Hyperthermia in Bone Generated with MR Imaging–controlled Focused Ultrasound: Control Strategies and Drug Delivery
Bibliographic record
Abstract
Purpose To evaluate the feasibility of achieving image-guided drug delivery in bone by using magnetic resonance (MR) imaging–controlled focused ultrasound hyperthermia and temperature-sensitive liposomes. Materials and Methods Experiments were approved by the institutional animal care committee. Hyperthermia (43°C, 20 minutes) was generated in 10-mm-diameter regions at a muscle-bone interface in nine rabbit thighs by using focused ultrasound under closed-loop temperature control with MR thermometry. Thermosensitive liposomal doxorubicin was administered systemically during heating. Heating uniformity and drug delivery were evaluated for control strategies with the temperature control image centered 10 mm (four rabbits) or 0 mm (five rabbits) from the bone. Simulations estimated temperature elevations in bone. Drug delivery was quantified by using the fluorescence of doxorubicin extracted from bone marrow and muscle and was compared between treated and untreated thighs by using the one-sided Wilcoxon signed rank test. Results With ultrasound focus and MR temperature control plane 0 mm and 10 mm from the bone interface, average target region temperatures were 43.1°C and 43.3°C, respectively; numerically estimated bone temperatures were 46.8°C and 78.1°C. The 10-mm offset resulted in thermal ablation; numerically estimated muscle temperature was 66.1°C at the bone interface. Significant increases in doxorubicin concentration occurred in heated versus unheated marrow (8.2-fold, P = .002) and muscle (16.8-fold, P = .002). Enhancement occurred for 0- and 10-mm offsets, which suggests localized drug delivery in bone is possible with both hyperthermia and thermal ablation. Conclusion MR imaging–controlled focused ultrasound can achieve localized hyperthermia in bone for image-guided drug delivery in bone with temperature-sensitive drug carriers. © RSNA, 2012 Supplemental material: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.11111189/-/DC1
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".