1. Introduction

1. Introduction

Computed tomography (CT) has come a long way since its introduction in 1972 and it has revolutionized the diagnostic radiology [1]. CT is a noninvasive imaging modality that creates cross-sectional and three-dimensional (3D) images of the internal anatomical structures of the body, leading to improved diagnosis, and in turn, saving many lives ^[2][3][2, 3]. There has been an exponential increase in the number of CT examinations in the last two decades. In 2019, more than 90 million CT scans were performed in the United States [4], up from 85 million in 2011 [5], 62 million in 2007 [6] and 57 million in 2000 [7]. CT is the major source of radiation exposure to the general population from medical imaging, which is evident from the fact that while CT represents only ~6.3% of all diagnostic radiologic procedures, it contributes to ~43.2% of the collective radiation dose given to the patients [8]. This has become a matter of growing concern as these harmful ionizing radiations can lead to DNA damage, mutagenesis and carcinogenesis in the exposed individuals [9].

Some epidemiological studies have shown a small yet significant increase in cancer risk at typical CT doses ^{[10][11][12][13][14][15]}[10-15]. One report estimated that 0.9% of cancer cases in the United States could be related to low-dose diagnostic X-rays performed between 1991–1996 [16]. Given the increasing use of CT, Brenner and Ha`ll translated these figures to 1.5–2% of the 2007 United States cancer cases [6]. Since the novel coronavirus disease 2019 (COVID-19) pandemic, the role of chest CT has garnered increased attention for screening, diagnosis and management of patients with suspected or known COVID-19, as well as for monitoring the disease progress and its complications ^[17][18][17, 18]. To date, more than 616 million cases of COVID-19 have been identified worldwide ^[19][20][19, 20], many of whom were subjected to CT scanning [21] and some even underwent repeat CT examinations ranging between 2–8 scans ^{[21][22][23][24][25]}[21-25]. The dramatic increase in the number of CT scans in a short span of time has raised concerns about patient safety [21].

2. Health Effects of Radiation Exposure

2. Health Effects of Radiation Exposure

The health effects of ionizing radiation can be divided into stochastics and deterministic effects. Stochastic effects suggest that exposure to radiation, even at low doses, may cause damage to the genetic material in cells that can result in cancer induction or hereditary disease in the future [26]. These are not seen immediately, but over a lifetime, and sometimes manifest several decades after the exposure. Stochastic effects are unpredictable, random events in nature with no specific threshold [27]. The probability of stochastic effects, rather than its severity, is assumed to increase linearly with the increasing dose [28, 29]. Prevention of stochastic effects is not possible in practice, though dose limits are established to reduce their chance of occurrence [26].

Deterministic effects, on the other hand, are seen when patients are exposed to high doses of radiation over a short span of time [27]. These have a threshold dose, below which they do not occur; however, once the threshold is exceeded, the severity of the outcome increases [28]. Skin erythema, cataract, hair loss and burns are examples of such effects [8, 27, 28]. However, these effects are seldom seen with low-dose diagnostic imaging modalities such as CT, except for a few sporadic incidences of gross medical error [30].

The general population is at some risk for cancer and associated mortality during their lifetime, even without being exposed to medical radiation. This risk is called the lifetime baseline risk (LBR) for cancer. In the United States, the sex-averaged LBR of cancer incidence and mortality (including solid cancers and leukemia) is about 42% and 20%, respectively [8]. According to the American Cancer Society, based on 2016–-2018 data, the average lifetime risk of developing cancer from other causes stands at 40.14 and 38.7% in men and women, respectively [31]. The additional cancer risk above and beyond LBR due to radiation exposure is called the lifetime attributable risk (LAR) ^[32][33][32, 33]. Table 1Tables 1 and Table 22 represent qualitative approaches to communicate the LAR of cancer incidence and mortality compared to LBR [8].

Table 1. A qualitative approach to communicate different levels of cancer incidence associated with radiation exposure compared with the lifetime baseline risk of cancer incidence.

LAR: Lifetime attributable risk; LBR: lifetime baseline risk. ^a: Sex-averaged lifetime attributable risk of cancer incidence in general population; ^b: probability of cancer incidence in general population. Adopted with permission from Ref. [8]. 2019, World health organizations.

