About this Journal Submit a Manuscript Table of Contents
ISRN Biomathematics
Volume 2012 (2012), Article ID 287394, 12 pages
http://dx.doi.org/10.5402/2012/287394
Review Article

Integrating Imaging Data into Predictive Biomathematical and Biophysical Models of Cancer

Thomas E. Yankeelov1,2,3,4,5,6

1Institute of Imaging Science, Vanderbilt University, AA-1105 Medical Center North, 1161 21st Avenue South, Nashville, TN 37232-2310, USA
2Department of Radiology and Radiological Sciences, Vanderbilt University, AA-1105 Medical Center North, 1161 21st Avenue South, Nashville, TN 37232-2310, USA
3Department of Biomedical Engineering, Vanderbilt University, AA-1105 Medical Center North, 1161 21st Avenue South, Nashville, TN 37232-2310, USA
4Department of Physics, Vanderbilt University, AA-1105 Medical Center North, 1161 21st Avenue South, Nashville, TN 37232-2310, USA
5Department of Cancer Biology, Vanderbilt University, AA-1105 Medical Center North, 1161 21st Avenue South, Nashville, TN 37232-2310, USA
6Vanderbilt Ingram Cancer Center, Vanderbilt University, 1161 21st Avenue South, Nashville, TN 37232-2310, USA

Received 1 November 2012; Accepted 21 November 2012

Academic Editors: J. Chow, S.-C. Ngan, and J. Suehnel

Copyright © 2012 Thomas E. Yankeelov. 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. P. Therasse, S. G. Arbuck, E. A. Eisenhauer et al., “New guidelines to evaluate the response to treatment in solid tumors,” Journal of the National Cancer Institute, vol. 92, no. 3, pp. 205–216, 2000. View at Scopus
  2. L. R. Arlinghaus, X. Li, M. Levy et al., “Current and future trends in magnetic resonance imaging assessments of the response of breast tumors to neoadjuvant chemotherapy,” Journal of Oncology, vol. 2010, Article ID 919620, 17 pages, 2010. View at Publisher · View at Google Scholar · View at Scopus
  3. R. L. Wahl, H. Jacene, Y. Kasamon, and M. A. Lodge, “From RECIST to PERCIST: evolving considerations for PET response criteria in solid tumors,” Journal of Nuclear Medicine, vol. 50, supplement 1, pp. 122S–150S, 2009. View at Publisher · View at Google Scholar · View at Scopus
  4. M.-C. Asselin, J. P. B. O'Connor, R. Boellaard, N. A. Thacker, and A. Jackson, “Quantifying heterogeneity in human tumours using MRI and PET,” European Journal of Cancer, vol. 48, no. 4, pp. 447–455, 2012. View at Publisher · View at Google Scholar
  5. E. A. Eisenhauer, P. Therasse, J. Bogaerts et al., “New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1),” European Journal of Cancer, vol. 45, no. 2, pp. 228–247, 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. A. R. A. Anderson and V. Quaranta, “Integrative mathematical oncology,” Nature Reviews Cancer, vol. 8, no. 3, pp. 227–234, 2008. View at Publisher · View at Google Scholar · View at Scopus
  7. K. A. Rejniak and A. R. A. Anderson, “State of the art in computational modelling of cancer,” Mathematical Medicine and Biology, vol. 29, no. 1, pp. 1–2, 2012. View at Publisher · View at Google Scholar
  8. A. H. Juffer, U. Marin, O. Niemitalo, and J. Koivukangas, “Computer modeling of brain tumor growth,” Mini Reviews in Medicinal Chemistry, vol. 8, no. 14, pp. 1494–1506, 2008. View at Scopus
  9. T. E. Yankeelov, J. C. Gore, and J. C. Dynamic, “Dynamic contrast enhanced magnetic resonance imaging in oncology: theory, data acquisition, analysis, and examples,” Current Medical Imaging Reviews, vol. 3, no. 2, pp. 91–107, 2009. View at Scopus
  10. E. S. Paulson and K. M. Schmainda, “Comparison of dynamic susceptibility-weighted contrast-enhanced MR methods: recommendations for measuring relative cerebral blood volume in brain tumors,” Radiology, vol. 249, no. 2, pp. 601–613, 2008. View at Publisher · View at Google Scholar · View at Scopus
  11. H. Dongfeng, M. Daqing, and J. Erhu, “Dynamic breast magnetic resonance imaging: pretreatment prediction of tumor response to neoadjuvant chemotherapy,” Clinical Breast Cancer, vol. 12, no. 2, pp. 94–101, 2012. View at Publisher · View at Google Scholar
  12. J. Guo, W. E. Reddick, J. O. Glass et al., “Dynamic contrast-enhanced magnetic resonance imaging as a prognostic factor in predicting event-free and overall survival in pediatric patients with osteosarcoma,” Cancer, vol. 118, no. 15, pp. 3776–3785, 2012. View at Publisher · View at Google Scholar
  13. M. E. Loveless, D. Lawson, M. Collins et al., “Comparisons of the efficacy of a Jak1/2 inhibitor (AZD1480) with a VEGF signaling inhibitor (cediranib) and sham treatments in mouse tumors using DCE-MRI, DW-MRI, and histology,” Neoplasia, vol. 14, no. 1, pp. 54–64, 2012.
