Predictors of the response of cystic brain metastases to gamma knife radiosurgery
Bibliographic record
Abstract
Background: Gamma knife radiosurgery (GKR) is an effective treatment modality for local control of brain metastases. The predictors of response of cystic brain metastases (CBM) to GKR is not well understood. To measure progression and determine treatment prognostic factors, we quantified the percentage cystic and solid components of brain metastases before and after GKR treatment. Methods: 71 patients with CBM treated with GKR from 2006 to 2010 were selected from our institution’s database. Volumetric analysis was performed on MRIs done on treatment date and the latest MRI. Clinical data and dosimetry parameters were reviewed to identify factors that predicted a response of cystic component and overall tumour control. Results: Metastatic lesions from the lung had significantly larger cystic components (by volume) prior to GKR than metastasis of colorectal origin (p=0.039), and also had significantly larger cystic/total ratios than metastases from the breast (p=0.023). Post-treatment, a trend of >25% improvement in both cystic and solid components of tumours was seen in lung primaries (p=0.239). Metastatic brain tumours of colorectal origin demonstrated the best treatment response of the cystic component. Conclusion: The primary cancer pathology of the CBM has an effect on the response to GKR, and can be used as a prognosticator of changes in cystic and solid volumes of lesions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".