Glial activation positron emission tomography imaging in radiation treatment of breast cancer brain metastases
Bibliographic record
Abstract
Brain metastases affect more breast cancer patients than ever before due to increased overall patient survival with improved molecularly targeted treatments. Approximately 25-34% of breast cancer patients develop brain metastases in their lifetime. Due to the blood-brain barrier (BBB), the standard treatment for breast cancer brain metastases (BCBM) is surgery, stereotactic radiosurgery (SRS) and/or whole brain radiation therapy (WBRT). At the cost of cognitive side effects, WBRT has proven efficacy in treating brain metastases when used with local therapies such as SRS and surgery. This review investigated the potential use of glial activation positron emission tomography (PET) imaging for radiation treatment of BCBM. In order to put these studies into context, we provided background on current radiation treatment approaches for BCBM, our current understanding of the brain microenvironment, its interaction with the peripheral immune system, and alterations in the brain microenvironment by BCBM and radiation. We summarized preclinical literature on the interactions between glial activation and cognition and clinical studies using translocator protein (TSPO) PET to image glial activation in the context of neurological diseases. TSPO-PET is not employed clinically in assessing and guiding cancer therapies. However, it has gained traction in preclinical studies where glial activation was investigated from primary brain cancer, metastases and radiation treatments. Novel glial activation PET imaging and its applications in preclinical studies using breast cancer models and glial immunohistochemistry are highlighted. Lastly, we discuss the potential clinical application of glial activation imaging to improve the therapeutic ratio of radiation treatments for BCBM.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".