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Technology Insight: water diffusion MRI—a potential new biomarker of response to cancer therapy

Nature Clinical Practice Oncology volume 5, pages 220–233 (2008)Cite this article

Abstract

Diffusion-weighted MRI (DW-MRI) is a functional imaging technique that displays information about the extent and direction of random water motion in tissues. Water movement in tissues is modified by interactions with hydrophobic cellular membranes, intracellular organelles and macromolecules. DW-MRI provides information on extracellular-space tortuosity, tissue cellularity and the integrity of cellular membranes. Images can be sensitive to large or small displacements of water, therefore, macroscopic water flows and microscopic water displacements in the extracellular space can be depicted. Preclinical and clinical data indicate a number of potential roles of DW-MRI in the characterization of malignancy, including determination of lesion aggressiveness and monitoring response to therapy. This Review outlines the biological basis of observations made on DW-MRI and describes how measurements are acquired and quantified, and discusses the interpretation of images and limitations of the technique. The strength of evidence for adoption of DW-MRI as a biomarker for the assessment of tumor response is presented.

Key Points

  • Diffusion-weighted MRI (DW-MRI) provides quantitative information on extracellular space tortuosity, tissue cellularity and the integrity of cellular membranes of tumor tissues by use of rapid imaging protocols that can be routinely adopted into clinical practice

  • Recent advances enable the technique to be widely applied to tumor evaluation in the abdomen and pelvis, and have led to the development of whole-body diffusion-weighted imaging

  • There are convincing data to support use of DW-MRI in the characterization of malignancy, including determination of lesion aggressiveness and for monitoring response to a variety of treatments

  • Changes observed in response to therapy are dependent on the biological mechanism of action of the treatment and on the timing of the MRI observations in relation to administration of treatment

  • Major challenges to the widespread adoption of DW-MRI include the need for improved artifact control, standardization of data acquisition, qualitative and quantitative analysis methods and data display techniques

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Figure 1: Schematic illustration of the diffusion-weighted imaging sequence and the cause of signal loss by mobile protons.
Figure 2: Typical series of diffusion-weighted images of endometrial cancer in the pelvis.
Figure 3: Typical series of diffusion-weighted images of liver metastases from colon cancer.
Figure 4: Typical series of diffusion-weighted images of an endometrial polyp and a uterine fibroid.
Figure 5: Biological processes proposed to be involved in therapy induced changes in tumor ADC.

References

  1. Tanner JE (1979) Self diffusion of water in frog muscle. Biophys J 28: 107–116

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Parker GJ (2004) Analysis of MR diffusion weighted images. Br J Radiol 77 (Suppl): S176–S185

    PubMed  Google Scholar 

  3. Ross BD et al. (2003) Evaluation of cancer therapy using diffusion magnetic resonance imaging. Mol Cancer Ther 2: 581–587

    CAS  PubMed  Google Scholar 

  4. Szafer A et al. (1995) Theoretical model for water diffusion in tissues. Magn Reson Med 33: 697–712

    CAS  PubMed  Google Scholar 

  5. Sykova E et al. (1994) Extracellular volume fraction and diffusion characteristics during progressive ischemia and terminal anoxia in the spinal cord of the rat. J Cereb Blood Flow Metab 14: 301–311

    CAS  PubMed  Google Scholar 

  6. Norris DG et al. (1994) Health and infarcted brain tissues studied at short diffusion times: the origins of apparent restriction and the reduction in apparent diffusion coefficient. NMR Biomed 7: 304–310

    CAS  PubMed  Google Scholar 

  7. Lyng H et al. (2000) Measurement of cell density and necrotic fraction in human melanoma xenografts by diffusion weighted magnetic resonance imaging. Magn Reson Med 43: 828–836

    CAS  PubMed  Google Scholar 

  8. Cheng KH and Hernandez M (1992) Magnetic resonance diffusion imaging detects structural damage in biological tissues upon hyperthermia. Cancer Res 52: 6066–6073

