How quickly does FLASH need to be delivered? A theoretical study of radiolytic oxygen depletion kinetics in tissues
Bibliographic record
Abstract
Abstract Purpose . Radiation delivered over ultra-short timescales (‘FLASH’ radiotherapy) leads to a reduction in normal tissue toxicities for a range of tissues in the preclinical setting. Experiments have shown this reduction occurs for total delivery times less than a ‘critical’ time that varies by two orders of magnitude between brain (∼0.3 s) and skin (⪆10 s), and three orders of magnitude across different bowel experiments, from ∼0.01 to ⪆(1–10) s. Understanding the factors responsible for this broad variation may be important for translation of FLASH into the clinic and understanding the mechanisms behind FLASH. Methods. Assuming radiolytic oxygen depletion (ROD) to be the primary driver of FLASH effects, oxygen diffusion, consumption, and ROD were evaluated numerically for simulated tissues with pseudorandom vasculatures for a range of radiation delivery times, capillary densities, and oxygen consumption rates (OCR’s). The resulting time-dependent oxygen partial pressure distribution histograms were used to estimate cell survival in these tissues using the linear quadratic model, modified to incorporate oxygen-enhancement ratio effects. Results . Independent of the capillary density, there was a substantial increase in predicted cell survival when the total delivery time was less than the capillary oxygen tension (mmHg) divided by the OCR (expressed in units of mmHg/s), setting the critical delivery time for FLASH in simulated tissues. Using literature OCR values for different normal tissues, the predicted range of critical delivery times agreed well with experimental values for skin and brain and, modifying our model to allow for fluctuating perfusion, bowel. Conclusions . The broad three-orders-of-magnitude variation in critical irradiation delivery times observed in in vivo preclinical experiments can be accounted for by the ROD hypothesis and differences in the OCR amongst simulated normal tissues. Characterization of these may help guide future experiments and open the door to optimized tissue-specific clinical protocols.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".