  14. M. A. Zahra, L. T. Tan, A. N. Priest et al., “Semiquantitative and quantitative dynamic contrast-enhanced magnetic resonance imaging measurements predict radiation response in cervix csancer,” International Journal of Radiation Oncology Biology Physics, vol. 74, no. 3, pp. 766–773, 2009. View at Publisher · View at Google Scholar · View at Scopus
  15. A. Einstein, “Über die von der molekularkinetischen Theorie der Wärme geforderte Bewegung von in ruhenden Flüssigkeiten suspendierten Teilchen,” Annalen der Physik, vol. 322, no. 8, pp. 549–560, 1905. View at Publisher · View at Google Scholar
  16. A. W. Anderson, J. Xie, J. Pizzonia, R. A. Bronen, D. D. Spencer, and J. C. Gore, “Effects of cell volume fraction changes on apparent diffusion in human cells,” Magnetic Resonance Imaging, vol. 18, no. 6, pp. 689–695, 2000. View at Publisher · View at Google Scholar · View at Scopus
  17. S. Bonekamp, C. P. Corona-Villalobos, and I. R. Kamel, “Oncologic applications of diffusion-weighted MRI in the body,” Journal of Magnetic Resonance Imaging, vol. 35, no. 2, pp. 257–279, 2012. View at Publisher · View at Google Scholar
  18. C. Fu, D. Bian, F. Liu, X. Feng, W. Du, and X. Wang, “The value of diffusion-weighted magnetic resonance imaging in assessing the response of locally advanced cervical cancer to neoadjuvant chemotherapy,” International Journal of Gynecological Cancer, vol. 22, no. 6, pp. 1037–1043, 2012. View at Publisher · View at Google Scholar
  19. S. Yoshida, F. Koga, S. Kobayashi et al., “Role of diffusion-weighted magnetic resonance imaging in predicting sensitivity to chemoradiotherapy in muscle-invasive bladder cancer,” International Journal of Radiation Oncology Biology Physics, vol. 83, no. 1, pp. e21–e27, 2012. View at Publisher · View at Google Scholar
  20. D. M. Patterson, A. R. Padhani, and D. J. Collins, “Technology insight: water diffusion MRI: a potential new biomarker of response to cancer therapy,” Nature Clinical Practice Oncology, vol. 5, no. 4, pp. 220–233, 2008. View at Publisher · View at Google Scholar · View at Scopus
  21. P. J. Basser, J. Mattiello, and D. LeBihan, “MR diffusion tensor spectroscopy and imaging,” Biophysical Journal, vol. 66, no. 1, pp. 259–267, 1994. View at Scopus
  22. S. Mori and P. C. van Zijl, “Fiber tracking: principles and strategies: a technical review,” NMR in Biomedicine, vol. 15, no. 7-8, pp. 468–480, 2002. View at Publisher · View at Google Scholar · View at Scopus
  23. X. Golay, H. Jiang, P. C. van Zijl, and S. Mori, “High-resolution isotropic 3D diffusion tensor imaging of the human brain,” Magnetic Resonance in Medicine, vol. 47, no. 5, pp. 837–843, 2002. View at Publisher · View at Google Scholar · View at Scopus
  24. G. J. M. Parker, C. A. M. Wheeler-Kingshott, and G. J. Barker, “Estimating distributed anatomical connectivity using fast marching methods and diffusion tensor imaging,” IEEE Transactions on Medical Imaging, vol. 21, no. 5, pp. 505–512, 2002. View at Publisher · View at Google Scholar · View at Scopus