    CAS  PubMed  Google Scholar 

  9. Dzik-Jurasz AS (2003) Molecular imaging in vivo: an introduction. Br J Radiol 76 (Suppl): S98–S109

    CAS  PubMed  Google Scholar 

  10. Guo AC et al. (2002) Lymphomas and high-grade astrocytomas: comparison of water diffusibility and histologic characteristics. Radiology 224: 177–183

    PubMed  Google Scholar 

  11. Stejskal EO and Tanner J (1965) Spin diffusion measurements: spin echoes in the presence of a time-dependent field gradient. J Chem Phys 42: 288–292

    CAS  Google Scholar 

  12. Le Bihan D et al. (1988) Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology 168: 497–505

    CAS  PubMed  Google Scholar 

  13. Turner R et al. (1990) Echo-planar imaging of intravoxel incoherent motion. Radiology 177: 407–414

    CAS  PubMed  Google Scholar 

  14. Turner R et al. (1991) Echo-planar imaging of diffusion and perfusion. Magn Reson Med 19: 247–253

    CAS  PubMed  Google Scholar 

  15. Morvan D (1995) In vivo measurement of diffusion and pseudo-diffusion in skeletal muscle at rest and after exercise. Magn Reson Imaging 13: 193–199

    CAS  PubMed  Google Scholar 

  16. Thoeny HC et al. (2004) Diffusion-weighted imaging of the parotid gland: influence of the choice of b-values on the apparent diffusion coefficient value. J Magn Reson Imaging 20: 786–790

    PubMed  Google Scholar 

  17. Niendorf T et al. (1996) Biexponential diffusion attenuation in various states of brain tissue: implications for diffusion-weighted imaging. Magn Reson Med 36: 847–857

    CAS  PubMed  Google Scholar 

  18. Mulkern RV et al. (2005) Magnetization transfer studies of the fast and slow tissue water diffusion components in the human brain. NMR Biomed 18: 186–194

    PubMed  Google Scholar 

  19. Thoeny HC et al. (2005) Effect of vascular targeting agent in rat tumor model: dynamic contrast-enhanced versus diffusion-weighted MR imaging. Radiology 237: 492–499

    PubMed  Google Scholar 

  20. Schaefer PW et al. (2000) Diffusion-weighted MR imaging of the brain. Radiology 217: 331–345

    CAS  PubMed  Google Scholar 

  21. Ries M et al. (2001) Diffusion tensor MRI of the human kidney. J Magn Reson Imaging 14: 42–49

    CAS  PubMed  Google Scholar 

  22. Bammer R (2003) Basic principles of diffusion-weighted imaging. Eur J Radiol 45: 169–184

    PubMed  Google Scholar 

  23. Yamada I et al. (1999) Diffusion coefficients in abdominal organs and hepatic lesions: evaluation with intravoxel incoherent motion echo-planar MR imaging. Radiology 210: 617–623

    CAS  PubMed  Google Scholar 

  24. Taouli B et al. (2003) Evaluation of liver diffusion isotropy and characterization of focal hepatic lesions with two single-shot echo-planar MR imaging sequences: prospective study in 66 patients. Radiology 226: 71–78

    PubMed  Google Scholar 

  25. Padhani AR (2003) MRI for assessing antivascular cancer treatments. Br J Radiol 76 (Suppl): S60–S80

    PubMed  Google Scholar 

  26. Leach MO 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: 1599–1610

    CAS  PubMed  PubMed Central  Google Scholar 

  27. O'Connor JP et al. (2007) DCE-MRI biomarkers in the clinical evaluation of antiangiogenic and vascular disrupting agents. Br J Cancer 96: 189–195

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Thoeny HC et al. (2005) Diffusion-weighted MR imaging in monitoring the effect of a vascular targeting agent on rhabdomyosarcoma in rats. Radiology 234: 756–764

