Image-Guided Energy Ablation for Palliation of Painful Bony Metastases—A Systematic Review
Bibliographic record
Abstract
PURPOSE: To analyze the effectiveness of image-guided energy ablation techniques with and without concurrent therapies in providing palliative pain relief in patients with bone metastases. MATERIALS AND METHODS: Ovid Embase, Ovid Medline, and Pubmed were searched from inception to April 14, 2023, using search terms related to bone lesions and MeSH terms regarding ablation therapy. English peer-reviewed primary articles were included that reported pain scores following image-guided energy-based ablation of bone metastases. Exclusion criteria included nonpalliative treatment, pain scores associated with specific treatment modalities not reported, and nonmetastatic bone lesions. Mean percentage reduction in pain score was calculated. RESULTS: Of the 1,396 studies screened, 54 were included. All but 1 study demonstrated decreased pain scores at final follow-up. Mean reductions in pain scores at final follow-up were 49% for radiofrequency (RF) ablation, 58% for RF ablation and adjunct, 54% for cryoablation (CA), 72% for cryoablation and adjunct (CA-A), 48% for microwave ablation (MWA), 81% for microwave ablation and adjunct (MWA-A), and 64% for high-intensity focused ultrasound (US). Postprocedural adverse event rates were 4.9% for RF ablation, 34.8% for RF ablation and adjunct, 9.6% for CA, 12.0% for CA-A, 48.9% for MWA, 33.5% for MWA-A, and 17.0% for high-intensity focused US. CONCLUSIONS: Image-guided energy ablation demonstrated consistently strong reduction in pain across all modalities, with variable postprocedural adverse event rates. Owing to heterogeneity of included studies, quantitative analysis was not appropriate. Future primary research should focus on creating consistent prospective studies with established statistical power, explicit documentation, and comparison with other techniques.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".