Longitudinal in-vivo quantification of tumour microvascular heterogeneity by optical coherence angiography in pre-clinical radiation therapy
Bibliographic record
Abstract
Stereotactic body radiotherapy (SBRT) is an emerging cancer treatment due to its logistical and potential therapeutic benefits as compared to conventional radiotherapy. However, its mechanism of action is yet to be fully understood, likely involving the ablation of tumour microvasculature by higher doses per fraction used in SBRT. In this study, we hypothesized that longitudinal imaging and quantification of the vascular architecture may elucidate the relationship between the microvasculature and tumour response kinetics. Pancreatic human tumour xenografts were thus irradiated with single doses of [Formula: see text], [Formula: see text] and [Formula: see text] Gy to simulate the first fraction of a SBRT protocol. Tumour microvascular changes were monitored with optical coherence angiography for up to [Formula: see text] weeks following irradiation. The temporal kinetics of two microvascular architectural metrics were studied as a function of time and dose: the diffusion-limited fraction, representing poorly vascularized tissue [Formula: see text] μm from the nearest detected vessel, and the vascular distribution convexity index, a measure of vessel aggregation at short distances. These biological metrics allowed for dose dependent temporal evaluation of tissue (re)vascularization and vessel aggregation after radiotherapy, showing promise for determining the SBRT dose-response relationship.
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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) | 0.000 | 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 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".