Early Experience With Two-Fraction Spine Stereotactic Body Radiation Therapy in Treating Spinal Metastases
Bibliographic record
Abstract
OBJECTIVES: Spinal metastases are common in metastatic cancer, affecting around 40% of patients. The primary treatment involves radiation therapy, transitioning from conventional external beam radiation therapy (EBRT) to stereotactic body radiation therapy (SBRT) for its superior, durable response. While spine SBRT has gained popularity in the United States, Level I evidence supporting it over EBRT is limited to a Canadian trial using a 2-fraction SBRT regimen. We present our findings from one of the earliest US experiences of 2-fraction spine SBRT for spinal metastases. METHODS: A retrospective analysis of patients with metastatic spine cancer receiving 2-fraction spine SBRT at a single center was conducted. Patients received treatment based on Level 1 evidence (24 Gy in 2 fractions). Follow-up records were assessed for local control outcomes and toxicity. RESULTS: Twenty patients since August 2022 have been treated with 2-fraction spine SBRT. Most patients were treated at 1 (45%) or 2 (40%) spinal levels, with the thoracic (55%) and lumbar (50%) spine being the most common locations. Common primary sites included the lung (30%), breast (20%), esophagus (15%), and prostate (10%). The rate of local control was 100%, while the rate of vertebral compression fracture was 15%. No esophageal or bowel toxicity occurred, and no fractures required intervention. CONCLUSIONS: These findings suggest that 2-fraction spine SBRT is safe and effective, consistent with existing Level I evidence. Our local control rate exceeding 95% aligns with the literature, indicating the feasibility and achievability of implementing this approach in the United States over a short period of time.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".