Table 2. A qualitative approach to communicate different levels of cancer mortality associated with radiation exposure compared with the lifetime baseline risk of cancer mortality.

LAR: lifetime attributable risk; LBR: lifetime baseline risk. ^a: Sex-averaged lifetime attributable risk of fatal cancer in the general population; ^b: probability of fatal cancer in the general population. Adopted with permission from Ref. [8]. 2019, World health organizations.

The LAR is calculated using risk estimation models derived from epidemiological studies, mainly Japanese atomic bomb survivors, taking into account a conservative assumption that there is a ‘linear-no-threshold’ (LNT) relationship between radiation exposure and cancer risk at all dose levels, even near zero [8, 27, 28, 34]. The foundation of the LNT model of dose–response is based on statistical extrapolation of the risks at high-dose (where the risks are observable with epidemiological evidence) to low-dose radiation (where the risks are not observable) [32, 35]. The LNT postulates that (i) a single ionization at any dose, however small it may be, has the potential to initiate complex processes that can cause stochastic health effect; (ii) the effects increase linearly with the increase in radiation dose; and (iii) these effects are cumulative over lifetime, and the sum of several small exposures carries the same potential to produce these effects as a single large exposure of equal dose value [36].

However, various authors and professional organizations, including the Health Physics Society [37], United Nations Scientific Committee on the Effects of Atomic Radiation [38], United States Nuclear Regulatory Commission [39] and American Nuclear Society [40], have challenged and debunked LNT theory, considering it only a mathematical formula that calculates the theoretical and hypothetical risk.

Many other studies have also deprecated the fundamental assumption and historical foundation of the LNT model, especially for low-dose radiation, as LNT theory ignores the body’s natural ability to repair damaged DNA and elimination of aberrant cells [41, 42]. Moreover, it has also been contested that most of the studies supporting the LNT theory lack merit, as they are not evidence-based and ignore radiobiology [43].

The existence of three other dose–response models (hypersensitivity, threshold and hormetic) for estimating the carcinogenic risks of radiation makes things even more complicated. The hypersensitivity model suggests a greater risk than those from the LNT model at low-dose radiation [44]. The ‘threshold’ model assumes that there exists a latency threshold below which small exposures of radiation are harmless [43], and the ‘hormetic’ model suggests that low-dose radiation, on the contrary, may help to prevent rather than cause cancer, by stimulating the body’s natural anticancer mechanisms that are otherwise not activated in the absence of radiation [42, 45]. Stimulation of such adaptive processes not only helps in the repair/elimination of the cells affected by radiogenic damage, but also of the pre-existing (pre-exposure), steady-state damaged cells that are there in the body due to spontaneous biological damage. It is understandable, though, that such repair and/or removal may not be 100% efficient, but it is incorrect to completely omit these mechanisms from consideration.

The various radiation dose–response models used to estimate the risk of cancer at low-dose (<100 mSv) radiation exposure are illustrated in Figure 1.

1. Different radiation risk models illustrating the estimated health risk at low levels of ionizing radiation (Reprinted with permission from Ref. [44]. 2022, Canadian Nuclear Safety Commission).

However, the National Council of Radiation Protection and Measurements (NCRP), based on a critical review of the recent epidemiological studies assessing dose–response at low-dose and low-dose rate radiation, recognized that the risks are small and uncertain. Nevertheless, it broadly supports the LNT theory for radiation protection purposes, as no better alternative dose–response model is available as of today [46]. Other regulatory bodies, such as the International Commission on Radiological Protection (ICRP) [26], the United States Environmental Protection Agency (EPA) [47], the United States Nuclear Regulatory Commission (NRC) [35] and the United States National Research Council (NRC) [32] also currently support LNT theory at low-dose radiation.

Another recent review of different dose–response models suggests that scientific evidence supports different biological mechanisms at low-dose radiation; however, they are still not fully understood. Moreover, even if there is an increased risk at low-dose radiation, it must be small, as there are no sufficient epidemiological data for an observable effect [48].

