Challenges and Novel Opportunities of Radiation Therapy for Brain Metastases in Non-Small Cell Lung Cancer
Bibliographic record
Abstract
Approximately 20% patients with non-small cell lung cancer (NSCLC) present with CNS spread at the time of diagnosis and 25-50% are found to have brain metastases (BMs) during the course of the disease. The improvement in the diagnostic tools and screening, as well as the use of new systemic therapies have contributed to a more precise diagnosis and prolonged survival of lung cancer patients with more time for BMs development. In the past, most of the systemic therapies failed intracranially because of the inability to effectively cross the blood brain barrier. Some of the new targeted therapies, especially the group of tyrosine kinase inhibitors (TKIs) have shown durable CNS response. However, the use of ionizing radiation remains vital in the management of metastatic brain disease. Although a decrease in CNS-related deaths has been achieved over the past decade, many challenges arise from the need of multiple and repeated brain radiation treatments, which carry along not insignificant risks and toxicity. The combination of stereotactic radiotherapy and systemic treatments in terms of effectiveness and adverse effects, such as radionecrosis, remains a subject of ongoing investigation. This review discusses the challenges of the use of radiation therapy in NSCLC BMs in view of different systemic treatments such as chemotherapy, TKIs and immunotherapy. It also outlines the future perspectives and strategies for personalized BMs management.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".