    PubMed  Google Scholar 

  29. Chaplin DJ and Hill SA (2002) The development of combretastatin A4 phosphate as a vascular targeting agent. Int J Radiat Oncol Biol Phys 54: 1491–1496

    CAS  PubMed  Google Scholar 

  30. Beauregard DA et al. (1998) Magnetic resonance imaging and spectroscopy of combretastatin A4 prodrug-induced disruption of tumour perfusion and energetic status. Br J Cancer 77: 1761–1767

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Jordan BF et al. (2005) Dynamic contrast-enhanced and diffusion MRI show rapid and dramatic changes in tumor microenvironment in response to inhibition of HIF-1alpha using PX-478. Neoplasia 7: 475–485

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Fan G et al. (2005) Usefulness of diffusion/perfusion-weighted MRI in rat gliomas: correlation with histopathology. Acad Radiol 12: 640–651

    PubMed  Google Scholar 

  33. Herneth AM et al. (2003) Apparent diffusion coefficient: a quantitative parameter for in vivo tumor characterization. Eur J Radiol 45: 208–213

    PubMed  Google Scholar 

  34. Sugahara T et al. (1999) Usefulness of diffusion-weighted MRI with echo-planar technique in the evaluation of cellularity in gliomas. J Magn Reson Imaging 9: 53–60

    CAS  PubMed  Google Scholar 

  35. Yabuuchi H et al. (2006) Phyllodes tumor of the breast: correlation between MR findings and histologic grade. Radiology 241: 702–709

    PubMed  Google Scholar 

  36. Higano S et al. (2006) Malignant astrocytic tumors: clinical importance of apparent diffusion coefficient in prediction of grade and prognosis. Radiology 241: 839–846

    PubMed  Google Scholar 

  37. Ichikawa T et al. (1998) Diffusion-weighted MR imaging with a single-shot echoplanar sequence: detection and characterization of focal hepatic lesions. AJR Am J Roentgenol 170: 397–402

    CAS  PubMed  Google Scholar 

  38. Okada Y et al. (1998) Breath-hold T2-weighted MRI of hepatic tumors: value of echo planar imaging with diffusion-sensitizing gradient. J Comput Assist Tomogr 22: 364–371

    CAS  PubMed  Google Scholar 

  39. Sato C et al. (2005) Differentiation of noncancerous tissue and cancer lesions by apparent diffusion coefficient values in transition and peripheral zones of the prostate. J Magn Reson Imaging 21: 258–262

    PubMed  Google Scholar 

  40. Ichikawa T et al. (2007) High-b value diffusion-weighted MRI for detecting pancreatic adenocarcinoma: preliminary results. AJR Am J Roentgenol 188: 409–414

    PubMed  Google Scholar 

  41. Nakayama T et al. (2004) Usefulness of the calculated apparent diffusion coefficient value in the differential diagnosis of retroperitoneal masses. J Magn Reson Imaging 20: 735–742

    PubMed  Google Scholar 

  42. Sarty GE et al. (2004) Magnetic resonance diffusion imaging of ovarian masses: a first experience with 12 cases. MAGMA 16: 182–193

    PubMed  Google Scholar 

  43. Kuroki Y et al. (2004) Diffusion-weighted imaging of breast cancer with the sensitivity encoding technique: analysis of the apparent diffusion coefficient value. Magn Reson Med Sci 3: 79–85

    PubMed  Google Scholar 

  44. Habermann CR et al. (2005) Diffusion-weighted echo-planar MRI: a valuable tool for differentiating primary parotid gland tumors? [German] Rofo 177: 940–945

    CAS  PubMed  Google Scholar 

  45. White ML et al. (2006) Evaluating tumors and tumorlike lesions of the nasal cavity, the paranasal sinuses, and the adjacent skull base with diffusion-weighted MRI. J Comput Assist Tomogr 30: 490–495