The relatively high magnitude of LBR of cancer incidence (~42%) in the general population makes it difficult to perform an epidemiological study with a large sample size to evaluate the risk of low-dose radiation with sufficient statistical power [49]. The sample size is proportional to the inverse square of the dose; thus, to quantify the risk of low-dose radiations with precision, larger epidemiological studies are required [50, 51]. For example, if a sample size of 500 individuals is needed to quantify the risk of a 1000 mSv dose, to maintain the same statistical power and precision, a sample size of ~5 million subjects would be required for a 10 mSv dose [50]. Additionally, there are many uncertainties in estimating radiation risks due to several other factors, such as statistical uncertainty, application of risk estimation results in the population exposed to other radiation sources, the random nature of processes that cause cancer, insufficient data, a lack of idealized models to describe the nature of risks in exposed and non-exposed populations, and exposure to other cancer risk factors such as smoking [26, 52]. The Biologic Effects of Ionizing Radiation (BEIR) VII report presented its best estimates for cancer incidence and mortality at low-dose radiation in human subjects (Table 3) [32]. These estimates are accompanied by 95% subjective confidence intervals that reflect the important sources of uncertainty, nearly by a factor of two.

Table 3. The BEIR VII preferred estimates of the lifetime attributable risk of cancer incidence and mortality from exposure to 100 mSv per 100,000 persons (95% subjective confidence interval).

Adapted with permission from Ref. [32]. 2022, Biologic Effects of Ionizing Radiation (BEIR) VII report.

With the given controversies and uncertainties in dose–response models, there is currently no consensus on LAR estimates for low-dose radiation exposures [8] and radiation protection policies [10]. It is likely that the risk of some cancers could be overestimated, while those of others is underestimated [50]. Moreover, a subset of individuals can be more susceptible and genetically predisposed to the carcinogenic effects of radiation, such as those with congenital/acquired genetic mutations or defective genes [53].

Thus, with the understanding of radiation-related cancer risk still evolving, and untill the time we obtain clear answers, a conservative policy needs to be adopted to ensure patients’ safety by following the basic ALARA (as low as reasonably achievable) principle of radiation exposure through the process of justification and optimization [8].

