Vertebral Compression Fracture After Spine Stereotactic Body Radiotherapy: A Multi-Institutional Analysis With a Focus on Radiation Dose and the Spinal Instability Neoplastic Score
Bibliographic record
Abstract
PURPOSE: Vertebral compression fracture (VCF) is increasingly recognized as an adverse event after spine stereotactic body radiotherapy (SBRT). We report a multi-institutional study aimed at clarifying the risk and predictive factors associated with VCF. PATIENTS AND METHODS: A total of 252 patients with 410 spinal segments treated with SBRT were included. The primary outcome was the development of VCF (a new VCF or progression of a baseline VCF). In addition to various patient-, treatment-, and tumor-specific factors, the Spinal Instability Neoplastic Scoring (SINS) system was applied to determine predictive value. RESULTS: The median follow-up was 11.5 months (range, 0.03 to 113 months). The median and mean overall survival rates were 16 and 26 months, respectively. We observed 57 fractures (57 of 410, 14%), with 47% (27 of 57) new fractures and 53% (30 of 57) fracture progression. The median time to VCF was 2.46 months (range, 0.03 to 43.01 months), and 65% occurred within the first 4 months. The 1- and 2-year cumulative incidences of fracture were 12.35% and 13.49%, respectively. Multivariable analysis identified dose per fraction (greatest risk for ≥ 24 Gy v 20 to 23 Gy v ≤ 19 Gy), in addition to three of the six original SINS criteria: baseline VCF, lytic tumor, and spinal deformity, as significant predictors of VCF. CONCLUSION: Caution must be observed when treating with ≥ 20 Gy/fraction, in particular, for patients with lytic tumor, spinal misalignment, and a baseline VCF. Frequent short-term follow-up is required, as nearly two thirds of all VCF occurred within the first 4 months. We also conclude that SINS may have utility in predicting patients at high risk of SBRT-induced VCF.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| 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".