In this post we will describe the stochastic effect of radiation carcinogenesis (a fancy way of saying a higher risk of cancer induction after being exposed to ionizing radiation). The mechanism for carcinogenesis is primarily x-rays coming in, generating energetic electrons which then either directly or indirectly cause DNA damage. The data which we have on carcinogenesis is from high radiation dose events such as the atomic bomb survivors and there is not complete consensus on the best way to estimate the effects of the very low levels of radiation, which are received during diagnostic procedures. The most accepted method being the linear no threshold model which directly fits a line to the data that is available and uses this to extrapolate what the effects will be for low levels of radiation.
When the DNA cannot be properly repaired there is some chance of cancer induction. Here we discuss the cases in human history where a clear link has been demonstrated between radiation dose and cancer risk.
All of the solid data on elevated cancer risk is from relatively high dose exposures. Therefore, in order to estimate the risk at the low radiation doses of diagnostic x-ray and CT scanning a method is needed to estimate data beyond the measurements.
The process of estimating data beyond the available measurements is called extrapolation, and multiple methods for performing the extrapolation are presented.
First Demonstrated Cases
The trailblazers, i.e. the first people who work in new industry, often pay the highest price. This is the case with those who did the pioneering research in radioactive decay and x-ray physics.
Marie Curie, famous physicist/chemist and two-time Nobel Prize laureate discovered fundamental properties involving radioactive isotopes. She is believed to have died from leukemia because of the high radiation doses that she had received during this research.
Additionally, early doctors, dentists and x-ray technologists who had been working with radiation before the downsides and the health effects of radiation were known also had significantly higher risks of cancer than the normal population.
Atomic Bomb Survivors (primary knowledge of radiation/cancer link)
Most information about radiation and its effects on humans comes from atomic bomb survivors. After the two explosions of atomic bombs in Hiroshima and Nagasaki Japan in 1945, information on the lifetime mortality and estimated radiation dose has been tracked and documented.
Since the information we have is primarily from these high doses of radiation there is a need to estimate (or make an educated guess) as to what the effects will be at lower dose levels. This process of using the high dose data to estimate low dose data is called extrapolation.
It would be unethical to irradiate individuals even at a low level given the known harms with high levels of radiation. Thus, we are unlikely to get better data in the near future about the exact effects of the low levels of radiation.
Examples of Radiation Effects
In this section we list several cancers that have been demonstrated to have an increase risk in human populations that have received radiation exposure. This list is not fully comprehensive but outlines that many types of cancer have been linked to radiation exposure.
Leukemia, blood cancer, has been developed in atomic bomb survivors. Another example is liver cancer that has been demonstrated to have a higher prevalence in atomic bomb survivors as well as from exposure to an early contrast agent which actually ended up depositing radiation dose to the liver.
Breast cancer has been detected among atomic bomb survivors and individuals in Nova Scotia who they received many repeated x-rays.
A group of young women developed bone cancer because they all were painting watch dials by hand. To have sharp brush tips, they were putting the brushes in their mouths frequently. Each time they would do that, they would end up actually ingesting a little bit of the paint. Since this paint contained radium they were ingesting radium. That radium ended up settling in their bones and led to an increased risk of bone cancer.
Lung cancer is a case that’s been observed in atomic bomb survivors as well as minors that have been exposed to radon.
Atomic bomb survivors from Hiroshima and Nagasaki also have seen increased risk of thyroid cancer. And the Chernobyl accident also demonstrated increased risk of thyroid cancer.
Additionally, skin cancer was the first demonstrated cancer to be caused by x-rays. In 1902 this was found on the hand of a dentist. Shortly thereafter it was found that physicians, and x-ray technologists also demonstrated increased risk of skin cancer especially in the early days of x-ray use.
Extrapolated Risk Data
Plots are used to visually demonstrate the excess risk of cancer in those exposed to radiation.
In this plot for instance the excess risk of a solid cancer is plotted against the radiation dose that those individuals received. This is from the BEIR VII report.
For the purpose of estimating the cancer induction risk due to diagnostic radiology procedures we need to determine what the impact is in the far bottom left-hand corner of this type of graph. These exams are typically on the order of several milliSieverts (mSv).