References

1. Schulz, R.A.; Stein, J.A.; Pelc, N.J. How CT happened: The early development of medical computed tomography. Med. Imaging2021, 8, 052110–26. [Google Scholar] [CrossRef] [PubMed]
2. Shao, Y.H.; Tsai, K.; Kim, S.; Wu, Y.J.; Demissie, K. Exposure to tomographic scans and cancer risks. JNCI Cancer Spectr.2020, 4, pkz072. [Google Scholar] [CrossRef] [PubMed]
3. Kuo, W.; Ciet, P.; Tiddens, H.A.; Zhang, W.; Guillerman, R.P.; Van Straten, M. Monitoring cystic fibrosis lung disease by computed tomography. Radiation risk in perspective. J. Respir. Crit. Care Med.2014, 189, 1328–1336. [Google Scholar] [CrossRef] [PubMed]
4. Davenport, M.S.; Chu, P.; Szczykutowicz, T.P.; Smith-Bindman, R. Comparison of Strategies to Conserve Iodinated Intravascular Contrast Media for Computed Tomography During a Shortage. JAMA2022, 328, 476–478. [Google Scholar] [CrossRef] [PubMed]
5. Miglioretti, D.L.; Johnson, E.; Williams, A. Pediatric computed tomography and associated radiation exposure and estimated cancer risk. JAMA Pediatr.2013, 167, 700–707. [Google Scholar] [CrossRef]
6. Brenner, D.; Hall, E. Computed tomography: An increasing source of radiation exposure. Engl. J. Med.2007, 357, 2277–2284. [Google Scholar] [CrossRef]
7. Coursey, C.; Frush, D.P.; Yoshizumi, T.; Toncheva, G.; Nguyen, G.; Greenberg, S.B. Pediatric chest MDCT using tube current modulation: Effect on radiation dose with breast shielding. J. Roentgenol.2008, 190, 54–61. [Google Scholar] [CrossRef]
8. World Health Organization. Communicating Radiation Risks in Paediatric Imaging: Information to Support Health Care Discussions about Benefit and Risk, 1st ed.; World Health Organization: Geneva, Switzerland, 2016; pp. 14–27. [Google Scholar]
9. Devic, C.; Bodgi, L.; Sonzogni, L.; Pilleul, F.; Ribot, H.; Charry, C.D.; Le Moigne, F.; Paul, D.; Carbillet, F.; Munier, M.; et al. Influence of cellular models and individual factor in the biological response to chest CT scan exams. Radiol. Exp.2022, 6, 14. [Google Scholar] [CrossRef]
10. Grant, E.J.; Brenner, A.; Sugiyama, H.; Sakata, R.; Sadakane, A.; Utada, M.; Cahoon, E.K.; Milder, C.M.; Soda, M.; Cullings, H.M.; et al. Solid cancer incidence among the life span study of atomic bomb survivors: 1958–2009. Res.2017, 187, 513–537. [Google Scholar] [CrossRef]
11. De Gonzalez, A.B.; Salotti, J.A.; McHugh, K.; Little, M.P.; Harbron, R.W.; Lee, C.; Ntowe, E.; Braganza, M.Z.; Parker, L.; Rajaraman, P.; et al. Relationship between paediatric CT scans and subsequent risk of leukaemia and brain tumours: Assessment of the impact of underlying conditions. J. Cancer2016, 114, 388–394. [Google Scholar] [CrossRef]
12. Huang, W.; Muo, C.; Lin, C.; Jen, Y.; Yang, M.; Lin, J.; Sung, F.C.; Kao, C.H. Paediatric head CT scan and subsequent risk of malignancy and benign brain tumour: A nation-wide population-based cohort study. J. Cancer2014, 110, 2354–2360. [Google Scholar] [CrossRef] [PubMed]
13. Wang, F.; Sun, Q.; Wang, J.; Yu, N. Risk of developing cancers due to low-dose radiation exposure among medical X-ray workers in China—Results of a prospective study. J. Clin. Exp. Pathol.2016, 9, 11897–11903. [Google Scholar]
14. Preston, D.; Kitahara, C.; Freedman, D.; Sigurdson, A.; Simon, S.; Little, M.; Cahoon, E.K.; Rajaraman, P.; Miller, J.S.; Alexander, B.H.; et al. Breast cancer risk and protracted low-to-moderate dose occupational radiation exposure in the US Radiologic Technologists Cohort, 1983–2008. J. Cancer2016, 115, 1105–1112. [Google Scholar] [CrossRef] [PubMed]
15. Mathews, J.D.; Forsythe, A.V.; Brady, Z.; Butler, M.W.; Goergen, S.K.; Byrnes, G.B.; Giles, G.G.; Wallace, A.B.; Anderson, P.R.; Guiver, T.A.; et al. Cancer risk in 680,000 people exposed to computed tomography scans in childhood or adolescence: Data linkage study of 11 million Australians. BMJ2013, 346, f2360. [Google Scholar] [CrossRef] [PubMed]
