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Computational and Mathematical Methods in Medicine
Volume 2015, Article ID 103843, 10 pages
http://dx.doi.org/10.1155/2015/103843
Research Article

Multimodality Functional Imaging in Radiation Therapy Planning: Relationships between Dynamic Contrast-Enhanced MRI, Diffusion-Weighted MRI, and 18F-FDG PET

1Medical Physics Department and Radiological Protection, Galaria-Hospital do Meixoeiro-Complexo Hospitalario Universitario de Vigo, 36200 Vigo, Spain
2Signal Theory and Communications Department, University of Vigo, 36310 Vigo, Spain
3Radiation Medicine Program, Princess Margaret Cancer Centre and University Health Network, Toronto, ON, Canada M5T 2M9
4Radiation Oncology Department, Galaria-Hospital do Meixoeiro-Complexo Hospitalario Universitario de Vigo, 36200 Vigo, Spain

Received 4 July 2014; Revised 15 September 2014; Accepted 10 October 2014

Academic Editor: Alexandru Dasu

Copyright © 2015 Moisés Mera Iglesias et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. C. Grau, M. Hoyer, M. Alber, J. Overgaard, J. C. Lindegaard, and L. P. Muren, “Biology-guided adaptive radiotherapy (BiGART)—more than a vision?” Acta Oncologica, vol. 52, no. 7, pp. 1243–1247, 2013. View at Publisher · View at Google Scholar · View at Scopus
  2. K. Smit, B. van Asselen, J. G. M. Kok, A. H. L. Aalbers, J. J. W. Lagendijk, and B. W. Raaymakers, “Towards reference dosimetry for the MR-linac: magnetic field correction of the ionization chamber reading,” Physics in Medicine and Biology, vol. 58, no. 17, pp. 5945–5957, 2013. View at Publisher · View at Google Scholar · View at Scopus
  3. D. Saenz, B. Paliwal, and J. Bayouth, “A dose homogeneity and conformity evaluation between ViewRay and pinnacle-based linear accelerator IMRT treatment plans,” Journal of Medical Physics, vol. 39, no. 2, pp. 64–70, 2014. View at Publisher · View at Google Scholar · View at Scopus
  4. J. Winter, M. Westmore, M. Dahan et al., “Patient alignment in MRI guided radiation therapy,” US Patent 20130235969 A1, 2013.
  5. B. W. Raaymakers, J. J. W. Lagendijk, J. Overweg et al., “Integrating a 1.5 T MRI scanner with a 6 MV accelerator: proof of concept,” Physics in Medicine and Biology, vol. 54, no. 12, pp. N229–N237, 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. V. Grégoire, K. Haustermans, X. Geets, S. Roels, and M. Lonneux, “PET-based treatment planning in radiotherapy: a new standard?” Journal of Nuclear Medicine, vol. 48, supplement 1, pp. 68S–77S, 2007. View at Google Scholar · View at Scopus
  7. M. Lonneux, M. Hamoir, H. Reychler et al., “Positron emission tomography with [18F]fluorodeoxyglucose improves staging and patient management in patients with head and neck squamous cell carcinoma: a multicenter prospective study,” Journal of Clinical Oncology, vol. 28, no. 7, pp. 1190–1195, 2010. View at Publisher · View at Google Scholar · View at Scopus
  8. M. MacManus, U. Nestle, K. E. Rosenzweig et al., “Use of PET and PET/CT for radiation therapy planning: IAEA expert report 2006-2007,” Radiotherapy and Oncology, vol. 91, no. 1, pp. 85–94, 2009. View at Publisher · View at Google Scholar · View at Scopus
  9. D. A. Hamstra, T. L. Chenevert, B. A. Moffat et al., “Evaluation of the functional diffusion map as an early biomarker of time-to-progression and overall survival in high-grade glioma,” Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 46, pp. 16759–16764, 2005. View at Publisher · View at Google Scholar · View at Scopus
