Technologies have improved, and cancers are being detected earlier. Cancer survival rates have improved over the past several decades.12 The specific contribution of screening has been debated for lung cancer (low-dose computed tomography [LDCT] or chest radiography), breast cancer (mammography), colorectal cancer (computed tomography [CT] colonography, CTC), and other cancers.13–16 Several trials have demonstrated that screening can reduce death from both lung cancer and breast cancer by 20% with LDCT and mammography, respectively.17,18 Epidemiological analyses also show a steady decline in mortality rates of colorectal cancer over the past several decades, which are attributed to screening.19

However, concerns have been raised about these routine screening and diagnostic evaluations because of the potential harms, including the radiation risks. The mean effective dose from an LDCT or a CTC is 1.6–2.1 mSv or 7.0–8.0 mSv, respectively.20,21 Mammography screening doses range from 2.0 to 5.0 mSv.22 The total radiation dose for a diagnostic CT can vary widely ranging from 2.2 to 14.0 mSv.23The dose is so low that these risks are not precisely quantifiable. Most of the quantitative information comes from studies of survivors of the atomic bombs and cohorts of radiation workers but it is characterized by a great uncertainty. The theoretical risk is estimated as follows: doses below 20 mSv have a minimal risk of cancer of less than one in 1000 patients, doses of 20–100 mSv have a moderate risk greater than one in 1000 patients, and doses above 100 mSv have clear evidence of radiation-induced cancer.24

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Above 100 mSv: most scientific review groups consider that this dose is appropriate to use a linear dose–response model; below 100 mSv: it is difficult to get consensus on dose–response due to considerable uncertainties. Currently, the most commonly used model is a linear no-threshold (LNT) model, wherein dose–effect data at high dose are simply extrapolated linearly downward to zero without a threshold dose (Figure 2).25 The LNT model is recommended by many expert advisory bodies for setting radiation protection regulations. Lifespan study (LSS) of atomic bomb survivors also shows that dose–response is consistent with the LNT model over 0–2 Gy range for all solid cancers as a group.26 However, the LNT model focuses only on molecular damage, while ignoring protective, organismal biological responses. There is a growing body of experimental and epidemiological evidence that does not support its usage for estimating cancer risks.27 Moreover, LNT sometimes causes considerable stress-related casualties.28 The French Academy of Sciences believes that the use of LNT for assessing carcinogenic risks induced by low doses below 100 mSv is unjustified and should be discouraged.29

(To view a larger version of Figure 2, click here.)

Apart from LNT model, there are various other contradicting models. A sub-linear model such as the linear–quadratic model based on “dual radiation action” theory which reflects the effect of dose and dose rate can explain current leukemia data from the LSS.30 A hormesis model predicts protective effects below a threshold dose, even for noncancer deaths. It belongs to an adaptive response triggered by low-dose radiation, which is supported by multiple animal studies revealing the extension of lifespan of various mammalian models and many retrospective human studies exhibiting the reduced cancer rates or negative excess relative risk.27,31,32 A threshold model predicts a negligible risk below a threshold dose such as immune suppression. This model is also compatible with leukemia data and sarcoma data, suggesting that thresholds cannot be larger than ~60 mSv for cancer or ~0.9 Sv for noncancer disease.33 A supra-linear model fits well with the current cancer incidence data explained by hypersensitivity or bystander effect, and the low-dose hypersensitivity decreases with increasing dose and then disappears at doses of >0.5 Gy due to the biological defense system.34

To better understand the nature of the dose–response in the low-dose zone, it is necessary to use biological models with low variability and high reproducibility. In addition, it is much more important to review the risks and benefits of screening and diagnostic tests. This may serve as an indication for the individual to make a more informed decision to undergo these procedures. For breast cancer risk associated with mammography, the estimated risk is 86 cancers and 11 deaths per 100,000 women screened annually from 40 to 55 years of age and biennially thereafter; the ratio of benefit to risk is 4.5:1 for lives saved and 9.5:1 for life-years saved.35 The estimated number of radiation-related cancers from CTC every 5 years from age 50 to 80 years is 150 cases/100,000 individuals; the estimated number of colorectal cancer prevented by CTC ranges from 3580 to 5190/100,000, yielding a benefit–risk ratio that varied from 24:1 to 35:1.21 The benefit-to-risk ratio of lung cancer screening with CT depends on several factors including efficacy of screening, smoking habits, sex of the screened subject, CT technology, and patient age at the commencement of screening. It can reach about 10:1 for special cohorts and screening efficacy, and it increases with advancing age.36

The radiation-related cancer risk is not well understood at the levels of screening and diagnostic radiation, but there are clearly risks associated with not performing an examination, such as missing a diagnosis and/or initiating treatment too late to improve the medical outcome. Currently, there is rigorous evidence supporting the value of screening and diagnosis, and it is important to implement screening and diagnosis in a manner that is focused on maximizing benefits and minimizing harms. For example, the dose to the fetus resulting from most conventional radiography or nuclear medicine procedures is <0.01 Gy for pregnant women, and it has a risk of childhood cancer and leukemia with an incidence of about 3–4/1000;37 if the dose exceeds 0.01 Gy, it can be reduced with proper tailoring of the examination or adopting another type of examination, such as ultrasonography or magnetic resonance imaging (MRI).

Recently, a new emerging field referred to as radiomics not only provides a quantitative way to assess tumor phenotype but also has shown promise in automatic detection of cancers, staging determinations, and predicting treatment response by applying a large number of quantitative features from screening or diagnosis images.38 This new field will make CT, MRI, and positron emission tomography (PET) more standardized, reproducible, and quantitative. Its potential has been demonstrated in many cancers, in turn allowing for adapting and individualizing treatment at low cost.39 In light of this, cancer risks may be more accurately predicted by radiomic changes, which are assessable prior to the development of observable changes on standard diagnostic imaging. Combining radiomics with genomic data, the so-called radiogenomics could provide the highest level of personalized risk stratification.