Quantifying Tumour Hypoxia By Pet Imaging - A Theoretical Analysis

  • Iuliana Toma-Daşu
  • Alexandru Daşuu
  • Anders Brahmeu
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 645)


Information on tumour oxygenation could be obtained from various imaging methods, but the success of incorporating it into treatment planning depends on the accuracy of quantifying it. This study presents a theoretical analysis of the efficiency of measuring tumour hypoxia by PET imaging. Tissue oxygenations were calculated for ranges of biologically relevant physiological parameters and were then used to simulate PET images for markers with different uptake characteristics. The resulting images were used to calculate dose distributions that could lead to predefined tumour control levels. The results have shown that quantification of tumour hypoxia with PET may lead to different values according to the tracer used and the tumour site investigated. This would in turn be reflected into the dose distributions recommended by the optimisation algorithms. However, irrespective of marker-specific differences, focusing the radiation dose to the hypoxic areas appears to reduce the average tumour dose needed to achieve a certain control level.


Positron Emission Tomography Image Dose Distribution Tumour Control Probability Tumour Oxygenation Hypoxic Fraction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. M. Nordsmark, M. Overgaard, and J. Overgaard, Pretreatment oxygenation predicts radiation response in advanced squamous cell carcinoma of the head and neck, Radiother. Oncol. 41,31-39 (1996).PubMedGoogle Scholar
  2. 2.
    M. Høckel and P. Vaupel, Biological consequences of tumor hypoxia, Semin. Oncol. 28,36-41 (2001).PubMedCrossRefGoogle Scholar
  3. 3.
    A. Brahme, Biologically optimized 3-dimensional in vivo predictive assay-based radiation therapy using positron emission tomography-computerized tomography imaging, Acta Oncol. 42,123-136 (2003).PubMedCrossRefGoogle Scholar
  4. 4.
    A. Brahme, Fractionated and biologically optimized IMRT using in vivo predictive assay based radiation therapy (BIOART), Proceedings of the Fifth International Symposium on Target Volume Definition in Radiation Oncology(Limburg, 2005).Google Scholar
  5. 5.
    J. S. Rasey, W. J. Koh, M. L. Evans, L. M. Peterson, T. K. Lewellen, M. M. Graham, and K. A. Krohn, Quantifying regional hypoxia in human tumors with positron emission tomography of [18F]fluoromisonidazole: a pretherapy study of 37 patients, Int. J. Radiat. Oncol. Biol. Phys. 36,417-428 (1996).PubMedGoogle Scholar
  6. 6.
    J. S. Lewis, D. W. McCarthy, T. J. McCarthy, Y. Fujibayashi, and M. J. Welch, Evaluation of 64Cu-ATSM in vitro and in vivo in a hypoxic tumor model, J. Nucl. Med. 40,177-183 (1999).PubMedGoogle Scholar
  7. 7.
    F. Dehdashti, P. W. Grigsby, M. A. Mintun, J. S. Lewis, B. A. Siegel, and M. J. Welch, Assessing tumor hypoxia in cervical cancer by positron emission tomography with 60Cu-ATSM: relationship to therapeutic response-a preliminary report, Int. J. Radiat. Oncol. Biol. Phys. 55,1233-1238 (2003).PubMedGoogle Scholar
  8. 8.
    B. K. Lind and A. Brahme, The radiation response of heterogeneous tumors, Physica Medica(in press).Google Scholar
  9. 9.
    I. Toma-Daşu, Theoretical modelling of tumour oxygenation and influences on treatment outcome, PhD Thesis, Umeå University (2004).Google Scholar
  10. 10.
    J. S. Rasey, P. D. Hofstrand, L. K. Chin, and T. J. Tewson, Characterization of [18F]fluoroetanidazole, a new radiopharmaceutical for detecting tumor hypoxia, J. Nucl. Med. 40,1072-1079 (1999).PubMedGoogle Scholar
  11. 11.
    A. Daşu, I. Toma-Daşu, and M. Karlsson, Theoretical simulation of tumour oxygenation and results from acute and chronic hypoxia, Phys. Med. Biol. 48,2829-2842 (2003).PubMedCrossRefGoogle Scholar
  12. 12.
    A. Daşu and I. Toma-Daşu, Theoretical simulation of tumour oxygenation–practical applications, Adv. Exp. Med. Biol. 578,357-362 (2006).PubMedCrossRefGoogle Scholar
  13. 13.
    B. G. Douglas and J. F. Fowler, Fractionation schedules and a quadratic dose-effect relationship, Br. J. Radiol. 48,502-504 (1975).PubMedGoogle Scholar
  14. 14.
    G. W. Barendsen, Dose fractionation, dose rate and iso-effect relationships for normal tissue responses, Int. J. Radiat. Oncol. Biol. Phys. 8,1981-1997 (1982).PubMedGoogle Scholar
  15. 15.
    J. F. Fowler, The linear-quadratic formula and progress in fractionated radiotherapy, Br. J. Radiol. 62,679-694 (1989).PubMedCrossRefGoogle Scholar
  16. 16.
    H. D. Thames, S. M. Bentzen, I. Turesson, M. Overgaard, and W. Van den Bogaert, Time-dose factors in radiotherapy: a review of the human data, Radiother. Oncol. 19,219-235 (1990).PubMedCrossRefGoogle Scholar
  17. 17.
    T. Alper and P. Howard-Flanders, Role of oxygen in modifying the radiosensitivity of E. ColiB., Nature 178,978-979 (1956).Google Scholar
  18. 18.
    A. Yaromina, D. Zips, H. D. Thames, W. Eicheler, M. Krause, A. Rosner, M. Haase, C. Petersen, J. A.Raleigh, V. Quennet, S. Walenta, W. Mueller-Klieser, and M. Baumann, Pimonidazole labelling and response to fractionated irradiation of five human squamous cell carcinoma (hSCC) lines in nude mice:the need for a multivariate approach in biomarker studies, Radiother. Oncol. 81,122-129 (2006).Google Scholar
  19. 19.
    A. Brahme and A. K. Agren, Optimal dose distribution for eradication of heterogeneous tumours, Acta Oncol. 26,377-385 (1987).Google Scholar
  20. 20.
    G. Kåver, B. K. Lind, J. Löf, A. Liander, and A. Brahme, Stochastic optimization of intensity modulated radiotherapy to account for uncertainties in patient sensitivity, Phys. Med. Biol. 44,2955-2969 (1999).PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Iuliana Toma-Daşu
    • 1
  • Alexandru Daşuu
    • 2
  • Anders Brahmeu
    • 3
  1. 1.Department of Medical Radiation PhysicsStockholm University and Karolinska InstitutetSweden
  2. 2.Department of Radiation PhysicsNorrland University HospitalSweden
  3. 3.Department of Medical Radiation PhysicsKarolinska InstitutetSweden

Personalised recommendations