Clinical outcomes following stereotactic radiosurgery for brain metastases from sarcoma primaries: An international multicenter analysis
Bibliographic record
Abstract
BACKGROUND: There is a paucity of data on treatment outcomes following stereotactic radiosurgery (SRS) for brain metastases from sarcoma primaries. METHODS: The International Radiosurgery Research Foundation member-sites were queried for patients with brain metastases from sarcoma primaries treated with SRS. Overall survival (OS) and local control (LC) were calculated via Kaplan-Meier analysis. Univariate analyses examined prognostic factors associated with LC and OS via log-rank t-tests and multivariate analyses (MVA) via Cox proportional hazards model. RESULTS: A total of 146 patients with 309 brain metastases were identified. Two-hundred and thirty lesions were treated with single-fraction SRS with a median dose of 20 Gy (15-24 Gy). Ninety-five patients had extracranial metastases, including 75 oligometastatic patients. One- and 2-year OS and LC rates were 47.7% and 37.3%, and 78.3% and 62.2%, respectively. On univariate analyses, superior 1-year OS was noted among leiomyosarcomas (69.7% vs. 42.6%; p = .02) with poorer outcomes among pleomorphic histologies (10.5% vs. 50.7%; p = .002). Pleomorphic histologies were associated with poorer OS on MVA (hazard ratio [HR], 3.13; p = .006). On MVA, LC was inferior among patients of age ≥45 years (HR, 3.78; p < .001) and superior among leiomyosarcomas (HR, 0.31; p = .03). OS was prognosticated based on adverse factors (ie, nonleiomyosarcoma histology and progressive extracranial metastases). Two-year OS for patients with and without adverse features were 78.6% and 31.5%, respectively. CONCLUSIONS: LC outcomes were driven by histology and age with superior LC among leiomyosarcomas and patients of age <45 years. OS was driven by nonleiomyosarcoma histology and the presence of progressive extracranial disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".