Novel radiotherapeutic strategies in the management of brain metastases: Challenging the dogma
Bibliographic record
Abstract
The role of radiation therapy in the management of brain metastasis is evolving. Advancements in machine learning techniques have improved our ability to both detect brain metastasis and our ability to contour substructures of the brain as critical organs at risk. Advanced imaging with PET tracers and magnetic resonance imaging-based artificial intelligence models can now predict tumor control and differentiate tumor progression from radiation necrosis. These advancements will help to optimize dose and fractionation for each patient's lesion based on tumor size, histology, systemic therapy, medical comorbidities/patient genetics, and tumor molecular features. This review will discuss the current state of brain directed radiation for brain metastasis. We will also discuss future directions to improve the precision of stereotactic radiosurgery and optimize whole brain radiation techniques to improve local tumor control and prevent cognitive decline without forming necrosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".