Papers Published

1. Ria, F; Lerebours, R; Zhang, A; Erkanli, A; Abadi, E; SOLOMON, J; Marin, D; Samei, E, THE 2023 AAPM ANNUAL MEETING PROGRAM., Med Phys, vol. 50 no. 6 (June, 2023), pp. e62-3992 [doi] .
   (last updated on 2025/05/31)

   Abstract:
   Purpose Informed by a recent mathematical framework, we formulated an imaging strategy to balance interpretative performance-based clinical risk (i.e., false positive and false negative rates) and radiation risk as a risk-versus-risk assessment. The model was applied to a population of one million cases simulating a clinical liver cancer scenario. Moreover, a model was developed to predict individualized risk-versus-risk optimization. Methods The proposed model defined a Total Risk (TR) as the linear combination of radiation risk and clinical risk defined as functions of the radiation burden, the disease prevalence disease, the false positive rate, the expected life-expectancy loss for an incorrect diagnosis, and the radiologist interpretative performance (i.e., AUC). The mathematical framework was applied to a simulated dataset of 1,000,000 CT studies investigating localized stage liver cancer assuming a typical false positive rate of 5% and optimal imaging conditions (AUC=0.75). Demographic information was simulated according with literature and census data including male and female for different patient races (white, black, Asian, and Hispanic). Following BEIR-VII report, organ-specific radiation doses were used to calculate the radiation Risk Index per each patient. The model was then extended to predict the optimal scanner output associated with the TR for specific patients. Results Across all races and sexes, median radiation risk ranged between 0.008 and 0.012 number of deaths per 100 patients; median clinical risk ranged between 0.042 and 0.076; and medial total risk ranged between 0.010 and 0.088 deaths per 100 patients. The mathematical model was then generalized to estimate individualized optimal imaging condition minimizing TR. Conclusion A mathematical framework to describe total risk in CT was robustly tested in a simulated dataset of 1,000,000 CT studies. The results highlighted the dominance of clinical risk at typical CT examination dose levels. The generalization of the mathematical model allowed the prediction of individualized risk optimization.