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

A Correction to this article was published on 31 March 2021

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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.


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 (CTDIvol), dose-length product (DLP), size-specific dose estimate (SSDE), DLP-based effective dose (EDk ), dose to a defining organ (ODD), effective dose and risk index based on organ doses (EDOD, RI), and risk index for a 20-year-old patient (RIrp). The last three metrics were also calculated for a reference ICRP-110 model (ODD,0, ED0, and RI0). Lastly, motivated by the ICRP, an adjusted-effective dose was calculated as \( E{D}_r=\frac{RI}{R{I}_{rp}}\times E{D}_{OD} \). 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).


The analysis reported significant differences between the metrics with EDr 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 (EDk) and 0.01 (EDr) cancers × 103patients × 100 mGy.


Different risk surrogates lead to different population risk characterizations. EDr 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.

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Fig. 1
Fig. 2

Change history


CTDIvol :

Volume computed tomography dose index


Dose length product

ED0 :

Organ dose–based effective dose from reference phantom

EDk :

DLP-based effective dose


Organ dose–based effective dose

EDr :

Relative effective dose


Institutional review board


Patient-specific organ dose


Defining organ dose

ODO,0 :

Defining organ dose from reference phantom


Risk differentiability index


Risk index

RI0 :

Risk index from reference phantom

RIrp :

Risk index for a reference patient


Risk Sensitivity index


Size-specific dose estimate


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Correspondence to Francesco Ria.

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The scientific guarantor of this publication is Dr. Ehsan Samei.

Conflict of interest

E.S. discloses relationship with the following entities unrelated to the present publication: GE, Siemens, Bracco, Imalogix, 12Sigma, SunNuclear, Metis Health Analytics, Cambridge University Press, and Wiley and Sons. The remaining authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

Francesco Ria, Wanyi Fu, and Jocelyn Hoye have significant statistical expertise.

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Written informed consent was waived by the Institutional Review Board.

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The original online version of this article was revised: The affiliations of Wanyi Fu, Jocelyn Hoye, W. Paul Segars and Anuj J. Kapadia were incorrect.

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Ria, F., Fu, W., Hoye, J. et al. Comparison of 12 surrogates to characterize CT radiation risk across a clinical population. Eur Radiol 31, 7022–7030 (2021).

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  • Computed X-ray tomography
  • Radiation exposure
  • Ionizing radiation
  • Risk assessment
  • Clinical decision-making