16. De Gonzalez, A.B.; Darby, S. Risk of cancer from diagnostic X-rays: Estimates for the UK and 14 other countries. Lancet2004, 363, 345–351. [Google Scholar] [CrossRef] [PubMed]
17. Zhou, Y.; Zheng, Y.; Wen, Y.; Dai, X.; Liu, W.; Gong, Q.; Chaoqiong, H.; Fajin, L.; Jiahui, W. Radiation dose levels in chest computed tomography scans of coronavirus disease 2019 pneumonia: A survey of 2119 patients in Chongqing, southwest China. Medicine2021, 100, e26692. [Google Scholar] [CrossRef] [PubMed]
18. Garg, M.; Prabhakar, N.; Muthu, V.; Farookh, S.; Kaur, H.; Suri, V.; Ritesh, A. CT findings of COVID-19–associated pulmonary mucormycosis: A case series and literature review. Radiology2022, 302, 214–217. [Google Scholar] [CrossRef]
19. WHO Coronavirus (COVID-19) Dashboard. Available online: https://covid19.who.int/(accessed on 12 October 2022).
20. COVID-19 Coronavirus Pandemic. Available online: https://www.worldometers.info/coronavirus/(accessed on 12 October 2022).
21. Yurdaisik, I.; Nurili, F.; Aksoy, S.H.; Agirman, A.G.; Aktan, A. Ionizing radiation exposure in patients with COVID-19: More than needed. Prot. Dosim.2021, 194, 135–143. [Google Scholar] [CrossRef]
22. Radmard, A.R.; Gholamrezanezhad, A.; Montazeri, S.A.; Kasaeian, A.; Nematollahy, N.; Langrudi, R.M.; Reza Javad, R.; Dehghan, A.; Hekmatnia, A.; Shakourirad, A.; et al. A multicenter survey on the trend of chest CT scan utilization: Tracing the first footsteps of COVID-19 in Iran. Iran. Med.2020, 23, 787–793. [Google Scholar] [CrossRef]
23. Bahrami-Motlagh, H.; Abbasi, S.; Haghighimorad, M.; Salevatipour, B.; Alavi Darazam, I.; Sanei Taheri, M.; Esmaeili Tarki, F.; Naghibi Irvani, S.S. Performance of low-dose chest CT scan for initial triage of COVID-19. J. Radiol.2020, 17, 104950. [Google Scholar] [CrossRef]
24. Kang, Z.; Li, X.; Zhou, S. Recommendation of low-dose CT in the detection and management of COVID-2019. Radiol.2020, 30, 4356–4357. [Google Scholar] [CrossRef] [PubMed]
25. Homayounieh, F.; Holmberg, O.; Umairi, R.A.; Aly, S.; Basevičius, A.; Costa, P.R.; Darweesh, A.; Gershan, V.; Ilves, P.; Kostova-Lefterova, D.; et al. Variations in CT utilization, protocols, and radiation doses in COVID-19 pneumonia: Results from 28 countries in the IAEA study. Radiology2021, 298, 141–151. [Google Scholar] [CrossRef] [PubMed]
26. The 2007 recommendations of the International Commission on Radiological Protection: ICRP publication 103. Ann. ICRP2007, 37, 1–322. [Google Scholar]
27. Dowd, S.B.; Tilson, E.R. Practical Radiation Protection and Applied Radiobiology; WB Saunders: Philadelphia, PA, USA, 1999. [Google Scholar]
28. Hall, E.J.; Giaccia, A.J. Radiobiology for the Radiologist; Lippincott Williams & Wilkins: Philadelphia, PA, USA, 2006. [Google Scholar]
29. Linet, M.S.; Slovis, T.L.; Miller, D.L.; Kleinerman, R.; Lee, C.; Rajaraman, P.; de Gonzalez, A.B. Cancer risks associated with external radiation from diagnostic imaging procedures. CA Cancer J. Clin.2012, 62, 75–100. [Google Scholar] [CrossRef]
30. Garg, M.; Prabhakar, N.; Bhalla, A.S. Cancer risk of CT scan in COVID-19: Resolving the dilemma. Indian J. Med. Res.2021, 153, 568–571. [Google Scholar] [CrossRef]
31. American Cancer Society. Lifetime Risk of Developing or Dying from Cancer. Available online: http://bit.ly/2hyGDR(accessed on 12 October 2022).
32. National Research Council. Health Risks from Exposure to Low Levels of Ionizing Radiation: BEIR VII Phase 2; National Academies Press: Washington, DC, USA, 2006.
33. Andersson, M.; Eckerman, K.; Mattsson, S. Lifetime attributable risk as an alternative to effective dose to describe the risk of cancer for patients in diagnostic and therapeutic nuclear medicine. Med. Biol.2017, 62, 9177–9188. [Google Scholar] [CrossRef]
34. Azadbakht, J.; Khoramian, D.; Lajevardi, Z.S.; Elikaii, F.; Aflatoonian, A.H.; Farhood, B.; Najafi, M.; Bagheri, H. A review on chest CT scanning parameters implemented in COVID-19 patients: Bringing low-dose CT protocols into play. J. Radiol. Nucl.2021, 52, 13. [Google Scholar] [CrossRef]
