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 non-invasive 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, helps in ssaving 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 [10-15]^{[10][11][12][13][14][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 [21-25]^{[21][22][23][24][25]}. Thise 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]. TablesTable 1 1 and 2Table 2 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.

Figur

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 till 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].

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