Stochastic effects occur by chance and can be compared to deterministic effects which result in a direct effect. Cancer induction and radiation induced hereditary effects are the two main examples of stochastic effects.
Models
Cancer induction as a result of exposure to radiation is thought by most to occur in a stochastic manner: there is no threshold point and the risk increases in proportionally with dose. Although the exact model which predicts the stochastic effects of radiation is contentious, numerous models do exist including:
- linear-no threshold model: the risk of cancer induction increases linearly with no threshold dose (this is currently the accepted model by the International Commission on Radiological Protection) ^{5}
- linear-quadratic model: the risk of cancer induction increases in a quadratic-linear function
- adaptive-dose response model (hormesis): an adaptive dose-response relationship where low doses are protective and high doses are detrimental ^{4}
- bystander effect: model of radiobiological damage (so-called epigenetic) related to the damage that cells not directly irradiated suffer due to molecular signals (e.g. reactive oxygen and nitrogen species and cytokines) arriving from irradiated cells present in the same tissue or in other tissues of the same organism ^{6-9}.
Although the risk increases with dose, the severity of the effects do not; the patient will either develop cancer or they will not.
Statistical power of current epidemiologic studies
It is important to note that individual studies that explore radiation-induced carcinogenesis as a result of low doses (<100mGy) have low statistical power and overall are not statistically significant ^{10}.