    PubMed  Google Scholar 

  46. Sumi M et al. (2006) MR microimaging of benign and malignant nodes in the neck. AJR Am J Roentgenol 186: 749–757

    PubMed  Google Scholar 

  47. deSouza NM et al. (2007) Magnetic resonance imaging in prostate cancer: value of apparent diffusion coefficients for identifying malignant nodules. Br J Radiol 80: 90–95

    CAS  PubMed  Google Scholar 

  48. Maeda M et al. (2005) Usefulness of the apparent diffusion coefficient in line scan diffusion-weighted imaging for distinguishing between squamous cell carcinomas and malignant lymphomas of the head and neck. AJNR Am J Neuroradiol 26: 1186–1192

    PubMed  Google Scholar 

  49. Kozlowski P et al. (2006) Combined diffusion-weighted and dynamic contrast-enhanced MRI for prostate cancer diagnosis—correlation with biopsy and histopathology. J Magn Reson Imaging 24: 108–113

    PubMed  Google Scholar 

  50. Reinsberg SA et al. (2007) Combined use of diffusion-weighted MRI and 1H MR spectroscopy to increase accuracy in prostate cancer detection. AJR Am J Roentgenol 188: 91–98

    PubMed  Google Scholar 

  51. Takahara T et al. (2004) Diffusion weighted whole body imaging with background body signal suppression (DWIBS): technical improvement using free breathing, STIR and high resolution 3D display. Radiat Med 22: 275–282

    PubMed  Google Scholar 

  52. Ballon D et al. (2004) Imaging therapeutic response in human bone marrow using rapid whole-body MRI. Magn Reson Med 52: 1234–1238

    PubMed  PubMed Central  Google Scholar 

  53. Brizel D et al. (1996) Tumor oxygenation predicts for the likelihood of distant metastases in human soft tissue sarcoma. Cancer Res 56: 941–943

    CAS  PubMed  Google Scholar 

  54. Swinson DE et al. (2002) Tumour necrosis is an independent prognostic marker in non-small cell lung cancer: correlation with biological variables. Lung Cancer 37: 235–240

    PubMed  Google Scholar 

  55. Gray LH et al. (1953) The concentration of oxygen dissolved in tissues at the time of irradiation as a factor in radiotherapy. Br J Radiol 26: 638–648

    CAS  PubMed  Google Scholar 

  56. Leek RD et al. (1999) Necrosis correlates with high vascular density and focal macrophage infiltration in invasive carcinoma of the breast. Br J Cancer 79: 991–995

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Lemaire L et al. (1999) Assessment of induced rat mammary tumour response to chemotherapy using the apparent diffusion coefficient of tissue water as determined by diffusion-weighted 1H-NMR spectroscopy in vivo. MAGMA 8: 20–26

    CAS  PubMed  Google Scholar 

  58. Roth Y et al. (2004) High-b-value diffusion-weighted MR imaging for pretreatment prediction and early monitoring of tumor response to therapy in mice. Radiology 232: 685–692

    PubMed  Google Scholar 

  59. Dzik-Jurasz A et al. (2002) Diffusion MRI for prediction of response of rectal cancer to chemoradiation. Lancet 360: 307–308

    PubMed  Google Scholar 

  60. DeVries AF et al. (2003) Tumor microcirculation and diffusion predict therapy outcome for primary rectal carcinoma. Int J Radiat Oncol Biol Phys 56: 958–965

    PubMed  Google Scholar 

  61. Chen KC and Nicholson C (2000) Changes in brain cell shape create residual extracellular space volume and explain tortuosity behavior during osmotic challenge. Proc Natl Acad Sci USA 97: 8306–8311

    CAS  PubMed  Google Scholar 

  62. Schlaug G et al. (1997) Time course of the apparent diffusion coefficient (ADC) abnormality in human stroke. Stroke 49: 113–119