  10. C. J. Galbán, S. K. Mukherji, T. L. Chenevert et al., “A feasibility study of parametric response map analysis of diffusion-weighted magnetic resonance imaging scans of head and neck cancer patients for providing early detection of therapeutic efficacy,” Translational Oncology, vol. 2, no. 3, pp. 184–190, 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. S. Kim, L. Loevner, H. Quon et al., “Diffusion-weighted magnetic resonance imaging for predicting and detecting early response to chemoradiation therapy of squamous cell carcinomas of the head and neck,” Clinical Cancer Research, vol. 15, no. 3, pp. 986–994, 2009. View at Publisher · View at Google Scholar · View at Scopus
  12. N. C. Atuegwu, J. C. Gore, and T. E. Yankeelov, “The integration of quantitative multi-modality imaging data into mathematical models of tumors,” Physics in Medicine and Biology, vol. 55, no. 9, pp. 2429–2449, 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. L. A. P. Romasanta, M. J. G. Velloso, and A. L. Medina, “Functional imaging in radiation therapy planning for head and neck cancer,” Reports of Practical Oncology and Radiotherapy, vol. 18, no. 6, pp. 376–382, 2013. View at Publisher · View at Google Scholar · View at Scopus
  14. J. M. Arbeit, J. M. Brown, K. S. C. Chao et al., “Hypoxia: importance in tumor biology, noninvasive measurement by imaging, and value of its measurement in the management of cancer therapy,” International Journal of Radiation Biology, vol. 82, no. 10, pp. 699–757, 2006. View at Publisher · View at Google Scholar · View at Scopus
  15. A. R. Padhani, K. A. Krohn, J. S. Lewis, and M. Alber, “Imaging oxygenation of human tumours,” European Radiology, vol. 17, no. 4, pp. 861–872, 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. Y. Cao, C. I. Tsien, V. Nagesh et al., “Clinical investigation survival prediction in high-grade gliomas by MRI perfusion before and during early stage of RT,” International Journal of Radiation Oncology Biology Physics, vol. 64, no. 3, pp. 876–885, 2006. View at Publisher · View at Google Scholar · View at Scopus
  17. J. F. Dunn, J. A. O'Hara, Y. Zaim-Wadghiri et al., “Changes in oxygenation of intracranial tumors with carbogen: a BOLD MRI and EPR oximetry study,” Journal of Magnetic Resonance Imaging, vol. 16, no. 5, pp. 511–521, 2002. View at Publisher · View at Google Scholar · View at Scopus
  18. M. A. Zahra, K. G. Hollingsworth, E. Sala, D. J. Lomas, and L. T. Tan, “Dynamic contrast-enhanced MRI as a predictor of tumour response to radiotherapy,” The Lancet Oncology, vol. 8, no. 1, pp. 63–74, 2007. View at Publisher · View at Google Scholar · View at Scopus
  19. J. T. Elliott, E. A. Wright, K. M. Tichauer et al., “Arterial input function of an optical tracer for dynamic contrast enhanced imaging can be determined from pulse oximetry oxygen saturation measurements,” Physics in Medicine and Biology, vol. 57, no. 24, pp. 8285–8295, 2012. View at Publisher · View at Google Scholar · View at Scopus
  20. K. Newbold, I. Castellano, E. Charles-Edwards et al., “An exploratory study into the rol of dynamic contrast-enhanced magnetic resonance imaging or perfusion computed tomography for detection of intratumoral hypoxia in head and neck cancer,” International Journal of Radiation Oncology Biology Physics, vol. 74, no. 1, pp. 29–37, 2009. View at Publisher · View at Google Scholar · View at Scopus
  21. S. B. Donaldson, G. Betts, S. C. Bonington et al., “Perfusion estimated with rapid dynamic contrast-enhanced magnetic resonance imaging correlates inversely with vascular endothelial growth factor expression and pimonidazole staining in head-and-neck cancer: a pilot study,” International Journal of Radiation Oncology Biology Physics, vol. 81, no. 4, pp. 1176–1183, 2011. View at Publisher · View at Google Scholar · View at Scopus
  22. J. M. Berstein, J. J. Homer, and C. M. West, “Dynamic contrast-enhanced magnetic resonance imaging biomarkers in head and neck cancer: potential to guide treatment? A systematic review,” Oral Oncology, vol. 50, no. 10, pp. 963–970, 2014. View at Publisher · View at Google Scholar
