Clinical Decision Making in CT: Risk Assessment Comparison Across 12 Risk Metrics in Patient Populations

Purpose The Medical Physics 3.0 initiative aims to enhance direct physicist involvement in clinical decision making to improve patient care. In this involvement, it is crucial to achieve effective and patient-specific radiation risk assessment. CT risk characterization presents a variety of metrics, many of which used as radiation risk surrogates; some are related to the device output (CTDI), whereas others include patient organ risk-, age-, and gender-factors (Effective Dose, Risk Index). It is unclear how different metrics can accurately reflect the radiological risk. This study compared how twelve metrics characterize risk across CT patient populations to inform effective clinical decision making in radiology. Methods This IRB-approved study included 1394 adult CT examinations (abdominopelvic and chest). Organ doses were calculated using Monte Carlo methods. The following risk surrogate metrics were calculated: CTDIvol, DLP, SSDE, DLP-based Effective Dose (EDk), organ-dose-based ED (EDOD), dose to defining organ (stomach- and lungs-ODD), organ-dose-based Risk Index (RI), and 20 y.o. patient Risk Index (RIr). Furthermore, ODD,0, ED0, and RI0 were calculated for a reference patient (ICRP 110). Lastly, an adjusted ED (ED') was computed as the product of RI/RIr and EDOD. A linear regression was applied to assess each metric’s dependency to RI, assumed to be the closest patient risk surrogate. The normalized-slope (nS) and a Minimum Risk Detectability Index (MRDI=RMSE/slope) were calculated for each fit. Results The analysis reported significant differences between the metrics. ED’ showed the best concordance with RI in terms of nS and MRDI. Across all metrics and protocols, nS ranged between 0.37(SSDE) to 1.29(RI0); MRDI ranged between 39.11(EDk) to 1.10(ED’) cancers per 105 patients per 0.1Gy. Conclusion Radiation risk characterization in CT populations is strongly affected by the index used to describe it. When involved in clinical decisions, medical physicists should exercise care in ascribing an implicit risk to factors that do not closely reflect risk.