Evaluating Changes to Blood-Brain Barrier Integrity in Brain Metastasis over Time and after Radiation Treatment
Bibliographic record
Abstract
INTRODUCTION: The incidence of brain metastasis due to breast cancer is increasing, and prognosis is poor. Treatment is challenging because the blood-brain barrier (BBB) limits efficacy of systemic therapies. In this work, we develop a clinically relevant whole brain radiotherapy (WBRT) plan to investigate the impact of radiation on brain metastasis development and BBB permeability in a murine model. We hypothesize that radiotherapy will decrease tumor burden and increase tumor permeability, which could offer a mechanism to increase drug uptake in brain metastases. METHODS: Contrast-enhanced magnetic resonance imaging (MRI) and high-resolution anatomical MRI were used to evaluate BBB integrity associated with brain metastases due to breast cancer in the MDA-MB-231-BR-HER2 model during their natural development. Novel image-guided microirradiation technology was employed to develop WBRT treatment plans and to investigate if this altered brain metastatic growth or permeability. Histology and immunohistochemistry were performed on whole brain slices corresponding with MRI to validate and further investigate radiological findings. RESULTS: Herein, we show successful implementation of microirradiation technology that can deliver WBRT to small animals. We further report that WBRT following diagnosis of brain metastasis can mitigate, but not eliminate, tumor growth in the MDA-MB-231-BR-HER2 model. Moreover, radiotherapy did not impact BBB permeability associated with metastases. CONCLUSIONS: Clinically relevant WBRT is not curative when delivered after MRI-detectable tumors have developed in this model. A dose of 20 Gy in 2 fractions was not sufficient to increase tumor permeability such that it could be used as a method to increase systemic drug uptake in brain metastasis.
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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 | 0.001 | 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 | 0.001 |
| 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".