  23. M. Busk, M. R. Horsman, S. Jakobsen et al., “Can hypoxia-PET map hypoxic cell density heterogeneity accurately in an animal tumor model at a clinically obtainable image contrast?” Radiotherapy and Oncology, vol. 92, no. 3, pp. 429–436, 2009. View at Publisher · View at Google Scholar · View at Scopus
  24. N. Y. Lee, J. G. Mechalakos, S. Nehmeh et al., “Fluorine-18-labeled fluoromisonidazole positron emission and computed tomography-guided intensity-modulated radiotherapy for head and neck cancer: a feasibility study,” International Journal of Radiation Oncology, Biology, Physics, vol. 70, no. 1, pp. 2–13, 2008. View at Publisher · View at Google Scholar · View at Scopus
  25. S. A. Nehmeh, N. Y. Lee, H. Schröder et al., “Reproducibility of intratumor distribution of (18)F-fluoromisonidazole in head and neck cancer,” International Journal of Radiation Oncology Biology Physics, vol. 70, no. 1, pp. 235–242, 2008. View at Publisher · View at Google Scholar · View at Scopus
  26. D. Thorwarth, S.-M. Eschmann, F. Paulsen, and M. Alber, “Hypoxia dose painting by numbers: a planning study,” International Journal of Radiation Oncology Biology Physics, vol. 68, no. 1, pp. 291–300, 2007. View at Publisher · View at Google Scholar · View at Scopus
  27. S.-M. Eschmann, F. Paulsen, M. Reimold et al., “Prognostic impact of hypoxia imaging with 18F-misonidazole PET in non-small cell lung cancer and head and neck cancer before radiotherapy,” Journal of Nuclear Medicine, vol. 46, no. 2, pp. 253–260, 2005. View at Google Scholar · View at Scopus
  28. K. Røe, T. B. Aleksandersen, A. Kristian et al., “Preclinical dynamic 18F-FDG PET tumor characterization and radiotherapy response assessment by kinetic compartment analysis,” Acta Oncologica, vol. 49, no. 7, pp. 914–921, 2010. View at Publisher · View at Google Scholar · View at Scopus
  29. V. A. Semenenko, B. Reitz, E. Day, X. S. Qi, M. Miften, and X. A. Li, “Evaluation of a commercial biologically based IMRT treatment planning system,” Medical Physics, vol. 35, no. 12, pp. 5851–5860, 2008. View at Publisher · View at Google Scholar · View at Scopus
  30. P. Stavrev, D. Hristov, B. Warkentin, E. Sham, N. Stavreva, and B. G. Fallone, “Inverse treatment planning by physically constrained minimization of a biological objective function,” Medical Physics, vol. 30, no. 11, pp. 2948–2958, 2003. View at Publisher · View at Google Scholar · View at Scopus
  31. J. Heukelom, O. Hamming, H. Bartelink et al., “Adaptive and innovative Radiation Treatment FOR improving Cancer treatment outcomE (ARTFORCE); a randomized controlled phase II trial for individualized treatment of head and neck cancer,” BMC Cancer, vol. 13, article 84, 2013. View at Publisher · View at Google Scholar · View at Scopus
  32. D. Thorwarth and M. Alber, “Implementation of hypoxia imaging into treatment planning and delivery,” Radiotherapy and Oncology, vol. 97, no. 2, pp. 172–175, 2010. View at Publisher · View at Google Scholar · View at Scopus
  33. I. Toma-Dasu, J. Uhrdin, L. Antonovic et al., “Dose prescription and treatment planning based on FMISO-PET hypoxia,” Acta Oncologica, vol. 51, no. 2, pp. 222–230, 2012. View at Publisher · View at Google Scholar · View at Scopus
  34. A. López Medina, D. Aramburu, M. Mera et al., “ARTFIBio project: quantifying tumour response voxel by voxel,” Radiotherapy & Oncology, vol. 106, p. S329, 2013. View at Google Scholar
  35. A. L. Medina, D. Aramburu, M. Mera et al., “Tumour response: a multiparametric function,” Radiotherapy & Oncology, vol. 111, supplement 1, pp. 149–150, 2014. View at Google Scholar
  36. I. Landesa-Vázquez, J. Alba-Castro, M. Mera-Iglesias, D. Aramburu-Núñez, A. López-Medina, and V. Muñóz-Garzón, “ARTFIBio: a cross-platform image registration tool for tumor response quantification in head and neck cancer,” in Proceedings of the IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ’14), pp. 149–152, Valencia, Spain, June 2014. View at Publisher · View at Google Scholar
