van Zijl PC, Eleff SM, Ulatowski JA, Oja JM, Ulug AM, Traystman RJ, Kauppinen RA. Quantitative assessment of blood flow, blood volume and blood oxygenation effects in functional magnetic resonance imaging. Nat Med. 1998;4:159–67.CrossRefGoogle Scholar
  10. 10.
    Silvennoinen MJ, Clingman CS, Golay X, Kauppinen RA, van Zijl PCM. Comparison of the dependence of blood R-2 and R*(2) on oxygen saturation at 1.5 and 4.7 Tesla. Magn Reson Med. 2003;49:47–60.CrossRefGoogle Scholar
  11. 11.
    Howe FA, Robinson SP, McIntyre DJ, Stubbs M, Griffiths JR. Issues in flow and oxygenation dependent contrast (FLOOD) imaging of tumours. NMR Biomed. 2001;14:497–506.CrossRefGoogle Scholar
  12. 12.
    Rijpkema M, Schuuring J, Bernsen PL, Bernsen HJ, Kaanders JH, van der Kogel AJ, et al. BOLD MRI response to hypercapnic hyperoxia in patients with meningiomas: correlation with Gadolinium-DTPA uptake rate. Magn Reson Imaging. 2004;22:761–7.CrossRefGoogle Scholar
  13. 13.
    Remmele S, Dahnke H, Flacke S, Soehle M, Wenningmann I, Kovacs A, et al. Quantification of the magnetic resonance signal response to dynamic (C)O(2)-enhanced imaging in the brain at 3 T: R*(2) BOLD vs. balanced SSFP. J Magn Reson Imaging. 2010;31:1300–10.CrossRefGoogle Scholar
  14. 14.
    Alonzi R, Padhani AR, Maxwell RJ, Taylor NJ, Stirling JJ, Wilson JI, et al. Carbogen breathing increases prostate cancer oxygenation: a translational MRI study in murine xenografts and humans. Br J Cancer. 2009;100:644–8.CrossRefGoogle Scholar
  15. 15.
    McPhail LD, Robinson SP. Intrinsic susceptibility MR imaging of chemically induced rat mammary tumors: relationship to histologic assessment of hypoxia and fibrosis. Radiology. 2010;254:110–8.CrossRefGoogle Scholar
  16. 16.
    Baker LC, Boult JK, Jamin Y, Gilmour LD, Walker-Samuel S, Burrell JS, et al. Evaluation and immunohistochemical qualification of carbogen-induced DeltaR(2) as a noninvasive imaging biomarker of improved tumor oxygenation. Int J Radiat Oncol Biol Phys. 2013;87:160–7.CrossRefGoogle Scholar
  17. 17.
    Valable S, Lemasson B, Farion R, Beaumont M, Segebarth C, Remy C, et al. Assessment of blood volume, vessel size, and the expression of angiogenic factors in two rat glioma models: a longitudinal in vivo and ex vivo study. NMR Biomed. 2008;21:1043–56.CrossRefGoogle Scholar
  18. 18.
    Tropres I, Grimault S, Vaeth A, Grillon E, Julien C, Payen JF, et al. Vessel size imaging. Magn Reson Med. 2001;45:397–408.CrossRefGoogle Scholar
  19. 19.
    Cercignani M, Horsfield MA. The physical basis of diffusion-weighted MRI. J Neurol Sci. 2001;186(Suppl 1):S11–4.CrossRefGoogle Scholar
  20. 20.
    Padhani AR. Dynamic contrast-enhanced MRI in clinical oncology: current status and future directions. J Magn Reson Imaging. 2002;16:407–22.CrossRefGoogle Scholar
  21. 21.
    Leach MO, Brindle KM, Evelhoch JL, Griffiths JR, Horsman MR, Jackson A, et al. The assessment of antiangiogenic and antivascular therapies in early-stage clinical trials using magnetic resonance imaging: issues and recommendations. Br J Cancer. 2005;92:1599–610.CrossRefGoogle Scholar
  22. 22.
    Lin YC, Wang JJ, Hong JH, Lin YP, Lee CC, Wai YY, et al. Noninvasive monitoring of microvascular changes with partial irradiation using dynamic contrast-enhanced and blood oxygen level-dependent magnetic resonance imaging. Int J Radiat Oncol Biol Phys. 2013;85:1367–74.CrossRefGoogle Scholar
  23. 23.
