A simple mathematical model of cyclic hypoxia and its impact on hypofractionated radiotherapy
Bibliographic record
Abstract
Abstract Purpose There is evidence that the population of cells that experience fluctuating oxygen levels (“acute,” or, “cyclic” hypoxia) are more radioresistant than chronically hypoxic ones and hence, this population may determine radiotherapy (RT) response, in particular for hypofractionated RT, where reoxygenation may not be as prominent. A considerable effort has been devoted to examining the impact of hypoxia on hypofractionated RT; however, much less attention has been paid to cyclic hypoxia specifically and the role its kinetics may play in determining the efficacy of these treatments. Here, a simple mathematical model of cyclic hypoxia and fractionation effects was worked out to quantify this. Methods Cancer clonogen survival fraction was estimated using the linear quadratic model, modified to account for oxygen enhancement effects. An analytic approximation for oxygen transport away from a random network of capillaries with fluctuating oxygen levels was used to model inter‐fraction tissue oxygen kinetics. The resulting survival fraction formula was used to derive an expression for the iso‐survival biologically effective dose (BED), BEDiso-SF. These were computed for some common extra‐cranial hypofractionated RT regimens. Results Using relevant literature parameter values, inter‐fraction fluctuations in oxygenation were found to result in an added 1–2 logs of clonogen survival fraction in going from five fractions to one for the same nominal BED (i.e., excluding the effects of oxygen levels on radiosensitivity). BEDiso-SF's for most ultra‐hypofractionated (five or fewer fractions) regimens in a given tumor site are similar in magnitude, suggesting iso‐efficacy for common fractionation schedules. Conclusions Although significant, the loss of cell‐killing with increasing hypofractionation is not nearly as large as previous estimates based on the assumption of complete reoxygenation between fractions. Most ultra‐hypofractionated regimens currently in place offer sufficiently high doses to counter this loss of cell killing, although care should be taken in implementing single‐fraction regimens.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".