  25. A. Mishra, Y. Lu, J. Meng, A. W. Anderson, and Z. Ding, “Unified framework for anisotropic interpolation and smoothing of diffusion tensor images,” NeuroImage, vol. 31, no. 4, pp. 1525–1535, 2006. View at Publisher · View at Google Scholar · View at Scopus
  26. E. R. Gerstner and A. G. Sorensen, “Diffusion and diffusion tensor imaging in brain cancer,” Seminars in Radiation Oncology, vol. 21, no. 2, pp. 141–146, 2011. View at Publisher · View at Google Scholar · View at Scopus
  27. A. Roniotis, G. C. Manikis, V. Sakkalis, M. E. Zervakis, I. Karatzanis, and K. Marias, “High-grade glioma diffusive modeling using statistical tissue information and diffusion tensors extracted from atlases,” IEEE Transactions on Information Technology in Biomedicine, vol. 16, no. 2, Article ID 6041031, pp. 255–263, 2012. View at Publisher · View at Google Scholar
  28. S. Kim, S. Pickup, O. Hsu, and H. Poptani, “Diffusion tensor MRI in rat models of invasive and well-demarcated brain tumors,” NMR in Biomedicine, vol. 21, no. 3, pp. 208–216, 2008. View at Publisher · View at Google Scholar · View at Scopus
  29. D. Leclercq, C. Delmaire, N. M. de Champfleur, J. Chiras, and S. Lehéricy, “Diffusion tractography: methods, validation and applications in patients with neurosurgical lesions,” Neurosurgery Clinics of North America, vol. 22, no. 2, pp. 253–268, 2011. View at Publisher · View at Google Scholar · View at Scopus
  30. E. L. Rosen, W. B. Eubank, and D. A. Mankoff, “FDG PET, PET/CT, and breast cancer imaging,” Radiographics, vol. 27, supplement 1, pp. S215–S229, 2007. View at Publisher · View at Google Scholar · View at Scopus
  31. E. Skoura, I. E. Datseris, I. Platis, G. Oikonomopoulos, and K. N. Syrigos, “Role of positron emission tomography in the early prediction of response to chemotherapy in patients with non-small-cell lung cancer,” Clinical Lung Cancer, vol. 13, no. 3, pp. 181–187, 2012. View at Publisher · View at Google Scholar
  32. T. Cazaentre, F. Morschhauser, M. Vermandel et al., “Pre-therapy 18F-FDG PET quantitative parameters help in predicting the response to radioimmunotherapy in non-hodgkin lymphoma,” European Journal of Nuclear Medicine and Molecular Imaging, vol. 37, no. 3, pp. 494–504, 2010. View at Publisher · View at Google Scholar · View at Scopus
  33. J. Duch, D. Fuster, M. Muñoz et al., “18F-FDG PET/CT for early prediction of response to neoadjuvant chemotherapy in breast cancer,” European Journal of Nuclear Medicine and Molecular Imaging, vol. 36, no. 10, pp. 1551–1557, 2009. View at Publisher · View at Google Scholar · View at Scopus
  34. F. Lordick, “The role of PET in predicting response to chemotherapy in oesophago-gastric cancer,” Acta Gastro-Enterologica Belgica, vol. 74, no. 4, pp. 530–535, 2011.
  35. J. Czernin, “Oncological applications of FDG-PET,” in PET: Molecular Imaging and Its Biological Applications, M. Phelps, Ed., pp. 270–321, Springer, New York, NY, USA, 2004.
