The Influence of Hypoxia on the Relative Sensitivity of Human Tumor Cells to 62.5 MeV (p→Be) Fast Neutrons and 4 MeV Photons
Bibliographic record
Abstract
Fast neutrons have been used in the clinical radiation therapy of tumors largely because of experimental evidence that their cytotoxic effects are much less dependent on oxygen levels than those of low-LET photons. The potential therapeutic advantage of fast neutrons based on hypoxia alone can be calculated as the "hypoxic gain factor", which is the ratio of the OERs for the fast-neutron compared to the photon beams. The hypoxic gain factor that is generally anticipated based on studies with established mammalian cell lines is about 1.6. However, surprisingly few studies have examined the influence of hypoxia on the fast-neutron radiosensitivity of human tumor cells of different histological types. For this reason, we have determined the OERs of five human tumor cell lines exposed to 62.5 MeV (p-->Be) cyclotron-generated fast neutrons or 4 MeV photons from a clinical linear accelerator. The OERs for four chemotherapy-naive cell lines, HT29/5, Hep2, HeLa and RT112, were invariably greater for photons than for neutrons, but all of these values were lower than expected on the basis of the previous literature. Despite their low OERs, these cell lines showed hypoxic gain factors that were within the range of 1.31-1.63, indicating that such effects cannot entirely explain the disappointing clinical results obtained with fast neutrons. In contrast, comparison of the surviving fractions at clinically relevant doses (1.6 Gy of neutrons and 2.0 Gy of photons) for these four tumor cell lines suggested that little benefit should result from neutron treatment. Only the cisplatin-resistant OAW42-CP line showed a significant hypoxic gain factor by this method of analysis. We conclude that, at the dose fractions used in clinical radiation therapy, there may not be a radiobiological precedent for higher local control rates after fast-neutron irradiation of hypoxic tumor cells.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".