Spine stereotactic body radiotherapy for renal cell cancer spinal metastases: analysis of outcomes and risk of vertebral compression fracture
Bibliographic record
Abstract
OBJECT: The aim of this study was to evaluate local control (LC) and the risk of vertebral compression fracture (VCF) after stereotactic body radiotherapy (SBRT) in patients with renal cell cancer spinal metastases. METHODS: Prospectively collected data on 71 spinal segments treated with SBRT in 37 patients were reviewed. The median follow-up was 12.3 months (range 1.2-55.4 months). The LC rate was assessed based on each spinal segment treated and overall survival (OS) according to each patient treated. Sixty of 71 segments (85%) were radiation naive, 11 of 71 (15%) were previously irradiated, and 10 of 71 (14%) were treated with postoperative SBRT. The median SBRT total dose and number of fractions were 24 Gy and 2, respectively. The VCF analysis also included evaluation of the Spinal Instability Neoplastic Score criteria. RESULTS: The 1-year OS and LC rates were 64% and 83%, respectively. Multivariate analysis identified oligometastatic disease (13 of 37 patients) as a positive prognostic factor (p = 0.018) for OS. Of 61 non-postoperative spinal segments treated, 10 (16%) developed VCFs; 3 of 10 were de novo VCFs and 7 of 10 occurred as progression of an existing VCF. The 1-year VCF-free probability rate was 82%. Multivariate analysis identified single-fraction SBRT and baseline VCF as significant predictors of SBRT-induced VCF (p = 0.028 and p = 0.012, respectively). CONCLUSIONS: Spine SBRT yields high rates of local tumor control in patients with renal cell cancer. Baseline VCF and 18-24 Gy delivered in a single fraction were predictive of further collapse. Patients with oligometastatic disease may benefit most from such aggressive local therapy, given the prolonged survival observed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".