    Chai WY, Chu PC, Tsai MY, Lin YC, Wang JJ, Wei KC, et al. Magnetic-resonance imaging for kinetic analysis of permeability changes during focused ultrasound-induced blood-brain barrier opening and brain drug delivery. J Control Release. 2014;192:1–9.CrossRefGoogle Scholar
  24. 24.
    Golay X, Hendrikse J, Lim TC. Perfusion imaging using arterial spin labeling. Top Magn Reson Imaging. 2004;15:10–27.CrossRefGoogle Scholar
  25. 25.
    Mariappan YK, Glaser KJ, Ehman RL. Magnetic resonance elastography: a review. Clin Anat. 2010;23:497–511.CrossRefGoogle Scholar
  26. 26.
    Veiseh O, Gunn JW, Zhang M. Design and fabrication of magnetic nanoparticles for targeted drug delivery and imaging. Adv Drug Deliv Rev. 2010;62:284–304.CrossRefGoogle Scholar
  27. 27.
    Kohler N, Sun C, Fichtenholtz A, Gunn J, Fang C, Zhang M. Methotrexate-immobilized poly(ethylene glycol) magnetic nanoparticles for MR imaging and drug delivery. Small. 2006;2:785–92.CrossRefGoogle Scholar
  28. 28.
    Jain TK, Morales MA, Sahoo SK, Leslie-Pelecky DL, Labhasetwar V. Iron oxide nanoparticles for sustained delivery of anticancer agents. Mol Pharm. 2005;2:194–205.CrossRefGoogle Scholar
  29. 29.
    Xiong XB, Ma Z, Lai R, Lavasanifar A. The therapeutic response to multifunctional polymeric nano-conjugates in the targeted cellular and subcellular delivery of doxorubicin. Biomaterials. 2010;31:757–68.CrossRefGoogle Scholar
  30. 30.
    Patterson DM, Padhani AR, Collins DJ. Technology Insight: water diffusion MRI – a potential new biomarker of response to cancer therapy. Nat Clin Pract Oncol. 2008;5:220–33.CrossRefGoogle Scholar
  31. 31.
    Sinkus R, Van Beers BE, Vilgrain V, DeSouza N, Waterton JC. Apparent diffusion coefficient from magnetic resonance imaging as a biomarker in oncology drug development. Eur J Cancer. 2012;48:425–31.CrossRefGoogle Scholar
  32. 32.
    Gupta RK, Sinha U, Cloughesy TF, Alger JR. Inverse correlation between choline magnetic resonance spectroscopy signal intensity and the apparent diffusion coefficient in human glioma. Magn Reson Med. 1999;41:2–7.CrossRefGoogle Scholar
  33. 33.
    Gerstner ER, Sorensen AG. Diffusion and diffusion tensor imaging in brain cancer. Semin Radiat Oncol. 2011;21:141–6.CrossRefGoogle Scholar
  34. 34.
    Valonen PK, Lehtimaki KK, Vaisanen TH, Kettunen MI, Grohn OH, Yla-Herttuala S, et al. Water diffusion in a rat glioma during ganciclovir-thymidine kinase gene therapy-induced programmed cell death in vivo: correlation with cell density. J Magn Reson Imaging. 2004;19:389–96.CrossRefGoogle Scholar
  35. 35.
    Sugahara T, Korogi Y, Kochi M, Ikushima I, Shigematu Y, Hirai T, et al. Usefulness of diffusion-weighted MRI with echo-planar technique in the evaluation of cellularity in gliomas. J Magn Reson Imaging. 1999;9:53–60.CrossRefGoogle Scholar
  36. 36.
    Heijmen L, Ter Voert EE, Nagtegaal ID, Span P, Bussink J, Punt CJ, et al. Diffusion-weighted MR Imaging in liver metastases of colorectal cancer: reproducibility and biological validation. Eur Radiol. 2013;23:748–56.CrossRefGoogle Scholar
  37. 37.
    Muraoka N, Uematsu H, Kimura H, Imamura Y, Fujiwara Y, Murakami M, et al. Apparent diffusion coefficient in pancreatic cancer: characterization and histopathological correlations. J Magn Reson Imaging. 2008;27:1302–8.CrossRefGoogle Scholar
  38. 38.
    Lin YC, Wang CC, Lin G, Lin YP, Wai YY, Ng SH, et al. A simple method to improve the quality of diffusion-weighted magnetic resonance imaging with rapid histologic correlation in a murine model. Mol Imaging. 2014;13:1–8.Google Scholar
  39. 39.
    Mori S, Crain BJ, Chacko VP, van Zijl PC. Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann Neurol. 1999;45:265–9.CrossRefGoogle Scholar
  40. 40.
