|Kidney||1.0 - 2.4|
|Bladder||3.1 - 7.0|
|Lung||3.9 - 8.2|
|Brainstem||2.0 - 2.1|
|Prostate||1.1 - 3.7|
|Spinal Cord (Cervical)||1.5 - 3.0|
|Spinal Cord (Lumbar)||2.3 - 4.9|
|Bowel||2.2 - 6.0|
|Brachial Plexus||2.1 - 4.1|
|HN||13.8 - 23|
|Breast||2.5 - 4.6|
The equation determines the biological effect (E) as a function of dose (D), the linear dose damage response (α), and the quadratic dose response in tissue (β).
In general, cell injury can be classified into three categories.
- Lethal damage: unrepairable and will lead to cell death
- Sublethal damage: interactions that are not singularly lethal but can combine to result in cell death
- Potentially lethal damage: might be lethal at certain stages of the cell cycle and not fatal at others (e.g., unrepaired damage induced at the mitotic stage)
In the above equation, α represents irreparable lethal damage like double-stranded breaks, while β represents the damage done by two separate breaks or an accumulation of sublethal events. The variables can be manipulated in such a way to create a biologically equivalent dose, which can help compare clinical fractionation schemes by quantifying the biological effect of different schemes using a tissue-based variable (α/β ratio):
The α/β ratio represents the dose at which linear and quadratic components of cell killing are equal for a given tissue. A low α/β ratio (~3) indicates a late response, and a high ratio (~10) suggests an early response in tissue. In general, early responding tissue proliferates quickly while late responding tissues tend to be slower growing with a longer cell cycle. The fast proliferation in early responding tissue causes a majority of cells to spend time in the most radioresistant (S) phase. Increasing dose to overcome this can risk exceeding tissue tolerances, instead, treatments take advantage of a process called self-sensitizing. After irradiation, the surviving cells in highly proliferating tissues redistribute across other phases. By hyperfractionating, cells in radiosensitive phases are killed and then replaced by the surviving cells from more radioresistant phases. The second closely timed fraction, in turn, targets the newly redistributed cells. For late responding tissues (low α/β ratio), radioresistance arises from cells in early or resting radioresistant phases (G1, G0). Conventionally this is overcome using hypofractionated schemes might provide superior tumor control due to a more robust response in the linear portion.
Suppose you want to compare hypofractionation vs. conventional fractionation and their respective effects on late (α/β = 3) and early (α/β = 10) responding tissues. The standard fractionation scheme is 180cGy x 44, and the hypofractionated is 250cGy x 28.
From equations three through six, it is seen that the conventional fractionation scheme is hotter for late responding tissue but colder for early responding tissue, so for early responding tissues, hypofractionation is superior. Now suppose we take a simplified HN case with an α/β = 10. Suppose you want to compare the conventional fractionation scheme of 200cGy x 30 with hyperfractionation of 110cGy x 70.
In this case, the hyperfractionated scheme delivers a more effective dose. In a more complicated prescription, a boost could be added to provide an additional dose. The total BED would just be the summation of each individual BED. It is important to note that if the boost were using brachytherapy instead of EBRT, the BEDs would still be summed; however, the brachytherapy BED requires a different formulation due to the significant damage repair during delivery.
Using the previous example, suppose you want to figure out what 2 Gy fractionation scheme would give the same BED as the original prescription. If the BED = 96 Gy and the α/β = 10, then to achieve the same BED, a prescription of 200cGy x 80 would be needed. Since the EQD2 is just a derivation from BED and the LQ model, the pitfalls for both are still applicable. However, it does offer a powerful tool for clinicians to relate their empirical experience with a quantitative value.
Written by Kevin Crotteau
Kevin Crotteau is a second-year MS student attending Hofstra University, anticipating graduation in May 2021. His work mostly focuses on optimizing tasks through programming, since as Church-Turing proposed, “Any effective procedure can be simulated by a (generalized) computer.” His hobbies include cooking, rock climbing (when not trapped on the complanate surface that is Long Island), and video games.
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