Room: Exhibit Hall
Purpose: Recent advances in immunoradiotherapy is important issue in biological modelling of radiotherapy. Applying a radiobiological model such as tumor control probability(TCP) and normal tissue complication probability(NTCP) for radiotherapy is one of the method to rank several treatment plans. The aim of this study was to assess the model with immunological aspects including such stochastic distribution as intercellular uncertainties, repopulation, repair, resistance.
Methods: For biological evaluation in a treatment planning, we used clinical 3D radiotherapy treatment planning system with biological function(Eclipse ver.11.0, Varian medical systems, US). Biological parameters were set as any given values to calculate the TCP/NTCP. All beam data used in this study were commissioned as a 6 MV photon beam of Novalis-Tx (BrainLab, US) in clinical use. For a prostate cancer patient of VMAT plan, different dose fraction protocols were set. For intercellular uncertainty of tumor and normal tissues(bladder, rectum), the stochastic biological model was applied.
Results: As respect to the difference of the Î±/Î², the changes of the TCP/NTCP between fraction schedules were increased in hypo-fraction regimens. The elongation of repair half-time(long) increased the TCP/NTCP twice or much higher in the case of hypo-fraction scheme. For tumor, it is suggested that repopulation parameters such as Tpot and Tstart, T1/2,long which are immunologically working to the tumor and normal tissue, would improve TCP/NTCP. However, the NTCP was increased in 6 fractions than 5 fractions in a week with conventional fraction and moderately hypo-fraction protocols.
Conclusion: We have shown the variety and difference of the various biological parameters including intercellular uncertainties, repopulation, repair, resistance. The possibility of an improvement of radiosensitivity in tumor or the ability of repair in normal tissue by immunological aspects were expected. For more precise prediction, treatment planning systems should be incorporated the complicated biological optimization in clinical practice.
Funding Support, Disclosures, and Conflict of Interest: This work was supported by JSPS KAKENHI Grant Number JP15K09998.