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

  • Physics
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A Correction to this article was published on 31 March 2021

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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 (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).

Results

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.

Conclusion

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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Abbreviations

CTDIvol :

Volume computed tomography dose index

DL:

Dose length product

ED0 :

Organ dose–based effective dose from reference phantom

EDk :

DLP-based effective dose

EDOD :

Organ dose–based effective dose

EDr :

Relative effective dose

IRB:

Institutional review board

OD:

Patient-specific organ dose

ODD :

Defining organ dose

ODO,0 :

Defining organ dose from reference phantom

RDI:

Risk differentiability index

RI:

Risk index

RI0 :

Risk index from reference phantom

RIrp :

Risk index for a reference patient

RSI:

Risk Sensitivity index

SSDE:

Size-specific dose estimate

References

  1. NCRP (2019) National Council on Radiation Protection and Measurements. Medical Radiation Exposure of Patients in the United States (2019). NCRP Report no. 184

  2. European Commission (2014) Radiation Protection N° 180 Medical Radiation Exposure of the European Population

  3. (2007) The 2007 Recommendations of the International Commission on Radiological Protection. ICRP Publication 103. Ann ICRP 37:9–34. https://doi.org/10.1016/j.icrp.2007.10.003

  4. Mayo-Smith WW (2016) Image Wisely - International Safety Initiatives. Protocol Design. https://www.imagewisely.org/-/media/Image-Wisely/Files/CT/IW-Mayo-Smith-Protocol-Design.pdf

  5. Samei E, Järvinen H, Kortesniemi M et al (2018) Medical imaging dose optimisation from ground up: expert opinion of an international summit. J Radiol Prot 38:967–989. https://doi.org/10.1088/1361-6498/aac575

    Article  PubMed  Google Scholar 

  6. Boone J, Strauss K, Cody D, et al (2011) AAPM report n. 204. Size-specific dose estimates (SSDE) in pediatric and adult body ct examinations, the Report of AAPM Task Group 204. College Park, MD

  7. McCollough C, Bakalyar DM, Bostani M, et al (2014) AAPM report n. 220. Use of water equivalent diameter for calculating patient size and size-specific dose estimates (SSDE) in CT: the report of AAPM Task Group 220. College Park, MD

  8. McCollough C, Cody D, Edyvean S, et al (2008) AAPM report n.96. The Measurement, Reporting, and Management of Radiation Dose in CT, The Report of AAPM Task Group 23. College Park, MD

  9. Valentin J (2007) Managing patient dose in multi-detector computed tomography (MDCT). ICRP Publication 102. Ann ICRP 37. https://doi.org/10.1016/j.icrp.2007.09.001

  10. (2006) Health risks from exposure to low levels of ionizing radiation: BEIR VII phase 2. National Academies Press, Washington, D.C.

  11. Fu W, Ria F, Segars WP et al (2021) Patient-informed organ dose estimation in clinical ct: implementation and effective dose assessment in 1048 clinical patients. AJR Am J Roentgenol 216:1–11

  12. Tian X, Segars WP, Dixon RL, Samei E (2016) Convolution-based estimation of organ dose in tube current modulated CT. Phys Med Biol 61:3935–3954. https://doi.org/10.1088/0031-9155/61/10/3935

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Hoye J, Sharma S, Zhang Y et al (2019) Organ doses from CT localizer radiographs: development, validation, and application of a Monte Carlo estimation technique. Med Phys 46:5262–5272. https://doi.org/10.1002/mp.13781

    Article  PubMed  Google Scholar 

  14. Samei E, Ria F, Tian X, Segars PW (2020) A database of 40 patient-based computational models for benchmarking organ dose estimates in CT. Med Phys. https://doi.org/10.1002/mp.14373

  15. Li X, Samei E, Segars WP et al (2011) Patient-specific radiation dose and cancer risk estimation in CT: part II. Application to patients. Med Phys 38:408–419

    Article  Google Scholar 

  16. Zhang Y, Li X, Paul Segars W, Samei E (2012) Organ doses, effective doses, and risk indices in adult CT: comparison of four types of reference phantoms across different examination protocols. Med Phys 39:3404–3423. https://doi.org/10.1118/1.4718710