  37. O. Warburg, “On the origin of cancer cells,” Science, vol. 123, no. 3191, pp. 309–314, 1956. View at Publisher · View at Google Scholar · View at Scopus
  38. T. Roose, S. J. Chapman, and P. K. Maini, “Mathematical models of avascular tumor growth,” SIAM Review, vol. 49, no. 2, pp. 179–208, 2007. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  39. M. Busk, M. R. Horsman, P. E. G. Kristjansen, A. J. van der Kogel, J. Bussink, and J. Overgaard, “Aerobic glycolysis in cancers: implications for the usability of oxygen-responsive genes and fluorodeoxyglucose-PET as markers of tissue hypoxia,” International Journal of Cancer, vol. 122, no. 12, pp. 2726–2734, 2008. View at Publisher · View at Google Scholar · View at Scopus
  40. R. A. Gatenby, E. T. Gawlinski, A. F. Gmitro, B. Kaylor, and R. J. Gillies, “Acid-mediated tumor invasion: a multidisciplinary study,” Cancer Research, vol. 66, no. 10, pp. 5216–5223, 2006. View at Publisher · View at Google Scholar · View at Scopus
  41. E. O. Stejskal and J. E. Tanner, “Spin diffusion measurements: spin echoes in the presence of a time-dependent field gradient,” The Journal of Chemical Physics, vol. 42, no. 1, pp. 288–292, 1965. View at Publisher · View at Google Scholar · View at Scopus
  42. D.-M. Koh and D. J. Collins, “Diffusion-weighted MRI in the body: applications and challenges in oncology,” American Journal of Roentgenology, vol. 188, no. 6, pp. 1622–1635, 2007. View at Publisher · View at Google Scholar · View at Scopus
  43. A. C. Morani, K. M. Elsayes, P. S. Liu et al., “Abdominal applications of diffusion-weighted magnetic resonance imaging: where do we stand?” World Journal of Radiology, vol. 5, no. 3, pp. 68–80, 2013. View at Publisher · View at Google Scholar
  44. J. F. Kallehauge, C. Nomden, and A. C. S. de Castro, “Temporal changes in DCE-MRI parameters during treatment of locally advanced cervical cancer,” Radiotherapy & Oncology, vol. 103, p. S78, 2012. View at Google Scholar
  45. D. S. Yoo, J. P. Kirkpatrick, O. Craciunescu et al., “Prospective trial of synchronous bevacizumab, erlotinib, and concurrent chemoradiation in locally advanced head and neck cancer,” Clinical Cancer Research, vol. 18, no. 5, pp. 1404–1414, 2012. View at Publisher · View at Google Scholar · View at Scopus
  46. D. Zheng, Y. Chen, X. Liu et al., “Early response to chemoradiotherapy for nasopharyngeal carcinoma treatment: value of dynamic contrast-enhanced 3.0 T MRI,” Journal of Magnetic Resonance Imaging, 2014. View at Publisher · View at Google Scholar
  47. B. Titz and R. Jeraj, “An imaging-based tumour growth and treatment response model: investigating the effect of tumour oxygenation on radiation therapy response,” Physics in Medicine and Biology, vol. 53, no. 17, pp. 4471–4488, 2008. View at Publisher · View at Google Scholar · View at Scopus
  48. World Medical Organization, “Declaration of Helsinki,” British Medical Journal, vol. 313, no. 7070, pp. 1448–1449, 1996. View at Google Scholar
  49. http://www.thedcetool.com/scientific.
  50. P. S. Tofts, “Modeling tracer kinetics in dynamic Gd-DTPA MR imaging,” Journal of Magnetic Resonance Imaging, vol. 7, no. 1, pp. 91–101, 1997. View at Publisher · View at Google Scholar · View at Scopus
  51. C. Coolens, B. Driscoll, C. Chung et al., “Automated voxel-based analysis of volumetric DCE CT data improves the measurement of serial changes in tumor vascular biomarkers,” International Journal of Radiation Oncology, Biology, Physics. In press.
  52. W. Foltz, B. Driscoll, S. J. Lee et al., “Comparison of arterial input functions by magnitude and phase signal measurement in dynamic contrast enhancement MRI using a dynamic flow phantom,” Medical Physics. In press.