  36. D. A. Mankoff, A. F. Shields, and K. A. Krohn, “PET imaging of cellular proliferation,” Radiologic Clinics of North America, vol. 43, no. 1, pp. 153–167, 2005. View at Publisher · View at Google Scholar · View at Scopus
  37. A. Salskov, V. S. Tammisetti, J. Grierson, and H. Vesselle, “FLT: measuring tumor cell proliferation in vivo with positron emission tomography and 3′-deoxy-3′-[18F]Fluorothymidine,” Seminars in Nuclear Medicine, vol. 37, no. 6, pp. 429–439, 2007. View at Publisher · View at Google Scholar · View at Scopus
  38. L. Kenny, R. C. Coombes, D. M. Vigushin, A. Al-Nahhas, S. Shousha, and E. O. Aboagye, “Imaging early changes in proliferation at 1 week post chemotherapy: a pilot study in breast cancer patients with 3′-deoxy-3′-[18F]fluorothymidine positron emission tomography,” European Journal of Nuclear Medicine and Molecular Imaging, vol. 34, no. 9, pp. 1339–1347, 2007. View at Publisher · View at Google Scholar · View at Scopus
  39. H. J. Sohn, Y. J. Yang, J. S. Ryu et al., “[18F]fluorothymidine positron emission tomography before and 7 days after gefitinib treatment predicts response in patients with advanced adenocarcinoma of the lung,” Clinical Cancer Research, vol. 14, no. 22, pp. 7423–7429, 2008. View at Publisher · View at Google Scholar · View at Scopus
  40. D. Soloviev, D. Lewis, D. Honess, and E. Aboagye, “[18F]FLT: an imaging biomarker of tumour proliferation for assessment of tumour response to treatment,” European Journal of Cancer, vol. 48, no. 4, pp. 416–424, 2012. View at Publisher · View at Google Scholar
  41. J. Overgaard, “Hypoxic radiosensitization: adored and ignored,” Journal of Clinical Oncology, vol. 25, no. 26, pp. 4066–4074, 2007. View at Publisher · View at Google Scholar · View at Scopus
  42. M. Sørensen, M. R. Horsman, P. Cumming, O. L. Munk, and S. Keiding, “Effect of intratumoral heterogeneity in oxygenation status on FMISO PET, autoradiography, and electrode Po2 measurements in murine tumors,” International Journal of Radiation Oncology Biology Physics, vol. 62, no. 3, pp. 854–861, 2005. View at Publisher · View at Google Scholar · View at Scopus
  43. J. D. Chapman, “Hypoxic sensitizers—implications for radiation therapy,” The New England Journal of Medicine, vol. 301, no. 26, pp. 1429–1432, 1979. View at Scopus
  44. W. J. Koh, J. S. Rasey, M. L. Evans et al., “Imaging of hypoxia in human tumors with [F-18]Fluoromisonidazole,” International Journal of Radiation Oncology Biology Physics, vol. 22, no. 1, pp. 199–212, 1992. View at Scopus
  45. K. Hirata, S. Terasaka, T. Shiga et al., “18F-fluoromisonidazole positron emission tomography may differentiate glioblastoma multiforme from less malignant gliomas,” European Journal of Nuclear Medicine and Molecular Imaging, vol. 39, no. 5, pp. 760–770, 2012. View at Publisher · View at Google Scholar
  46. M. Kikuchi, T. Yamane, S. Shinohara et al., “18F-fluoromisonidazole positron emission tomography before treatment is a predictor of radiotherapy outcome and survival prognosis in patients with head and neck squamous cell carcinoma,” Annals of Nuclear Medicine, vol. 25, no. 9, pp. 1–9, 2011. View at Publisher · View at Google Scholar · View at Scopus
  47. C. Oehler, J. A. O'Donoghue, J. Russell et al., “18F-fluromisonidazole PET imaging as a biomarker for the response to 5,6-dimethylxanthenone-4-acetic acid in colorectal xenograft tumors,” Journal of Nuclear Medicine, vol. 52, no. 3, pp. 437–444, 2011. View at Publisher · View at Google Scholar · View at Scopus