35. Nuclear Regulatory Commission. Linear no-threshold model and standards for protection against radiation. Reg.2021, 86, 45923–45936. [Google Scholar]
36. Calabrese, E.J. Linear non-threshold (LNT) fails numerous toxicological stress tests: Implications for continued policy use. -Biol. Interact.2022, 365, 110064. [Google Scholar] [CrossRef]
37. Health Physics Society. Position statement of the health physics society PS010-4: Radiation risk in perspective. Health Phys.2020, 118, 79–80. [Google Scholar] [CrossRef] [PubMed]
38. United Nations. Sources, Effects and Risks of Ionizing Radiation: UNSCEAR 2013. United Nations Scientific Committee on the Effects of Atomic Radiation. Available online: https://www.unscear.org/docs/publications/2013/UNSCEAR_2013_Report_Vol.I.pdf(accessed on 12 October 2022).
39. United States Nuclear Regulatory Commission. Radiation Exposure and Cancer. Available online: https://www.nrc.gov/about-nrc/radiation/health-effects/rad-exposure-cancer.html(accessed on 12 October 2022).
40. American Nuclear Society. Health Effects of Low-Level Radiation: Position Statement 41. Available online: https://ans.org/pi/ps/docs/ps41.pdf(accessed on 12 October 2022).
41. Calabrese, E.J.; Shamoun, D.Y.; Agathokleous, E. Dose response and risk assessment: Evolutionary foundations. Pollut.2022, 309, 119787. [Google Scholar] [CrossRef] [PubMed]
42. Scott, B.R.; Tharmalingam, S. The LNT model for cancer induction is not supported by radiobiological data. Biol. Interact.2019, 301, 34–53. [Google Scholar] [CrossRef] [PubMed]
43. Pennington, C.W.; Siegel, J.A. The linear no-threshold model of low-dose radiogenic cancer: A failed fiction. Dose-Response2019, 17, 824200. [Google Scholar] [CrossRef] [PubMed]
44. Canadian Nuclear Safety Commission. Linear-Non-Threshold Model. Available online: https://nuclearsafety.gc.ca/eng/resources/health/linear-non-threshold-model/index.cfm(accessed on 12 October 2022).
45. Vaiserman, A.; Koliada, A.; Socol, Y. Hormesis through Low-Dose Radiation. Hormesis Health Longev.2019, 22, 129–138. [Google Scholar] [CrossRef]
46. Shore, R.E.; Beck, H.L.; Boice, J.D.; Caffrey, E.A.; Davis, S.; Grogan, H.; Mettler, F.A.; Preston, R.J.; Till, J.E.; Wakeford, R.; et al. Implications of recent epidemiologic studies for the linear nonthreshold model and radiation protection. Radiol. Prot.2018, 38, 1217–1233. [Google Scholar] [CrossRef] [PubMed]
47. Puskin, J.S. Perspective on the use of LNT for radiation protection and risk assessment by the US Environmental Protection Agency. Dose-Response2009, 7, 284–291. [Google Scholar] [CrossRef]
48. Leblanc, J.E.; Burtt, J.J. Radiation biology and its role in the Canadian radiation protection framework. Health Phys.2019, 117, 319–329. [Google Scholar] [CrossRef]
49. Smith-Bindman, R.; Lipson, J.; Marcus, R.; Kim, K.-P.; Mahesh, M.; Gould, R.; de González, A.B.; Miglioretti, D.L. Radiation dose associated with common computed tomography examinations and the associated lifetime attributable risk of cancer. Intern. Med.2009, 169, 2078–2086. [Google Scholar] [CrossRef]
50. Brenner, D.J.; Doll, R.; Goodhead, D.T.; Hall, E.J.; Land, C.E.; Little, J.B.; Lubin, J.H.; Preston, D.L.; Preston, R.J.; Puskin, J.S.; et al. Cancer risks attributable to low doses of ionizing radiation: Assessing what we really know. Natl. Acad. Sci. USA2003, 100, 13761–13766. [Google Scholar] [CrossRef]
51. Pochin, E. Problems involved in detecting increased malignancy rates in areas of high natural radiation background. Health Phys.1976, 31, 148–151. [Google Scholar] [PubMed]
52. International Atomic Energy Agency. Methods for Estimating the Probability of Cancer from Occupational Radiation Exposure. Available online: https://www-pub.iaea.org/MTCD/Publications/PDF/te_870_web.pdf(accessed on 12 October 2022).
53. Williams, D. Radiation carcinogenesis: Lessons from Chernobyl. Oncogene2008, 27, 9–18. [Google Scholar] [CrossRef] [PubMed]