Development of a mechanistic model for the prediction of Normal Tissue Complication Probability in cancer hadrontherapy

The goal of cancer radiation therapy, including ion therapy, consists of delivering a therapeutic radiation dose to the tumour volume while sparing healthy tissues, thus minimizing the probability of complications. Currently, the optimization of dose distribution and the evaluation of the treatment plan quality are performed by means of criteria based on the optimization of the distribution of absorbed dose, or Dose-Volume (DV) criteria. Although nowadays this is the most widespread method applied in patient treatment planning, it would be highly desirable to integrate it with biological indicators capable to directly predict the clinical outcomes. In this framework, the main goal of this thesis work was to develop an approach to accurately predict NTCP (Normal Tissue Complication Probability) curves following ion irradiation. A mechanistic model called Critical element model, which can predict NTCP for late tissue reactions, was considered as a starting basis. The model was applied to experimental data available in the literature on radiation damage in the rat spinal cord. Two out of the four model parameters only depend on the features of the irradiated tissue: therefore, as a first step of the work, the values of these parameters were calibrated on photon experimental data, and subsequently they were left unchanged for ion irradiation. The remaining two parameters, which depend on radiation quality, are the α and β coefficients characterizing the Linear-Quadratic (LQ) cell survival model, which is widely used to describe the cell survival probability as a function of radiation dose. To predict α and β values for ion irradiation, starting from those calibrated on photon data, we applied a biophysical model of cell killing called BIANCA, previously developed by our research group. This allowed generating a database of α and β coefficients as a function of ion type and Linear Energy Transfer (LET) by means of a purely predictive algorithm. The model was then tested against NTCP experimental data obtained by irradiating the rat spinal cord with protons, carbon ions and helium ions of different LET. Since all model parameters for ion irradiation were set a priori, the obtained NTCP simulation outcomes are pure predictions. Finally, a statistical analysis of such predictions was performed, showing good agreement with the experimental data for all ion irradiations. This approach may thus represent a significant improvement in patient treatment plan optimization and evaluation, allowing integration of DV criteria with predictions of clinical outcomes. The next steps of this work will consist of implementing analogous models for Tumour Control Probability (TCP) as well as for NTCP in the case of stochastic effects, typically secondary tumours.