Abstract
Intra-tumor variability of oxygenation and clonogenic cell density causes tumor non-uniform spatial response to radiation. Strategies like dose redistribution/boosting, whose impact should be quantified in terms of tumor control probability (TCP), have been proposed to improve treatment outcome. In 1999, Sánchez-Nieto et al. developed a tool to evaluate the impact of dose distribution inhomogeneities, compared to a reference homogeneous dose distribution, in terms of TCP. DVH data were used to calculate the so-called ∆TCP, defined as the difference in TCP arising from dose variations in individual DVH-bins. In this work, we develop an open source tool to calculate volumetric ∆TCP and evaluate the impact on TCP of: (i) Spatial dose distribution variations with respect to a reference dose; (ii) Spatial radiosensitivity variations with respect to a reference radiosensitivity; (iii) Simultaneous variation in dose distribution and radiosensitivity. ∆TCP calculations can be evaluated voxel-by-voxel, or in a user defined subvolume basis. The tool capabilities are shown with 2 examples of H&N RT treatments and subvolume contours data providing information about tumor oxygenation status. ΔTCP values are computed for a homogeneous dose to a well oxygenated tumor volume (with a homogeneous 5% vascular fraction), as reference condition, with respect to the same dose now considering 3 oxygenation levels and 3 cell density values (104, 106 and 107 cells/mm3, respectively). ΔTCP values are also computed for the comparison of a homogenous dose distribution vs a redistributed dose distribution delivered to the non-homogeneous tumor.
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References
Bentzen, SM. Theragnostic imaging for radiation oncology: Dose-painting by numbers. Lancet Oncol. 6, 112–117 (2005).
Lee, N. Y., and Le, Q-T.: New developments in radiation therapy for head and neck cancer: Intensity modulated radiation therapy and hypoxia targeting. Seminars in Oncology 35(3): 236–250 (2008).
Okunieff, P., de Bie, J., Dunphy, EP., Terris, DJ., Höckel, M.: Oxygen distributions partly explain the radiation response of human squamous cell carcinomas. British Journal of Cancer. 74,185–190 (1996).
Briztel, DM. Sibley, GS. Prosnitz, LR. Scher, RL. and Dewhirst, MW.: Tumor hypoxia adversely affects the prognosis of carcinoma of the head and neck. Int.J.Radiat.Oncol.Biol.Phys. 38, 285–289 (1997).
Brizel, DM.: Targeting the future in head and neck cancer. The lancet oncology;10, 204–205 (2009).
Nordsmark, M., Bentzen, SM., Rudat, V. et al: Prognosis value of tumor oxygenation in 397 head and neck tumors after primary radiation therapy. An international multicenter study. Radiother. Oncol.77,18–24 (2005).
Nutting, C M. Corbishley, C M. Sanchez-Nieto, B. Cosgrove, V P. Webb, S. Dearnaley, D P: Potential im-provements in the therapeutic ratio of prostate cancer irradiation: dose escalation of pathologically identified tumour nodules using intensity modulated radio-therapy. The British Journal of Radiology, 75, 151–161 (2002).
Sanchez-Nieto, B. Nahum, A. E.: The delta-TCP concept: a clinically useful measure of tumor control probability. Int. J. Radiation Oncology Biol. Phys., Vol. 44, No. 2, pp. 369–380, (1999).
Espinoza, I. Peschke, P. Karger, CP: A model to simulate the oxygen distribution in hypoxic tumors for different vascular architectures. Med Phys. 40(8):081703(2013).
Wouters B G and Brown J M: Cells at intermediate oxygen levels can be more important than the ‘hypoxic fraction’ in determining tumor response to fractionated radiotherapy. Radiat. Res.147, 541–50 (1997).
Michaelidou, A., et al.: PO-0609: 18F-FDG-PET in Guiding Dose-painting with IMRT in Oropharyngeal Tumours (FiGaRO)-Early Results. Radiother. and Oncol. 123 S318 (2017).
Mönnich, D. Troost, E. G. Kaanders, J. H. Oyen, W. J. Alber, M. Thorwarth, D: Modelling and simulation of [18F]fluoromisonidazole dynamics based on histology-derived microvessel maps. Phys. Med. Biol. 56, 2045–2057(2011).
Webb, S. Nahum, AE: A model for calculating tumour control probability in radiotherapy including the effects of inhomogeneous distributions of dose and clonogenic cell density. Phys Med Biol. Jun;38(6):653–66 (1993).
Del Monte, U. Does the cell number 109 still really fit one gram of tumor tissue? Cell Cycle 8(3):505–6 (2009).
Acknowledgements
D. F. and A. G-A. and B. S-N acknowledge the support of FONDECYT 2015 Postdoctoral Grant 3150422, FONDECYT 2017 Iniciación Grant 11170575 and Fondo de apoyo a la organización de reuniones científicas (VRI UC 2015). The authors thank the help of Christopher Thomas concerning the treatment planning and transfer of anonymized patient data between institutions.
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Fabri, D., Gago-Arias, A., Guerrero-Urbano, T., Lopez-Medina, A., Sanchez-Nieto, B. (2019). A Volumetric Delta TCP Tool to Quantify Treatment Outcome Effectiveness Based on Biological Parameters and Different Dose Distributions. In: Lhotska, L., Sukupova, L., Lacković, I., Ibbott, G. (eds) World Congress on Medical Physics and Biomedical Engineering 2018. IFMBE Proceedings, vol 68/3. Springer, Singapore. https://doi.org/10.1007/978-981-10-9023-3_124
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