Quantification of the tumour microvascular response to high dose-per-fraction radiotherapy
Bibliographic record
Abstract
Abstract Objective. Microvascular ablation during high dose-per-fraction radiotherapy (HDFRT) is disparately reported in the literature. This study was conducted to quantify the tumour microvascular response to different HDFRT schedules. Approach. A high single-dose irradiation of 20 Gy and two multifraction schedules (three fractions of 10 Gy and 15 Gy each) were studied. Patient-derived BxPC-3 pancreatic tumours in a mouse dorsal skinfold window chamber were treated and their 3D microvascular networks were longitudinally imaged with speckle variance optical coherence tomography for up to 7 weeks post irradiation. The overall vascular volume density (VVD), VVD for small vessels (diameters between 15–25 μm and 25–35 μm), and the vascular convexity index λ (a measure of vessel organization and space filling at short distances) were quantified. Main results. There were no significant differences in overall VVD for treated vs. control tumours at all timepoints. Examination of small-diameter vessels revealed some transient reductions in VVD15−25 μm and VVD25−35 μm compared to controls at t ∼ 3 weeks for larger dose-per-fraction regimens (3 × 15 Gy and 1 × 20 Gy); ablated vasculature regrew back to baseline values by 7 weeks. Convexity indices for these larger-dose-per-fraction tumours were ∼55% larger than unirradiated controls by the end of monitoring period; no such effects were seen in the 3 × 10 Gy cohort. Significance. The results of this study reveal the complex role of small vessels in microvascular ablation caused by HDFRT, with a dependence on the dose per fraction and total delivered dose. After small vessel ablation, regrown vessels had more uniform and regular spacing than non-ablated vessels as quantified by λ, potentially suggesting improved tumour response if subsequent retreatments are attempted.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".