TU‐C‐M100F‐03: MRI‐Controlled Transurethral Ultrasound Therapy for Prostate Cancer
Bibliographic record
Abstract
Purpose: To develop and test a transurethral ultrasound therapy system which uses quantitative real‐time MRI temperature feedback to control the shape of the coagulated region within the prostate while sparing surrounding structures from thermal damage. Method and Materials: An MRI‐compatible transurethral heating applicator comprised of planar ultrasound transducers that produce a directional heating pattern has been constructed. This device is rotated within a 1.5T MR imager to distribute energy to targeted regions of the prostate by an MRI‐compatible motor, concurrent with imaging. The region of the prostate to be treated is selected based on MR imaging information. Subsequent heating is controlled by MRI temperature images acquired every 5s and a complete prostate volume can be coagulated with a single rotation in about 20m. In‐vivo experiments have been performed in a canine model and the spatial accuracy of the coagulation patterns has been assessed using contrast‐enhanced MR images and a novel, quantitative whole‐mount histology technique with image registration to the MR temperature maps. Results: Sufficient spatial resolution and temperature accuracy can be obtained at 1.5T to provide accurate feedback control of the coagulation pattern within ±1.5mm of the targeted heating radius. Histological analysis indicates that, under these treatment conditions, the margin between completely coagulated tissue and apparently undamaged tissue is ⩽3mm in this acute assay. These histological boundaries, when registered carefully with the quantitative MRI temperature histories, provide a good estimate of the temperature threshold (54.6±1.7°C) for complete coagulation. The contrast‐enhanced MR images clearly show the coagulated region but register less accurately with the histological boundaries. Conclusion: Successful control of transurethral ultrasound therapy using quantitative, real‐time MRI temperature images has been demonstrated in vivo. This technology offers good potential for an effective treatment for localized prostate cancer with reduction in morbidity.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".