Download PDF
Methods Inf Med 2007; 46(03): 367-375
DOI: 10.1160/ME0312
paper
Schattauer GmbH

A Patient-specific in vivo Tumor and Normal Tissue Model for Prediction of the Response to Radiotherapy

A Computer Simulation Approach
V. P. Antipas
1   In Silico Oncology Group, Microwave and Fibre Optics Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
,
G. S. Stamatakos
1   In Silico Oncology Group, Microwave and Fibre Optics Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
,
N. K. Uzunoglu
1   In Silico Oncology Group, Microwave and Fibre Optics Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
› Author Affiliations
Further Information

Publication History

Publication Date:
20 January 2018 (online)

    Permissions and Reprints

Summary

Objectives: Integration of multiscale experimental cancer biology through the development of computer simulation models seems to be a necessary step towards the better understanding of cancer and patient-individualized treatment optimization. The integration of a four-dimensional patient-specific model of in vivo tumor response to radiotherapy developed by our group with a model of slowly responding normal tissue based on W. Duechting’s approach is presented in this paper. The case of glioblastoma multiforme and its surrounding neural tissue is addressed as a modeling paradigm.

Methods: A cubic discretizing mesh is superimposed upon the anatomic region of interest as is reconstructed from pertinent imaging (e.g. MRI) data. On each geometrical cell of the mesh the most crucial biological “laws” e.g. metabolism, cell cycling, tumor geometry changes, cell kill following irradiation etc. are applied. Slowly responding normal neural tissue is modeled by a functional compartment containing indivisible cells and a divisible compartment containing glial cells.

Results: The model code has been executed for a simulated period normally covering the radiotherapy course duration and extending a few days after its completion. The following schemes have been simulated: standard fractionation, hyperfractionation, accelerated fractionation, accelerated hyperfractionation and hypofractionation. The predictions are in agreement with the outcome of the RTOG 83-02 phase I/II trial, the retrospective study conducted by Sugawara et al. and the theoretical predictions of Duechting et al.

Conclusions: The presented model, although oversimplified, may serve as a basis for a refined simulation of the biological mechanisms involved in tumor and normal tissue response to radiotherapy.

Keywords

In silico oncology - simulation models - integrative cancer biology - radiobiology - glioblastoma multiforme
 

