Optimization of abdominal CT based on a model of total risk minimization by putting radiation risk in perspective with imaging benefit

Risk-versus-benefit optimization required a quantitative comparison of the two. The latter, directly related to effective diagnosis, can be associated to clinical risk. While many strategies have been developed to ascertain radiation risk, there has been a paucity of studies assessing clinical risk, thus limiting the optimization reach to achieve a minimum total risk to patients undergoing imaging examinations. In this study, we developed a mathematical framework for an imaging procedure total risk index considering both radiation and clinical risks based on specific tasks and investigated diseases. The proposed model characterized total risk as the sum of radiation and clinical risks defined as functions of radiation burden, disease prevalence, false-positive rate, expected life-expectancy loss for misdiagnosis, and radiologist interpretative performance (i.e., AUC). The proposed total risk model was applied to a population of one million cases simulating a liver cancer scenario. For all demographics, the clinical risk outweighs radiation risk by at least 400%. The optimization application indicates that optimizing typical abdominal CT exams should involve a radiation dose increase in over 90% of the cases, with the highest risk optimization potential in Asian population (24% total risk reduction; 306% $${{CTDI}}_{{vol}}$$ increase) and lowest in Hispanic population (5% total risk reduction; 89% $${{CTDI}}_{{vol}}$$ increase). Framing risk-to-benefit assessment as a risk-versus-risk question, calculating both clinical and radiation risk using comparable units, allows a quantitative optimization of total risks in CT. The results highlight the dominance of clinical risk at typical CT examination dose levels, and that exaggerated dose reductions can even harm patients. The proper practice of radiology (using imaging technology to diagnose and treat diseases) should take into consideration both the risk and benefit to a patient. Such a comparison can be hard because risk and benefit are measured in different ways. The risk includes some amount of radiation exposure to patients which can cause harm, but the benefit could be identifying a medical problem that needs attention. To overcome this obstacle, we developed a mathematical model describing the risk-to-benefit of a medical imaging study. Our modeling exercise found that the clinical benefit outweighs the radiation risk. The finding that benefit of detecting a problem is worth the risk of imaging is contrary to common belief. This study shows that so much emphasis could be put on radiation safety in imaging that avoiding imaging could negatively impact patients’ path of care. Ria et al. develop a mathematical framework for estimating total risk of an imaging procedure that accounts for both radiation and clinical risks. The authors propose a model that accounts for a variety of factors including disease prevalence, false positive rate, and expected life-expectancy loss.