  48. R. Bejot, V. Kersemans, C. Kelly, L. Carroll, R. C. King, and V. Gouverneur, “Pre-clinical evaluation of a 3-nitro-1,2,4-triazole analogue of [18F]FMISO as hypoxia-selective tracer for PET,” Nuclear Medicine and Biology, vol. 37, no. 5, pp. 565–575, 2010. View at Scopus
  49. S. T. Lee and A. M. Scott, “Hypoxia positron emission tomography imaging With 18F-Fluoromisonidazole,” Seminars in Nuclear Medicine, vol. 37, no. 6, pp. 451–461, 2007. View at Publisher · View at Google Scholar · View at Scopus
  50. Y. Yoshii, M. Yoneda, M. Ikawa et al., “Radiolabeled Cu-ATSM as a novel indicator of overreduced intracellular state due to mitochondrial dysfunction: studies with mitochondrial DNA-less ρ0 cells and cybrids carrying MELAS mitochondrial DNA mutation,” Nuclear Medicine and Biology, vol. 39, no. 2, pp. 177–185, 2012. View at Publisher · View at Google Scholar
  51. A. Obata, E. Yoshimi, A. Waki et al., “Retention mechanism of hypoxia selective nuclear imaging/radiotherapeutic agent Cu-diacetyl-bis(N4-methylthiosemicarbazone) (Cu-ATSM) in tumor cells,” Annals of Nuclear Medicine, vol. 15, no. 6, pp. 499–504, 2001. View at Scopus
  52. H. Kurihara, N. Honda, Y. Kono, and Y. Arai, “Radiolabelled agents for PET imaging of tumor hypoxia,” Current Medicinal Chemistry, vol. 19, no. 20, pp. 3282–3289, 2012. View at Publisher · View at Google Scholar
  53. Y. Minagawa, K. Shizukuishi, I. Koike et al., “Assessment of tumor hypoxia by 62Cu-ATSM PET/CT as a predictor of response in head and neck cancer: a pilot study,” Annals of Nuclear Medicine, vol. 25, no. 5, pp. 1–7, 2011. View at Scopus
  54. D. W. Dietz, F. Dehdashti, P. W. Grigsby et al., “Tumor hypoxia detected by positron emission tomography with 60Cu-ATSM as a predictor of response and survival in patients undergoing neoadjuvant chemoradiotherapy for rectal carcinoma: a pilot study,” Diseases of the Colon and Rectum, vol. 51, no. 11, pp. 1641–1648, 2008. View at Scopus
  55. F. Dehdashti, P. W. Grigsby, J. S. Lewis, R. Laforest, B. A. Siegel, and M. J. Welch, “Assessing tumor hypoxia in cervical cancer by PET with 60Cu- labeled diacetyl-bis(N4-methylthiosemicarbazone),” Journal of Nuclear Medicine, vol. 49, no. 2, pp. 201–205, 2008. View at Publisher · View at Google Scholar · View at Scopus
  56. F. Dehdashti, M. A. Mintun, J. S. Lewis et al., “In vivo assessment of tumor hypoxia in lung cancer with 60Cu-ATSM,” European Journal of Nuclear Medicine and Molecular Imaging, vol. 30, no. 6, pp. 844–850, 2003. View at Scopus
  57. J. P. Holland, J. S. Lewis, and F. Dehdashti, “Assessing tumor hypoxia by positron emission tomography with Cu-ATSM,” Quarterly Journal of Nuclear Medicine and Molecular Imaging, vol. 53, no. 2, pp. 193–200, 2009. View at Scopus
  58. C. Catana, D. Procissi, Y. Wu et al., “Simultaneous in vivo positron emission tomography and magnetic resonance imaging,” Proceedings of the National Academy of Sciences of the United States of America, vol. 105, no. 10, pp. 3705–3710, 2008. View at Publisher · View at Google Scholar · View at Scopus
  59. M. S. Judenhofer, H. F. Wehrl, D. F. Newport et al., “Simultaneous PET-MRI: a new approach for functional and morphological imaging,” Nature Medicine, vol. 14, no. 4, pp. 459–465, 2008. View at Publisher · View at Google Scholar · View at Scopus
  60. T. E. Yankeelov, T. E. Peterson, R. G. Abramson et al., “Simultaneous PET-MRI in oncology: a solution looking for a problem?” Magnetic Resonance Imaging, vol. 30, no. 9, pp. 1342–1356, 2012. View at Publisher · View at Google Scholar
