An Integrated Method for Reproducible and Accurate Image-Guided Stereotactic Cranial Irradiation of Brain Tumors Using the Small Animal Radiation Research Platform
Bibliographic record
Abstract
Preclinical studies of cranial radiation therapy (RT) using animal brain tumor models have been hampered by technical limitations in the delivery of clinically relevant RT. We established a bioimageable mouse model of glioblastoma multiforme (GBM) and an image-guided radiation delivery system that facilitated precise tumor localization and treatment and which closely resembled clinical RT. Our novel radiation system makes use of magnetic resonance imaging (MRI) and bioluminescent imaging (BLI) to define tumor volumes, computed tomographic (CT) imaging for accurate treatment planning, a novel mouse immobilization system, and precise treatments delivered with the Small Animal Radiation Research Platform. We demonstrated that, in vivo, BLI correlated well with MRI for defining tumor volumes. Our novel restraint system enhanced setup reproducibility and precision, was atraumatic, and minimized artifacts on CT imaging used for treatment planning. We confirmed precise radiation delivery through immunofluorescent analysis of the phosphorylation of histone H2AX in irradiated brains and brain tumors. Assays with an intravenous near-infrared fluorescent probe confirmed that radiation of orthografts increased disruption of the tumor blood-brain barrier (BBB). This integrated model system, which facilitated delivery of precise, reproducible, stereotactic cranial RT in mice and confirmed RT's resultant histologic and BBB changes, may aid future brain tumor research.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".