Feasibility and safety assessment of magnetic resonance-guided high-intensity focused ultrasound (MRgHIFU)-mediated mild hyperthermia in pelvic targets evaluated using an <i>in vivo</i> porcine model
Bibliographic record
Abstract
Purpose: To evaluate the feasibility and assess safety parameters of magnetic resonance-guided high-intensity focused ultrasound (MRgHIFU)-mediated hyperthermia (HT; heating to 40–45 °C) in various pelvic targets in a porcine model in vivo.Methods: Thirteen HT treatments were performed in six pigs with a commercial MRgHIFU system (Sonalleve V2, Profound Medical Inc., Mississauga, Canada) to muscle adjacent to the ventral/dorsal bladder wall and uterus to administer 42 °C (±1°) for 30 min (±5%) using an 18-mm target diameter and 100 W power. Feasibility was assessed using accuracy, uniformity, and MR-thermometry performance-based metrics. Safety parameters were assessed for tissues in the targets and beam-path by contrast-enhanced MRI, gross-pathology and histopathology.Results: Across all HT sessions, the mean difference between average temperature (Tavg) and the target temperature within the target region-of-interest (tROI, the cross-section of the heated volume at focal depth) was 0.51 ± 0.33 °C. Within the tROI, the temperature standard deviation averaged 1.55 ± 0.31 °C, the average 30-min Tavg variation was 0.80 ± 0.17 °C, and the maximum difference between Tavg and the 10th- or 90th-percentile temperature averaged 2.01 ± 0.44 °C. The average time to reach ≥41 °C and cool to ≤40 °C within the tROI at the beginning and end of treatment was 47.25 ± 27.47 s and 66.37 ± 62.68 s, respectively. Compared to unheated controls, no abnormally-perfused tissue or permanent damage was evident in the MR images, gross pathology or histological analysis.Conclusions: MRgHIFU-mediated HT is feasible and safety assessment is satisfactory for treating an array of clinically-mimicking pelvic geometries in a porcine model in vivo, implying the technique may have utility in treating pelvic targets in human patients.
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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 | 0.001 |
| 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 | 0.000 | 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".