The interest in the induction of secondary tumours following radiotherapy has greatly increased as developments in detecting and treating the primary tumours have improved the life expectancy of cancer patients. However, most of the knowledge on the current levels of risk comes from patients treated many decades ago. As developments of irradiation techniques take place at a much faster pace than the progression of the carcinogenesis process, the earlier results could not be easily extrapolated to modern treatments. Indeed, the patterns of irradiation from historically-used orthovoltage radiotherapy and from contemporary techniques like conformal radiotherapy with megavoltage radiation, intensity modulated radiation therapy with photons or with particles are quite different. Furthermore, the increased interest in individualised treatment options raises the question of evaluating and ranking the different treatment plan options from the point of view of the risk for cancer induction, in parallel with the quantification of other long-term effects. It is therefore inevitable that models for risk assessment will have to be used to complement the knowledge from epidemiological studies and to make predictions for newer forms of treatment for which clinical evidence is not yet available. This work reviews the mathematical models that could be used to predict the risk of secondary cancers from radiotherapy-relevant dose levels, as well as the approaches and factors that have to be taken into account when including these models in the clinical evaluation process. These include the effects of heterogeneous irradiation, secondary particles production, imaging techniques, interpatient variability and other confounding factors.
London: Elsevier, 2017.