    CAS  Google Scholar 

  63. Hortelano S et al. (2001) Intracellular water motion decreases in apoptotic macrophages after caspase activation. Cell Death Differ 8: 1022–1028

    CAS  PubMed  Google Scholar 

  64. Bortner CD and Cidlowski JA (2003) Uncoupling cell shrinkage from apoptosis reveals that Na+ influx is required for volume loss during programmed cell death. J Biol Chem 278: 39176–39184

    CAS  PubMed  Google Scholar 

  65. Desjardins LM and MacManus JP (1995) An adherent cell model to study different stages of apoptosis. Exp Cell Res 216: 380–387

    CAS  PubMed  Google Scholar 

  66. Akagi Y et al. (1993) Radiation-induced apoptosis and necrosis in Molt-4 cells: a study of dose-effect relationships and their modification. Int J Radiat Biol 64: 47–56

    CAS  PubMed  Google Scholar 

  67. Plaks V et al. (2004) Photodynamic therapy of established prostatic adenocarcinoma with TOOKAD: a biphasic apparent diffusion coefficient change as potential early MRI response marker. Neoplasia 6: 224–233

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Mardor Y et al. (2001) Monitoring response to convection-enhanced taxol delivery in brain tumor patients using diffusion-weighted magnetic resonance imaging. Cancer Res 61: 4971–4973

    CAS  PubMed  Google Scholar 

  69. Hein PA et al. (2003) Diffusion-weighted magnetic resonance imaging for monitoring diffusion changes in rectal carcinoma during combined, preoperative chemoradiation: preliminary results of a prospective study. Eur J Radio 45: 214–222

    Google Scholar 

  70. Zhao M et al. (1996) Early detection of treatment response by diffusion-weighted 1H-NMR spectroscopy in a murine tumour in vivo. Br J Cancer 73: 61–64

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Zhao M and Evelhoch JL (1996) Detection of response to 5-fluorouracil by diffusion-weighted 1H-NMR spectroscopy in murine tumours in vivo. Proc Int Soc Magn Reson Med Sci Meet Exhib 2: 118

    Google Scholar 

  72. Chenevert TL et al. (1997) Monitoring early response of experimental brain tumors to therapy using diffusion magnetic resonance imaging. Clin Cancer Res 3: 1457–1466

    CAS  PubMed  Google Scholar 

  73. Chenevert TL et al. (2000) Diffusion magnetic resonance imaging: an early surrogate marker of therapeutic efficacy in brain tumors. J Natl Cancer Inst 92: 2029–2036

    CAS  PubMed  Google Scholar 

  74. Hall DE et al. (2004) Therapeutic efficacy of DTI-015 using diffusion magnetic resonance imaging as an early surrogate marker. Clin Cancer Res 10: 7852–7859

    CAS  PubMed  Google Scholar 

  75. Galons JP et al. (1999) Early increases in breast tumor xenograft water mobility in response to paclitaxel therapy detected by non-invasive diffusion magnetic resonance imaging. Neoplasia 1: 113–117

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Jennings D et al. (2002) Early response of prostate carcinoma xenografts to docetaxel chemotherapy monitored with diffusion MRI. Neoplasia 4: 255–262

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Thoeny HC et al. (2005) Diffusion-weighted magnetic resonance imaging allows noninvasive in vivo monitoring of the effects of combretastatin a-4 phosphate after repeated administration. Neoplasia 7: 779–787

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Moffat BA et al. (2005) Functional diffusion map: a noninvasive MRI biomarker for early stratification of clinical brain tumor response. Proc Natl Acad Sci USA 102: 5524–5529

    CAS  PubMed  Google Scholar 

  79. Hamstra DA et al. (2005) Evaluation of the functional diffusion map as an early biomarker of time-to-progression and overall survival in high-grade glioma. Proc Natl Acad Sci USA 102: 16759–16764

