Spine Stereotactic Body Radiotherapy for Prostate Cancer Metastases and the Impact of Hormone Sensitivity Status on Local Control
Bibliographic record
Abstract
BACKGROUND: Stereotactic body radiotherapy (SBRT) is used to deliver ablative dose of radiation to spinal metastases. OBJECTIVE: To report the first dedicated series of spine SBRT specific to prostate cancer (PCa) metastases with outcomes reported according to hormone sensitivity status. METHODS: A prospective database was reviewed identifying patients with PCa treated with spine SBRT. This included those with hormone-sensitive PCa (HSPC) and castrate-resistant PCa (CRPC). The primary end point was MRI-based local control (LC). RESULTS: A total of 183 spine segments in 93 patients were identified; 146 segments had no prior radiation and 37 had been previously radiated; 27 segments were postoperative. The median follow-up was 31 months. At the time of SBRT, 50 patients had HSPC and the remaining 43 had CRPC. The most common fractionation scheme was 24-28 Gy in 2 SBRT fractions (76%). LC rates at 1 and 2 years were 99% and 95% and 94% and 78% for the HSPC and CRPC cohorts, respectively. For patients treated with de novo SBRT, a higher risk of local failure was observed in patients with CRPC (P = .0425). The 1-year and 2-year overall survival rates were significantly longer at 98% and 95% in the HSPC cohort compared with 79% and 65% in the CRPC cohort (P = .0005). The cumulative risk of vertebral compression fracture at 2 years was 10%. CONCLUSION: Favorable LC rates were observed after spine SBRT for PCa metastases; strategies to improve long-term LC in patients with CRPC require further investigation.
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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 | 0.002 |
| 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".