Comparison of 12 surrogates to characterize CT radiation risk across a clinical population

Abstract

Objectives: Quantifying radiation burden is essential for justification, optimization, and personalization of CT procedures and can be characterized by a variety of risk surrogates inducing different radiological risk reflections. This study compared how twelve such metrics can characterize risk across patient populations.

Methods: This study included 1394 CT examinations (abdominopelvic and chest). Organ doses were calculated using Monte Carlo methods. The following risk surrogates were considered: volume computed tomography dose index (CTDI_vol), dose-length product (DLP), size-specific dose estimate (SSDE), DLP-based effective dose (ED_k ), dose to a defining organ (OD_D), effective dose and risk index based on organ doses (ED_OD, RI), and risk index for a 20-year-old patient (RI_rp). The last three metrics were also calculated for a reference ICRP-110 model (OD_D,0, ED₀, and RI₀). Lastly, motivated by the ICRP, an adjusted-effective dose was calculated as [Formula: see text]. A linear regression was applied to assess each metric's dependency on RI. The results were characterized in terms of risk sensitivity index (RSI) and risk differentiability index (RDI).

Results: The analysis reported significant differences between the metrics with ED_r showing the best concordance with RI in terms of RSI and RDI. Across all metrics and protocols, RSI ranged between 0.37 (SSDE) and 1.29 (RI₀); RDI ranged between 0.39 (ED_k) and 0.01 (ED_r) cancers × 10³patients × 100 mGy.

Conclusion: Different risk surrogates lead to different population risk characterizations. ED_r exhibited a close characterization of population risk, also showing the best differentiability. Care should be exercised in drawing risk predictions from unrepresentative risk metrics applied to a population.

Key points: • Radiation risk characterization in CT populations is strongly affected by the surrogate used to describe it. • Different risk surrogates can lead to different characterization of population risk. • Healthcare professionals should exercise care in ascribing an implicit risk to factors that do not closely reflect risk.

Keywords: Clinical decision-making; Computed X-ray tomography; Ionizing radiation; Radiation exposure; Risk assessment.