    Kim S, Pickup S, Hsu O, Poptani H. Diffusion tensor MRI in rat models of invasive and well-demarcated brain tumors. NMR Biomed. 2008;21:208–16.CrossRefGoogle Scholar
  41. 41.
    Mori S, Frederiksen K, van Zijl PC, Stieltjes B, Kraut MA, Solaiyappan M, et al. Brain white matter anatomy of tumor patients evaluated with diffusion tensor imaging. Ann Neurol. 2002;51:377–80.CrossRefGoogle Scholar
  42. 42.
    Jamin Y, Boult JK, Li J, Popov S, Garteiser P, Ulloa JL, et al. Exploring the biomechanical properties of brain malignancies and their pathologic determinants in vivo with magnetic resonance elastography. Cancer Res. 2015;75:1216–24.CrossRefGoogle Scholar
  43. 43.
    Burrell JS, Walker-Samuel S, Baker LC, Boult JK, Jamin Y, Halliday J, et al. Exploring DeltaR(2) * and DeltaR(1) as imaging biomarkers of tumor oxygenation. J Magn Reson Imaging. 2013;38:429–34.CrossRefGoogle Scholar
  44. 44.
    O’Connor JP, Boult JK, Jamin Y, Babur M, Finegan KG, Williams KJ, et al. Oxygen-enhanced MRI accurately identifies, quantifies, and maps tumor hypoxia in preclinical cancer models. Cancer Res. 2016;76:787–95.CrossRefGoogle Scholar
  45. 45.
    Jain RK, Di Tomaso E, Duda DG, Loeffler JS, Sorensen AG, Batchelor TT. Angiogenesis in brain tumours. Nat Rev Neurosci. 2007;8:610–22.CrossRefGoogle Scholar
  46. 46.
    Miller JC, Pien HH, Sahani D, Sorensen AG, Thrall JH. Imaging angiogenesis: applications and potential for drug development. J Natl Cancer Inst. 2005;97:172–87.CrossRefGoogle Scholar
  47. 47.
    Zee YK, O’Connor JP, Parker GJ, Jackson A, Clamp AR, Taylor MB, et al. Imaging angiogenesis of genitourinary tumors. Nat Rev Urol. 2010;7:69–82.CrossRefGoogle Scholar
  48. 48.
    Parker GJM, Padhani AR. T1-w DCE-MRI: T1-weighted dynamic contrast-enhanced MRI. In: Tofts PS, editor. Quantitative MRI of the brain. Chippenham: John Wiley & Sons Ltd; 2004. p. 341–64. Google Scholar
  49. 49.
    Mueller-Lisse UG, Mueller-Lisse UL. Imaging of advanced renal cell carcinoma. World J Urol. 2010;28:253–61.CrossRefGoogle Scholar
  50. 50.
    Moffat BA, Chenevert TL, Hall DE, Rehemtulla A, Ross BD. Continuous arterial spin labeling using a train of adiabatic inversion pulses. J Magn Reson Imaging. 2005;21:290–6.CrossRefGoogle Scholar
  51. 51.
    Jamin Y, Glass L, Hallsworth A, George R, Koh DM, Pearson AD, et al. Intrinsic susceptibility MRI identifies tumors with ALKF1174L mutation in genetically-engineered murine models of high-risk neuroblastoma. PLoS One. 2014;9:e92886.CrossRefGoogle Scholar
  52. 52.
    Kalber TL, Waterton JC, Griffiths JR, Ryan AJ, Robinson SP. Longitudinal in vivo susceptibility contrast MRI measurements of LS174T colorectal liver metastasis in nude mice. J Magn Reson Imaging. 2008;26:1451–8.CrossRefGoogle Scholar
  53. 53.
    Ungersma SE, Pacheco G, Ho C, Yee SF, Ross J, van Bruggen N, et al. Vessel imaging with viable tumor analysis for quantification of tumor angiogenesis. Magn Reson Med. 2010;63:1637–47.CrossRefGoogle Scholar
  54. 54.
    Burrell JS, Bradley RS, Walker-Samuel S, Jamin Y, Baker LCJ, Boult JKR, et al. MRI measurements of vessel calibre in tumour xenografts: comparison with vascular corrosion casting. Microvasc Res. 2012;84:323–9.CrossRefGoogle Scholar
  55. 55.
    Douma K, Oostendorp M, Slaaf DW, Post MJ, Backes WH, van Zandvoort MA. Evaluation of magnetic resonance vessel size imaging by two-photon laser scanning microscopy. Magn Reson Med. 2010;63:930–9.CrossRefGoogle Scholar
  56. 56.
