F.02 Towards the complete control of brain metastases using surveillance screening and stereotactic radiosurgery
Bibliographic record
Abstract
Background: The incidence of brain metastases is increasing with the development of improved systemic therapies with limited impact on intracranial disease. The purpose of this study was to determine if there is a threshold tumor size below which local control (LC) rates approach 100% after stereotactic radiosurgery (SRS). Methods: 200 patients with 1237 tumors were identified from a prospective registry of patients having undergone SRS between 2012-2014. Histology consisted predominantly of non-small cell lung cancer (NSCLC), melanoma and breast cancer. Results: The median tumor size was 6 mm in diameter or 70 mm3 and most commonly NSCLC. Thirty-three tumors had local progression at a median time of 8.8 months. The 1- and 2-year actuarial LC for all tumors were 97% and 93%. LC of 100% was seen for intracranial metastases less than 100 mm3 or 6 mm in diameter, independent of histology. Total tumor volume was an independent predictor of overall survival, after adjusting for age, KPS and extracranial disease status. Conclusions: SRS can achieve LC rates approaching 100% for subcentimeter metastases. The earlier detection and prompt treatment of small intracranial metastases may prevent the development of neurological symptoms, the need for surgical resection, and potentially improve overall survival. The results of this study would favour the implementation of routine staging MRIs.
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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 | 0.004 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".