A critical analysis of Shuryak’s Predictive Radiocarcinogenesis Model
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Date
2024
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Abstract
Objective: To critically analyze, simplify and implement a predictive radiocarcinogenesis model to estimate the risk of secondary cancer after RT which can effectively compare different radiotherapy treatment plans with the aim of having an additional element of information during the decision-making process for the best RT plan.Methodology: A Python software was developed that was able to implement the model proposed by Shuryak et al. (2009). Simplifications and minor corrections were made which allowed for more compactness and more efficient run times. The model was then reparametrized with newer data from the Surveillance, Epidemiology, and End Results Program (SEER) database and several epidemiological studies using Bayesian Inference. Uncertainty propagation studies were then conducted to understand their propagation better. Finally, the model with its new parameters was applied to a selection of prostate plans to determine if it could construct a risk hierarchy.Results: The model was successfully reparametrized with newer data. Although some parameters show significant deviation from Shuryak’s original parameters, they are mostly on the same order of magnitude, and the differences arise likely due to differences in fitted data and the fitting process itself. Shuryak’s model successfully built a risk hierarchy between prostate plans, although it deviated from the more simplistic linear non-threshold BEIR VII model. It was also possible to simplify some complex mathematical equations, both in general and for particular cases, allowing for easier implementation and more efficient run times.Conclusions: Shuryak’s model was successfully reparametrized and implemented, showing potential to become clinically applicable. However, more comparisons between the model’s result and epidemiological data must be made to evaluate its accuracy better, and more concise and complete second primary cancer studies must be used before the model is reliable enough for clinical decision-making.
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Tesis (Master’s in Medical Physics)--Pontificia Universidad Católica de Chile, 2024.