    Persigehl T, Ring J, Budny T, Hahnenkamp A, Stoeppeler S, Schwartz LH, et al. Vessel size imaging (VSI) by robust magnetic resonance (MR) relaxometry: MR-VSI of solid tumors in correlation with immunohistology and intravital microscopy. Mol Imaging. 2013;12:1–11.CrossRefGoogle Scholar
  57. 57.
    Kim E, Cebulla J, Ward BD, Rhie K, Zhang J, Pathak AP. Assessing breast cancer angiogenesis in vivo: which susceptibility contrast MRI biomarkers are relevant? Magn Reson Med. 2013;70:1106–16.CrossRefGoogle Scholar
  58. 58.
    Rodrigues LM, Howe FA, Griffiths JR, Robinson SP. Tumor R2* is a prognostic indicator of acute radiotherapeutic response in rodent tumors. J Magn Reson Imaging. 2004;19:482–8.CrossRefGoogle Scholar
  59. 59.
    Moffat BA, Hall DE, Stojanovska J, McConville PJ, Moody JB, Chenevert TL, et al. Diffusion imaging for evaluation of tumor therapies in preclinical animal models. MAGMA. 2004;17:249–59.CrossRefGoogle Scholar
  60. 60.
    Gross S, Gilead A, Scherz A, Neeman M, Salomon Y. Monitoring photodynamic therapy of solid tumors online by BOLD-contrast MRI. Nat Med. 2003;9:1327–31.CrossRefGoogle Scholar
  61. 61.
    Zhao D, Jiang L, Hahn EW, Mason RP. Continuous low-dose (metronomic) chemotherapy on rat prostate tumors evaluated using MRI in vivo and comparison with histology. Neoplasia. 2005;7:678–87.CrossRefGoogle Scholar
  62. 62.
    Padhani A. Science to practice: what does MR oxygenation imaging tell us about human breast cancer hypoxia? Radiology. 2010;254:1–3.CrossRefGoogle Scholar
  63. 63.
    Bradley DP, Tessier JJ, Ashton SE, Waterton JC, Wilson Z, Worthington PL, et al. Correlation of MRI biomarkers with tumor necrosis in Hras5 tumor xenograft in athymic rats. Neoplasia. 2007;9:382–91.CrossRefGoogle Scholar
  64. 64.
    Lemasson B, Christen T, Tizon X, Farion R, Fondraz N, Provent P, et al. Assessment of multiparametric MRI in a human glioma model to monitor cytotoxic and anti-angiogenic drug effects. NMR Biomed. 2011;24:473–82.CrossRefGoogle Scholar
  65. 65.
    Walker-Samuel S, Boult JK, McPhail LD, Box G, Eccles SA, Robinson SP. Non-invasive in vivo imaging of vessel calibre in orthotopic prostate tumour xenografts. Int J Cancer. 2012;130:1284–93.CrossRefGoogle Scholar
  66. 66.
    Farrar CT, Kamoun WS, Ley CD, Kim YR, Catana C, Kwon SJ, et al. Sensitivity of MRI tumor biomarkers to VEGFR inhibitor therapy in an orthotopic mouse glioma model. PLoS One. 2011;6:e17228.CrossRefGoogle Scholar
  67. 67.
    Sampath D, Oeh J, Wyatt SK, Cao TC, Koeppen H, Eastham-Anderson J, et al. Multimodal microvascular imaging reveals that selective inhibition of class I PI3K is sufficient to induce an antivascular response. Neoplasia. 2013;15:694–711.CrossRefGoogle Scholar
  68. 68.
    Vogel-Claussen J, Gimi B, Artemov D, Bhujwalla ZM. Diffusion-weighted and macromolecular contrast enhanced MRI of tumor response to antivascular therapy with ZD6126. Cancer Biol Ther. 2007;6:1469–75.CrossRefGoogle Scholar
  69. 69.
    Jamin Y, Tucker ER, Poon E, Popov S, Vaughan L, Boult JK, et al. Evaluation of clinically translatable MR imaging biomarkers of therapeutic response in the TH-MYCN transgenic mouse model of neuroblastoma. Radiology. 2013;266:130–40.CrossRefGoogle Scholar
  70. 70.
    McSheehy PMJ, Weidensteiner C, Cannet C, Ferretti S, Laurent D, Ruetz S, et al. Quantified tumor T1 is a generic early-response imaging biomarker for chemotherapy reflecting cell viability. Clin Cancer Res. 2010;16:212–25.CrossRefGoogle Scholar
  71. 71.