  53. IAEA, Quantitative Nuclear Medicine Imaging: Concepts, Requirements and Methods, IAEA Library, 2014.
  54. En línea, http://www.itk.org/.
  55. T. W. Secomb, R. Hsu, M. W. Dewhirst, B. Klitzman, and J. F. Gross, “Analysis of oxygen transport to tumor tissue by microvascular networks,” International Journal of Radiation Oncology, Biology, Physics, vol. 25, no. 3, pp. 481–489, 1993. View at Publisher · View at Google Scholar · View at Scopus
  56. C. J. Kelly and M. Brady, “A model to simulate tumour oxygenation and dynamic [18F]-Fmiso PET data,” Physics in Medicine and Biology, vol. 51, no. 22, pp. 5859–5873, 2006. View at Publisher · View at Google Scholar · View at Scopus
  57. S. S. Foo, D. F. Abbott, N. Lawrentschuk, and A. M. Scott, “Functional imaging of intratumoral hypoxia,” Molecular Imaging and Biology, vol. 6, no. 5, pp. 291–305, 2004. View at Publisher · View at Google Scholar · View at Scopus
  58. R. A. Cooper, B. M. Carrington, J. A. Loncaster et al., “Tumour oxygenation levels correlate with dynamic contrast-enhanced magnetic resonance imaging parameters in carcinoma of the cervix,” Radiotherapy and Oncology, vol. 57, no. 1, pp. 53–59, 2000. View at Publisher · View at Google Scholar · View at Scopus
  59. H. Lyng, A. O. Vorren, K. Sundfør et al., “Assessment of tumor oxygenation in human cervical carcinoma by use of dynamic Gd-DTPA-enhanced MR imaging,” Journal of Magnetic Resonance Imaging, vol. 14, no. 6, pp. 750–756, 2001. View at Publisher · View at Google Scholar · View at Scopus
  60. P. S. Morgan, R. W. Bowtell, D. J. O. McIntyre, and B. S. Worthington, “Correction of spatial distortion in EPI due to inhomogeneous static magnetic fields using the reversed gradient method,” Journal of Magnetic Resonance Imaging, vol. 19, no. 4, pp. 499–507, 2004. View at Publisher · View at Google Scholar · View at Scopus
  61. J. G. Eriksen and M. R. Horsman, “Tumour hypoxia—a characteristic feature with a complex molecular background,” Radiotherapy and Oncology, vol. 81, no. 2, pp. 119–121, 2006. View at Publisher · View at Google Scholar · View at Scopus
  62. T. E. Yankeelov, M. Lepage, A. Chakravarthy et al., “Integration of quantitative DCE-MRI and ADC mapping to monitor treatment response in human breast cancer: initial results,” Magnetic Resonance Imaging, vol. 25, no. 1, pp. 1–13, 2007. View at Publisher · View at Google Scholar · View at Scopus
  63. P. Dirix, V. Vandecaveye, F. De Keyzer, S. Stroobants, R. Hermans, and S. Nuyts, “Dose painting in radiotherapy for head and neck squamous cell carcinoma: value of repeated functional imaging with 18F-FDG PET, 18F-fluoromisonidazole PET, diffusion-weighted MRI, and dynamic contrast-enhanced MRI,” Journal of Nuclear Medicine, vol. 50, no. 7, pp. 1020–1027, 2009. View at Publisher · View at Google Scholar · View at Scopus
  64. S. Baba, T. Isoda, Y. Maruoka et al., “Diagnostic and prognostic value of pretreatment SUV in 18F-FDG/ PET in breast cancer: comparison with apparent diffusion coefficient from diffusion-weighted MR imaging,” Journal of Nuclear Medicine, vol. 55, no. 5, pp. 736–742, 2014. View at Publisher · View at Google Scholar · View at Scopus
  65. B. B. Choi, S. H. Kim, B. J. Kang et al., “Diffusion-weighted imaging and FDG PET/CT: predicting the prognoses with apparent diffusion coefficient values and maximum standardized uptake values in patients with invasive ductal carcinoma,” World Journal of Surgical Oncology, vol. 10, article 126, 2012. View at Publisher · View at Google Scholar · View at Scopus
  66. J. A. Weis, M. I. Miga, L. R. Arlinghaus et al., “A mechanically coupled reaction-diffusion model for predicting the response of breast tumors to neoadjuvant chemotherapy,” Physics in Medicine and Biology, vol. 58, no. 17, pp. 5851–5866, 2013. View at Publisher · View at Google Scholar · View at Scopus
  67. B. Driscoll, H. Keller, D. Jaffray, and C. Coolens, “Development of a dynamic quality assurance testing protocol for multisite clinical trial DCE-CT accreditation,” Medical Physics, vol. 40, no. 8, Article ID 081906, 2013. View at Publisher · View at Google Scholar · View at Scopus