High intensity focused ultrasound periosteal ablation in an animal model: potential for palliation of bone metastases
Bibliographic record
Abstract
PURPOSE: Radiotherapy is standard-of-care for painful bone metastases yet has limitations and associated side effects. Ablation of sensory nerves endings along the periosteum with magnetic resonance imaging guided high intensity focused ultrasound (MRgFUS) proved safe and clinically effective for pain relief in patients with bone metastases, received FDA and CE approval, but has not gained widespread adoption due to significant cost and procedural and logistical complexity. This preclinical study evaluated the safety and feasibility of a fluoroscopy-guided high intensity focused ultrasound platform to ablate a targeted region along the surface of bones. METHODS: Two healthy adult pigs received 6 kJ to 10 kJ sonications to the femur, ileum, and ribs. Animals were followed-up for 3 months. Longitudinal clinical observation and follow up magnetic resonance imaging (MRI), and computed tomography (CT) scans were performed. After sacrifice, the targeted bone and adjacent tissues were sent for histopathological evaluation to confirm thermal ablation. RESULTS: Clinical observations revealed no neurological or musculoskeletal deficits. MRI scans on day 5 demonstrated robust ablation in all targeted sites. At 12 weeks, CT scans and histopathological evaluation showed complete healing of ablated regions. CONCLUSIONS: Periosteal bone ablation using the Neurolyser XR in a healthy porcine model is feasible with no adverse events, and no radiological or histological evidence of lasting injury or fracture to the targeted bones.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".