    Graham TJ, Box G, Tunariu N, Crespo M, Spinks TJ, Miranda S, et al. Preclinical evaluation of imaging biomarkers for prostate cancer bone metastasis and response to cabozantinib. J Natl Cancer Inst. 2014;106:dju033.CrossRefGoogle Scholar
  72. 72.
    Ardenkjaer-Larsen JH, Fridlund B, Gram A, Hansson G, Hansson L, Lerche MH, et al. Increase in signal-to-noise ratio of >10,000 times in liquid-state NMR. Proc Natl Acad Sci U S A. 2003;100:10158–63.CrossRefGoogle Scholar
  73. 73.
    Ward CS, Venkatesh HS, Chaumeil MM, Brandes AH, Vancriekinge M, Dafni H, et al. Noninvasive detection of target modulation following phosphatidylinositol 3-kinase inhibition using hyperpolarized 13C magnetic resonance spectroscopy. Cancer Res. 2010;70:1296–305.CrossRefGoogle Scholar
  74. 74.
    Lin G, Hill DK, Andrejeva G, Boult JK, Troy H, Fong AC, et al. Dichloroacetate induces autophagy in colorectal cancer cells and tumours. Br J Cancer. 2014;111:375–85.CrossRefGoogle Scholar
  75. 75.
    Day SE, Kettunen MI, Gallagher FA, Hu DE, Lerche M, Wolber J, et al. Detecting tumor response to treatment using hyperpolarized 13C magnetic resonance imaging and spectroscopy. Nat Med. 2007;13:1382–7.CrossRefGoogle Scholar
  76. 76.
    Kurhanewicz J, Vigneron DB, Brindle K, Chekmenev EY, Comment A, Cunningham CH, et al. Analysis of cancer metabolism by imaging hyperpolarized nuclei: prospects for translation to clinical research. Neoplasia. 2011;13:81–97.CrossRefGoogle Scholar
  77. 77.
    Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144:646–74.CrossRefGoogle Scholar
  78. 78.
    Schulze A, Harris AL. How cancer metabolism is tuned for proliferation and vulnerable to disruption. Nature. 2012;491:364–73.CrossRefGoogle Scholar
  79. 79.
    Bulik M, Jancalek R, Vanicek J, Skoch A, Mechl M. Potential of MR spectroscopy for assessment of glioma grading. Clin Neurol Neurosurg. 2013;115:146–53.CrossRefGoogle Scholar
  80. 80.
    Kundu S, Chopra S, Verma A, Mahantshetty U, Engineer R, Shrivastava SK. Functional magnetic resonance imaging in cervical cancer: current evidence and future directions. J Cancer Res Ther. 2012;8:11–8.CrossRefGoogle Scholar
  81. 81.
    Chen W, Zu Y, Huang Q, Chen F, Wang G, Lan W, et al. Study on metabonomic characteristics of human lung cancer using high resolution magic-angle spinning 1H NMR spectroscopy and multivariate data analysis. Magn Reson Med. 2011;66:1531–40.CrossRefGoogle Scholar
  82. 82.
    Bolan PJ. Magnetic resonance spectroscopy of the breast: current status. Magn Reson Imaging Clin N Am. 2013;21:625–39.CrossRefGoogle Scholar
  83. 83.
    Penet MF, Gadiya MM, Krishnamachary B, Nimmagadda S, Pomper MG, Artemov D, et al. Metabolic signatures imaged in cancer-induced cachexia. Cancer Res. 2011;71:6948–56.CrossRefGoogle Scholar
  84. 84.
    Palmnas MS, Vogel HJ. The future of NMR metabolomics in cancer therapy: towards personalizing treatment and developing targeted drugs? Metabolites. 2013;3:373–96.CrossRefGoogle Scholar
  85. 85.
    Chung Y-L, Madhu B, Griffiths JR. Metabolism and metabolomics by MRS. eMagRes. 2015;4:689–98.CrossRefGoogle Scholar
  86. 86.
    Glunde K, Bhujwalla ZM, Ronen SM. Choline metabolism in malignant transformation. Nat Rev Cancer. 2011;11:835–48.CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Department of Medical Imaging and Intervention, Clinical Phenome Centre and Imaging Core Lab, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou, College of MedicineChang Gung UniversityTaoyuanTaiwan
  2. 2.Cancer Research UK Cancer Imaging Centre, Division of Radiotherapy and ImagingThe Institute of Cancer ResearchLondonUK

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