  61. A. Boss, S. Bisdas, A. Kolb et al., “Hybrid PET/MRI of intracranial masses: initial experiences and comparison to PET/CT,” Journal of Nuclear Medicine, vol. 51, no. 8, pp. 1198–1205, 2010. View at Publisher · View at Google Scholar · View at Scopus
  62. C. Buchbender, T. A. Heusner, T. C. Lauenstein, A. Bockisch, and G. Antoch, “Oncologic PET/MRI, part 1: tumors of the brain, head and neck, chest, abdomen, and pelvis,” Journal of Nuclear Medicine, vol. 53, no. 6, pp. 928–938, 2012. View at Publisher · View at Google Scholar
  63. C. Buchbender, T. A. Heusner, T. C. Lauenstein, A. Bockisch, and G. Antoch, “Oncologic PET/MRI, part 2: bone tumors, soft-tissue tumors, melanoma, and lymphoma,” Journal of Nuclear Medicine, vol. 53, no. 8, pp. 1244–1252, 2012. View at Publisher · View at Google Scholar
  64. S. Jbabdi, E. Mandonnet, H. Duffau et al., “Simulation of anisotropic growth of low-grade gliomas using diffusion tensor imaging,” Magnetic Resonance in Medicine, vol. 54, no. 3, pp. 616–624, 2005. View at Publisher · View at Google Scholar · View at Scopus
  65. D. E. Woodward, J. Cook, P. Tracqui, G. C. Cruywagen, J. D. Murray, and E. C. Alvord, “A mathematical model of glioma growth: the effect of extent of surgical resection,” Cell Proliferation, vol. 29, no. 6, pp. 269–288, 1996. View at Scopus
  66. P. K. Burgess, P. M. Kulesa, J. D. Murray, and E. C. Alvord Jr., “The interaction of growth rates and diffusion coefficients in a three-dimensional mathematical model of gliomas,” Journal of Neuropathology and Experimental Neurology, vol. 56, no. 6, pp. 704–713, 1997. View at Scopus
  67. O. Clatz, M. Sermesant, P. Y. Bondiau et al., “Realistic simulation of the 3-D growth of brain tumors in MR images coupling diffusion with biomechanical deformation,” IEEE Transactions on Medical Imaging, vol. 24, no. 10, pp. 1334–1346, 2005. View at Publisher · View at Google Scholar · View at Scopus
  68. P. Y. Bondiau, O. Clatz, M. Sermesant et al., “Biocomputing: numerical simulation of glioblastoma growth using diffusion tensor imaging,” Physics in Medicine and Biology, vol. 53, no. 4, pp. 879–893, 2008. View at Publisher · View at Google Scholar · View at Scopus
  69. M. I. Miga, K. D. Paulsen, F. Kennedy, P. Hoopes, A. Hartov, and D. Roberts, “In vivo analysis of heterogeneous brain deformation computation for model updated image guidance,” Computer Methods in Biomechanics and Biomedical Engineering, vol. 3, no. 2, pp. 129–146, 2000.
  70. C. H. Wang, J. K. Rockhill, M. Mrugala et al., “Prognostic significance of growth kinetics in newly diagnosed glioblastomas revealed by combining serial imaging with a novel biomathematical model,” Cancer Research, vol. 69, no. 23, pp. 9133–9140, 2009. View at Publisher · View at Google Scholar · View at Scopus
  71. K. R. Swanson, H. L. P. Harpold, D. L. Peacock et al., “Velocity of radial expansion of contrast-enhancing gliomas and the effectiveness of radiotherapy in individual patients: a proof of principle,” Clinical Oncology, vol. 20, no. 4, pp. 301–308, 2008. View at Publisher · View at Google Scholar · View at Scopus
  72. R. Fisher, “The wave of advance of advantageous genes,” Annals of Eugenics, vol. 7, no. 4, pp. 355–369, 1937. View at Publisher · View at Google Scholar
  73. R. Rockne, E. C. Alvord Jr., J. K. Rockhill, and K. R. Swanson, “A mathematical model for brain tumor response to radiation therapy,” Journal of Mathematical Biology, vol. 58, no. 4-5, pp. 561–578, 2009. View at Publisher · View at Google Scholar · View at Scopus
