Asset Details
Do you wish to reserve the book?
Comparison of 12 surrogates to characterize CT radiation risk across a clinical population
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Comparison of 12 surrogates to characterize CT radiation risk across a clinical population
Do you wish to request the book?
Comparison of 12 surrogates to characterize CT radiation risk across a clinical population
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Comparison of 12 surrogates to characterize CT radiation risk across a clinical population
2021
Overview
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
, ED
0
, and RI
0
). Lastly, motivated by the ICRP, an adjusted-effective dose was calculated as
E
D
r
=
RI
R
I
rp
×
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).
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 (RI
0
); RDI ranged between 0.39 (ED
k
) and 0.01 (ED
r
) cancers × 10
3
patients × 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.