    Article  PubMed  PubMed Central  Google Scholar 

  17. ICRP (2009) Adult reference computational phantoms. ICRP Publication 110. Ann ICRP 39:21–45. https://doi.org/10.1016/j.icrp.2009.07.004

    Article  Google Scholar 

  18. ICRP The use of effective dose as a risk related radiological protection quantity. A Task Group under Committee 2. http://www.icrp.org/icrp_group.asp?id=54

  19. Samei E, Tian X, Paul Segars W, Frush DP (2017) Radiation risk index for pediatric CT: a patient-derived metric. Pediatr Radiol 47:1737–1744. https://doi.org/10.1007/s00247-017-3973-z

    Article  PubMed  Google Scholar 

  20. Vañó E, Miller DL, Martin CJ et al (2017) ICRP publication 135: diagnostic reference levels in medical imaging. Ann ICRP 46:1–144. https://doi.org/10.1177/0146645317717209

    Article  PubMed  Google Scholar 

  21. Kanal KM, Butler PF, Sengupta D et al (2017) U.S. Diagnostic reference levels and achievable doses for 10 adult ct examinations. Radiology 284:120–133. https://doi.org/10.1148/radiol.2017161911

    Article  PubMed  Google Scholar 

  22. Ria F, Davis JT, Solomon JB et al (2019) Expanding the concept of diagnostic reference levels to noise and dose reference levels in CT. AJR Am J Roentgenol 213:889–894. https://doi.org/10.2214/AJR.18.21030

    Article  PubMed  Google Scholar 

  23. Ria F, Wilson JM, Zhang Y, Samei E (2017) Image noise and dose performance across a clinical population: patient size adaptation as a metric of CT performance. Med Phys 44:2141–2147. https://doi.org/10.1002/mp.12172

    Article  PubMed  Google Scholar 

  24. Ria F, Solomon JB, Wilson JM, Samei E (2020) Technical note: validation of TG 233 phantom methodology to characterize noise and dose in patient CT data. Med Phys 47:1633–1639. https://doi.org/10.1002/mp.14089

    Article  PubMed  Google Scholar 

  25. McCollough CH (2003) Patient dose in cardiac computed tomography. Herz 28:1–6. https://doi.org/10.1007/s00059-003-2447-2

    Article  PubMed  Google Scholar 

  26. Abadi E, Sanders J, Samei E (2017) Patient-specific quantification of image quality: an automated technique for measuring the distribution of organ Hounsfield units in clinical chest CT images. Med Phys 44:4736–4746. https://doi.org/10.1002/mp.12438

    Article  PubMed  Google Scholar 

  27. Sanders J, Hurwitz L, Samei E (2016) Patient-specific quantification of image quality: an automated method for measuring spatial resolution in clinical CT images. Med Phys 43:5330–5338. https://doi.org/10.1118/1.4961984

    Article  PubMed  Google Scholar 

  28. Christianson O, Winslow J, Frush DP, Samei E (2015) Automated technique to measure noise in clinical ct examinations. AJR Am J Roentgenol 205:W93–W99. https://doi.org/10.2214/AJR.14.13613

    Article  PubMed  Google Scholar 

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Author notes

  1. Francesco Ria and Wanyi Fu contributed equally to this work.

Authors and Affiliations

  1. Carl E. Ravin Advanced Imaging Labs, Duke University Health System, 2424 Erwin Road, Suite 302, Durham, NC, 27710, USA

    Francesco Ria, Wanyi Fu, Jocelyn Hoye, W. Paul Segars, Anuj J. Kapadia & Ehsan Samei

  2. Clinical Imaging Physics Group, Duke University Health System, 2424 Erwin Road, Suite 302, Durham, NC, 27710, USA

    Francesco Ria & Ehsan Samei

  3. Medical Physics Graduate Program, Departments of Radiology, Physics, Biomedical Engineering, and Electrical and Computer Engineering, Duke University, 2424 Erwin Road, Suite 302, Durham, NC, 27710, USA

    Ehsan Samei

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  1. Francesco Ria
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Correspondence to Francesco Ria.

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Guarantor

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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Institutional Review Board approval was obtained.

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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). https://doi.org/10.1007/s00330-021-07753-9

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  • DOI: https://doi.org/10.1007/s00330-021-07753-9

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