Simulation of MRI-Guided Transurethral Conformal 3-D Ultrasound Therapy of the Prostate
Bibliographic record
Abstract
The capability of MRI to measure spatial heating patterns during therapy delivery with ultrasound makes adaptive thermal therapy possible. Active feedback provided by MR thermometry enables on‐line adjustment of the treatment to compensate for tissue/perfusion changes during heating. The feasibility of performing 3‐D conformal thermal therapy of the entire prostate gland with a multi‐element transurethral ultrasound heating applicator was considered in this study. The major challenge was using MR temperature feedback to adjust simultaneously the device’s rate of rotation and the power and frequency of multiple independent ultrasound transducers, to shape the region of thermal damage to the prostate gland in all spatial dimensions while sparing surrounding tissues from damage. The 3‐D Bioheat Transfer Equation was used to model the ultrasound therapy using manually segmented MRI prostate geometries from 20 prostate cancer patients. Average prostate dimensions (±SD) were: length: 37.8±7.2 mm, width: 47.1±5.5 mm, height: 28.9±5.7 mm. Typical treatments of the entire prostate volume take less than 30 min. Results from various treatment strategies were compared by calculating the percentage volume of under‐ and over‐treated tissue and the potential thermal damage incurred by important adjacent anatomical structures using “dose‐effect” curves. Visualization tools were developed to investigate patient‐specific prostate and periprostatic anatomy, as well as the simulated coagulated volumes in 3‐D, enabling evaluation of individual patient outcomes. These simulations also enabled the investigation of the number and size of transducer segments required for accurate treatment delivery. In general, the under‐treated fraction can be maintained below 1% of the prostate volume, but the over‐treated fraction can range up to 15%, emphasizing the importance of accurate location of sensitive adjacent structures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".