    CAS  PubMed  Google Scholar 

  80. Theilmann RJ et al. (2004) Changes in water mobility measured by diffusion MRI predict response of metastatic breast cancer to chemotherapy. Neoplasia 6: 831–837

    PubMed  PubMed Central  Google Scholar 

  81. Chen CY et al. (2006) Early response of hepatocellular carcinoma to transcatheter arterial chemoembolization: choline levels and MR diffusion constants—initial experience. Radiology 239: 448–456

    PubMed  Google Scholar 

  82. Naganawa S et al. (2005) Apparent diffusion coefficient in cervical cancer of the uterus: comparison with the normal uterine cervix. Eur Radiol 15: 71–78

    PubMed  Google Scholar 

  83. Einarsdottir H et al. (2004) Diffusion-weighted MRI of soft tissue tumours. Eur Radiol 14: 959–963

    PubMed  Google Scholar 

  84. Pickles MD et al. (2006) Diffusion changes precede size reduction in neoadjuvant treatment of breast cancer. Magn Reson Imaging 24: 843–847

    PubMed  Google Scholar 

  85. Manton DJ et al. (2006) Neoadjuvant chemotherapy in breast cancer: early response prediction with quantitative MR imaging and spectroscopy. Br J Cancer 94: 427–435

    CAS  PubMed  PubMed Central  Google Scholar 

  86. Mardor Y et al. (2003) Early detection of response to radiation therapy in patients with brain malignancies using conventional and high b-value diffusion-weighted magnetic resonance imaging. J Clin Oncol 21: 1094–1100

    PubMed  Google Scholar 

  87. Jacobs MA et al. (2005) Uterine fibroids: diffusion-weighted MR imaging for monitoring therapy with focused ultrasound surgery—preliminary study. Radiology 236: 196–203

    PubMed  Google Scholar 

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Acknowledgements

We would like to thank Mr James d'Arcy and Dr Dow Mu-Koh from Cancer Research UK Clinical Magnetic Resonance Research Group, Institute of Cancer Research and The Royal Marsden NHS Trust, Sutton, UK, for providing DiffusionView software which was used to create illustrations for this Review. Drs Dow Mu-Koh and N Jane Taylor (Mount Vernon Hospital, Northwood, UK) also provided many helpful perspectives on the manuscript.

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Authors and Affiliations

  1. DM Patterson is a Research Fellow in the Department of Medical Oncology, Mount Vernon Hospital, Northwood, UK,

    Daniel M Patterson

  2. AR Padhani is Consultant Radiologist and Head of Imaging Research at the Paul Strickland Scanner Centre, Mount Vernon Hospital, Northwood, UK and Synarc Inc., San Francisco, CA, USA,

    Anwar R Padhani

  3. DJ Collins is Principal Clinical Scientist, Cancer Research UK Clinical Magnetic Resonance Research Group, Institute of Cancer Research and The Royal Marsden NHS Trust, Sutton, UK.,

    David J Collins

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  1. Daniel M Patterson
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Correspondence to Anwar R Padhani.

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Supplementary information

Supplementary Box 1.

Glossary terms. (DOC 22 kb)

Supplementary Figure 1.

Fibroid undergoing infarction. (DOC 75 kb)

Supplementary Figure 2.

Nodal metastases from squamous carcinoma with image registration. (DOC 59 kb)

Supplementary Figure 3.

Antiangiogenic effects of Taxol chemotherapy on breast cancer. (DOC 72 kb)

Supplementary Figure 4.

Nodal involvement by Hodgkin's lymphomal. (DOC 46 kb)

Supplementary Figure 5.

Sacral metastases incompletely treated by chemoradiation. (DOC 118 kb)

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Patterson, D., Padhani, A. & Collins, D. Technology Insight: water diffusion MRI—a potential new biomarker of response to cancer therapy. Nat Rev Clin Oncol 5, 220–233 (2008). https://doi.org/10.1038/ncponc1073

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