A Framework for Uncertainty Propagation in Risk-to-Risk Assessment of Clinical and Radiation Risks in CT Across Patient Demographics

Abstract

Recent work has introduced a quantitative framework for evaluating the trade-off between radiation risk and clinical benefit in computed tomography (CT) imaging through a risk-to-risk model that defines total patient risk as the sum of radiation risk and clinical risk. While the model provides a novel method for evaluating justification and optimization of CT procedures, the uncertainties associated with its underlying variables have not previously been quantified. Without uncertainty estimation, interpretation of total risk calculations remains incomplete. The purpose of this study was to develop and implement a method for estimating uncertainties associated with the total risk model. Uncertainty propagation was first applied to parameters with previously reported variability. This initial approach produced relatively small uncertainties in radiation risk estimates. Because this result did not adequately reflect the known uncertainty in the stochastic relationship between radiation dose and induced cancer, a second uncertainty framework was developed based on additional uncertainties published in the BEIR VII report. Risk and uncertainty calculations were performed for a simulated population of 100,000 digital twins reflecting United States demographic distributions stratified by age, sex, and race. The initial propagation scheme yielded relative uncertainties of approximately 7% for radiation risk and 8% for clinical risk. Incorporation of epidemiologically derived variability substantially increased radiation risk uncertainty to approximately 34–35%. These results demonstrate that uncertainty associated with radiation risk estimates is substantially larger than that captured by Monte Carlo dose modeling alone. Incorporating epidemiological uncertainty provides a more realistic representation of the variability inherent in radiation risk estimation. This work establishes a methodological framework for uncertainty quantification within the total risk model and provides a necessary step toward evaluating the robustness and clinical applicability of risk-to-risk assessment in CT imaging.