Urologist-Administered MRI-Guided Transurethral Ultrasound Ablation of the Prostate Using a Mobile Treatment Center: A Case Series
Bibliographic record
Abstract
Introduction: Transurethral ultrasound ablation (TULSA) relies on real-time MRI to precisely control the treatment of prostate cancer and avoid side effects. To overcome limited urologist access to MRI, we present a case series on the first use of a mobile TULSA treatment center. Methods: Clinical outcomes were retrospectively reviewed from our institutional database. A mobile MRI scanner was installed next to a surgical hospital, equipped with the ablation system and MRI-compatible anesthesia. Manufacturer testing confirmed that mobile TULSA continued to meet the requirements of fixed installations. Patients underwent preoperative assessment of candidacy for anesthesia and treatment in a mobile MRI setting. Positioning, anesthesia induction, and device placement were performed in the MRI scanner room. The intended ablation zone was delineated by the urologist using magnetic resonance images acquired with treatment devices in place. Ablation was controlled by the treatment software and monitored by the urologist using real-time MRI. A Foley catheter was placed for postoperative drainage; the patients awoke in the mobile MRI before transfer to postanesthesia care unit. Results: Six patients underwent TULSA in the mobile treatment unit, with no intraoperative complications. The median procedure time was 3.2 hours, with workflow improvements during equipment setup, patient positioning, and treatment planning. Contrast-enhanced images confirmed devascularization of the index lesion and successful nerve sparing. Six-month follow-up demonstrated promising PSA and MRI findings, and improvements in lower urinary tract symptoms. Conclusions: TULSA can be safely administered by urologists using a mobile MRI treatment center. Mobile MRI units could be used to expand patient access to TULSA.
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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 | 0.000 | 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 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".