  74. E. Hall, Radiobiology for the Radiologist, Lippincott, Philadelphia, Pa, USA, 1994.
  75. R. Rockne, J. K. Rockhill, M. Mrugala et al., “Predicting the efficacy of radiotherapy in individual glioblastoma patients in vivo: a mathematical modeling approach,” Physics in Medicine and Biology, vol. 55, no. 12, pp. 3271–3285, 2010. View at Publisher · View at Google Scholar · View at Scopus
  76. N. C. Atuegwu, L. R. Arlinghaus, X. Li et al., “Integration of diffusion-weighted MRI data and a simple mathematical model to predict breast tumor cellularity during neoadjuvant chemotherapy,” Magnetic Resonance in Medicine, vol. 66, no. 6, pp. 1689–1696, 2011. View at Publisher · View at Google Scholar
  77. N. C. Atuegwu, D. C. Colvin, M. E. Loveless, L. Xu, J. C. Gore, and T. E. Yankeelov, “Incorporation of diffusion-weighted magnetic resonance imaging data into a simple mathematical model of tumor growth,” Physics in Medicine and Biology, vol. 57, no. 1, pp. 225–240, 2012. View at Publisher · View at Google Scholar
  78. 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
  79. T. E. Yankeelov, N. C. Atuegwu, N. G. Deane, and J. C. Gore, “Modeling tumor growth and treatment response based on quantitative imaging data,” Integrative Biology, vol. 2, no. 7-8, pp. 338–345, 2010. View at Publisher · View at Google Scholar · View at Scopus
  80. N. C. A. L. Atuegwu, X. Li, E. B. Welch, A. B. Chakravarthy, and T. E. Yankeelov, Parameterizing the Logistic Model of Tumor Growth by DW-MRI and DCE-MRI to Predict Breast Tumor Cellularity During Neoadjuvant Chemotherapy, Montreal, Canada, 2012.
  81. B. M. Ellingson, P. S. Laviolette, S. D. Rand et al., “Spatially quantifying microscopic tumor invasion and proliferation using a voxel-wise solution to a glioma growth model and serial diffusion MRI,” Magnetic Resonance in Medicine, vol. 65, no. 4, pp. 1132–1144, 2011. View at Publisher · View at Google Scholar · View at Scopus
  82. B. M. Ellingson, T. F. Cloughesy, A. Lai, P. L. Nghiemphu, and W. B. Pope, “Cell invasion, motility, and proliferation level estimate (CIMPLE) maps derived from serial diffusion MR images in recurrent glioblastoma treated with bevacizumab,” Journal of Neuro-Oncology, vol. 105, no. 1, pp. 91–101, 2011. View at Publisher · View at Google Scholar · View at Scopus
  83. 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
  84. Y. Fujibayashi, H. Taniuchi, Y. Yonekura, H. Ohtani, J. Konishi, and A. Yokoyama, “Copper-62-ATSM: a new hypoxia imaging agent with high membrane permeability and low redox potential,” Journal of Nuclear Medicine, vol. 38, no. 7, pp. 1155–1160, 1997. View at Scopus
  85. M. D. Szeto, G. Chakraborty, J. Hadley et al., “Quantitative metrics of net proliferation and invasion link biological aggressiveness assessed by MRI with hypoxia assessed by FMISO-PET in newly diagnosed glioblastomas,” Cancer Research, vol. 69, no. 10, pp. 4502–4509, 2009. View at Publisher · View at Google Scholar · View at Scopus
  86. S. Gu, G. Chakraborty, K. Champley et al., “Applying a patient-specific bio-mathematical model of glioma growth to develop virtual [18F]-FMISO-PET images,” Mathematical Medicine and Biology, vol. 29, no. 1, pp. 31–48, 2012. View at Publisher · View at Google Scholar
  87. C. Vangestel, C. van de Wiele, N. van Damme et al., “99mTc-(CO)3 His-annexin A5 micro-SPECT demonstrates increased cell death by irinotecan during the vascular normalization window caused by bevacizumab,” Journal of Nuclear Medicine, vol. 52, no. 11, pp. 1786–1794, 2011.