There is not actual good data in this area of the graph so we have to do something called extrapolation to estimate the risk of cancer induction due to these diagnostic procedures.
There are multiple possible ways that this extrapolation can be performed and there is not widespread agreement amongst professionals in the radiation health safety and medical physicists.
In this figure we demonstrate four basic options for performing the extrapolation.
The simplest technique is to draw a straight line through the data. The blue line here is what we get if we draw a straight line through the data and then we keep drawing that straight line down all the way to the origin where there’s zero radiation dose.
That line is called linear extrapolation and in this case we also say there’s no threshold. So, there’s no threshold below which the effects change. Even at a very low level, we assume the effects are the same. For this reason the most commonly accepted extrapolation method is referred to as Linear No Threshold (LNT).
The other possible options would be that at low levels, the radiation damage is actually worse. That would be the curve shown in yellow. That’s called supra-linear quadratic extrapolation.
There’s also quadratic extrapolation shown in green where you assume that the effects at very low levels actually are not as bad for higher levels of radiation radiation.
Then the final is called hormesis. Examples of hormesis have been demonstrated in single cell experiments. In that case, the extrapolated curve actually goes below the x-axis indicating that at very low levels, there’s actually a positive effect of the radiation dose.
Given the currently available data the most accepted model is the Linear No Threshold extrapolation. From the conclusion of BIER VII. “The committee concludes that the current scientific evidence is consistent with the hypothesis that there is a linear, no-threshold dose response relationship between exposure to ionizing radiation and the development of cancer in humans.”
|High Dose, High Dose Rate||Low Dose, Low Dose Rate|
|Working Population||0.08 / Sv||0.04 / Sv|
|Entire Population||0.01 / Sv||0.05 / Sv|
To summarize, this currently accepted approach in the field is to use the simplest approach of drawing the straight line through the data and use a linear extrapolation.
Using this approach the ICRP guidelines are given in this figure. In a working population, if there is a relatively high radiation dose, it’s about an 8% increase in cancer induction per Sv. Keep in mind that the doses for diagnostic exams are typically reported in mSv (1/1000 of a Sv).
For a relatively lower radiation dose, like a diagnostic procedure, it’s about a 4% increase and this is per Sievert.
For example if the radiation dose of the procedure is 10mSv, then we need to multiply these risk factors by 1/100. (10mSv/1000mSv).
Another way of describing the estimated risk is described in BIER VII, “For example, the committee predicts that approximately one individual per thousand would develop cancer from an exposure to 0.01 Sv (10mSv). As another example, approximately one individual per hundred would be expected to develop cancer from a lifetime (70-year) exposure to low-LET, natural background radiation (excluding radon and other high-LET radiation).”
This info graphic shows the estimate from the BIER VII summary of the increased cancer risk from a single high radiation dose (high compared with standard diagnostic doses) of 100mSv. This calculation assumed a Linear No Threshold (LNT) of the high dose known risk data from atomic bomb survivors. Another assumption was that the radiation source is a low LET [Linear Energy Transfer] source such as x-rays (whereas high LET sources like neutrons cause more DNA damage given the same energy deposited). Finally, the additional risk calculations accounted for both solid tumors and leukemia.
Finally, we want to point out that these are all estimates over a large population and there is significant uncertainty in these estimates, so it is not recommended to use these data to make an individual risk assessment.
To quote again from BIER VII, “Because of limitations in the data used to develop risk models, risk estimates are uncertain, and estimates that are a factor of two or three larger or smaller cannot be excluded.”
Rad Take-home Point:
- Multiple types of cancer are associated with radiation exposure and rates of cancer were high before there was awareness of the dangers of radiation.
- The data on cancer induction from radiation exposure is primarily at high radiation doses and is currently extrapolated using the Linear No Threshold (LNT) model.
- There is significant uncertainty in the estimates of cancer induction.
- Given the low risk of cancer induction from diagnostic imaging the current standard is to recommend, ALARA is an acronym for “as low as (is) reasonably achievable,” which means making every reasonable effort to maintain exposures to ionizing radiation as far below the dose limits as practical.