  • References

  • 1 Jones B, Dale RG. Mathematical models of tumor and normal tissue response. Acta Oncol 1999; 38 (07) 883-983.
    CrossrefPubMedSearch in Google Scholar
  • 2 Kansal AR. et al. Simulated brain tumor growth dynamics using a three-dimensional cellular automaton. J Theor Biol 2000; 203: 367-82.
    CrossrefPubMedSearch in Google Scholar
  • 3 Kansal AR. et al. Cellular automaton of idealized brain tumor growth dynamics. Bio Systems 2000; 55: 119-27.
    CrossrefPubMedSearch in Google Scholar
  • 4 Stamatakos G. et al. In vivo tumor growth and response to radiation therapy: a novel algorithmic description. Int J Radiat Oncol Biol Phys 2001; 51 (01) 240.
    PubMedSearch in Google Scholar
  • 5 Stamatakos GS. et al. In Silico Radiation Oncology: Combining Novel Simulation Algorithms with Current Visualization Techniques. Proceedings of the IEEE. 2002 90: (11). Invited paper.
    PubMedSearch in Google Scholar
  • 6 Antipas VP. et al. A spatio-temporal simulation model of the response of solid tumours to radiotherapy in vivo: parametric validation concerning oxygen enhancement ratio and cell cycle duration. Phys Med Biol 2004; 49: 1485-1504.
    CrossrefPubMedSearch in Google Scholar
  • 7 Dionysiou DD. et al. A four dimensional in vivo model of tumour response to radiotherapy: parametric validation considering radiosensitivity genetic profile and fractionation. J Theor Biol 2004; 230: 1-20.
    CrossrefPubMedSearch in Google Scholar
  • 8 Duechting W. et al. Radiogenic responses of normal cells induced by fractionated irradiation – a simulation study. Strahlentherap Onkol 1995; 171 (08) 460-7.
    PubMedSearch in Google Scholar
  • 9 Duechting W, Ginsberg T, Ulmer W. Modeling of radiogenic responses induced by fractionated irradiation in malignant and normal tissue. Stem Cells 1995; 13 S1 301-6.
    CrossrefPubMedSearch in Google Scholar
  • 10 Douglas BG, Fowler JF. The effect of multiple small doses of x-rays on skin reactions in the mouse and a basic interpretation. Radiat Res 1976; 66: 401-26.
    CrossrefPubMedSearch in Google Scholar
  • 11 Van Geijn J. et al. Extraction of average values of tumor parameters from clinical local tumor probabilities data. Baier K, Baltas D. Modeling in clinical radiobiology. Chapter 20, Monography 2.. Freiburg: Freiburg Oncology Series; 1997: 217-28.
    Search in Google Scholar
  • 12 Swanson KR. et al. Virtual and real brain tumors: using mathematical modeling to quantify glioma growth and invasion. J Neurol Sciences 2003; 216: 1-10.
    CrossrefPubMedSearch in Google Scholar
  • 13 Swanson K. et al. Dynamics of amodel for brain tumors reveals a small window for the therapeutic intervention. Discrete and Continuous Dynamical Systems – Series B, 2004; 04 (01) 289-95.
    PubMedSearch in Google Scholar
  • 14 Rahnenfuehrer J. Clustering algorithms and other exploratory methods for microarray data analysis. Methods Inf Med 2005; 44: 444-8.
    Thieme ConnectPubMedSearch in Google Scholar
  • 15 Brors B. Microarray annotation and biological information on function. Methods Inf Med 2005; 44: 468-72.
    Thieme ConnectPubMedSearch in Google Scholar
  • 16 Haney SM. et al. Tracking tumor growth rates in patients with malignant gliomas: a test of two algorithms. Am J Neuroradiol 2001; 22 (01) 73-82.
    PubMedSearch in Google Scholar
  • 17 Prastawa et al. Automatic brain segmentation by subject specific modification of atlas priors. Acad Radiol 2003; 10 (12) 1341-8.
    CrossrefPubMedSearch in Google Scholar
  • 18 Leuba G, Garey L J. Comparison of neuronal and glial numerical density in primary and secondary visual cortex of man. Exp Brain Res 1989; 77 (01) 31-8.
    PubMedSearch in Google Scholar
  • 19 Antipas V. et al. A theoretical investigation into the role of tumour radiosensitivity clonogen repopulation, tumour shrinkage and radionuclide RBE in permanent brachytherapy implants of 125-I and 103-Pd. Phys Med Biol 2001; 46: 2557-69.
    CrossrefPubMedSearch in Google Scholar
  • 20 Webb S. Optimum parameters in amodel for tumour-control probability including interpatient heterogeneity. Phys Med Biol 1994; 39: 1895-914.
    CrossrefPubMedSearch in Google Scholar
  • 21 Nahum A, Sanchez-Nieto B. Tumor control probability modeling: basic principles and applications in treatment planning. Phys Med 2001; 17 (xvii) (Suppl. 02) 13-23.
    PubMedSearch in Google Scholar
  • 22 Perez C. et al. Overview. Widhers H. McBrie Biologic basis of cancer. Perez C, Brady L. Principles and Practice of Radiation Oncology. Philadelphia: Lippincott-Raven; 1998: 10,37-38,87.
    Search in Google Scholar
  • 23 Steel GG. Introduction: The significance of radiobiology for radiotherapy Steel GG. Cell survival as a determinant of tumor response. Baumann M, et al. Modified fractionation. Horsman MR, Overgaard J. The oxygen effect and tumor microenvironment. Steel GG. The radiobiology of tumors. Steel GG. Basic Clinical Radiobiology. 3. London UK: Arnold; 2002: 13 52-70, 147-157, 159-160,165-166,182-91.
    Search in Google Scholar
  • 24 Kocher M, Treuer H. Reoxygenation of hypoxic cells by tumor shrinkage during irradiation. A computer simulation. Strahlentherapie und Onkologie 1995; 171: 219-30.
    PubMedSearch in Google Scholar
  • 25 Kocher M. et al. Computer simulation of cytoxic and vascular effects of radiosurgery in solid and necrotic brain metastasis. Radiother Oncol 2000; 54: 149-56.
    CrossrefPubMedSearch in Google Scholar
  • 26 Langleben DD, Segall GM. PET in differentiation of recurrent brain tumor from radiation injury. J Nuclear Medicine 2000; 41: 1861-7.
    PubMedSearch in Google Scholar
  • 27 Werner-Wasik M. et al. Final report of a phase I/II trial of hyperfractionated and accelerated hyper-fractionated radiation therapy with carmustine for adults with supratentorial malignant gliomas. Radiation Therapy Oncology Group Study 83-02. Cancer 1996; 77 (08) 1535-43.
    CrossrefPubMedSearch in Google Scholar
  • 28 Sugawara T. et al. Accelerated hyperfractionationin the treatment of malignant glioma. Nippon Igaku Hoshasen Gakkai Zasshi 1994; 54 (04) 278-85.
    PubMedSearch in Google Scholar
  • 29 Beck-Bornhold HP. et al. Hyperfractionation: where do we stand?. Radiother Oncol 1997; 43: 1-21.
    CrossrefPubMedSearch in Google Scholar
  • 30 Baumann M. et al. Hyperfractionated radiotherapy in head and neck cancer: a second look at the clinical data. Radiother Oncol 1998; 46: 127-30.
    CrossrefPubMedSearch in Google Scholar
  • 31 Bentzen SM, Thames HD. Clinical evidence for tumor clonogen regeneration: interpretations of the data. Radiother Oncol 1999; 22: 161-6.
    PubMedSearch in Google Scholar