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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".