Ultrasound–CT fusion compared with MR–CT fusion for postimplant dosimetry in permanent prostate brachytherapy
Bibliographic record
Abstract
PURPOSE: Postplan evaluation is essential for quality assurance in prostate brachytherapy. MRI has demonstrated greater interobserver consistency in prostate contouring compared with CT. Although a valuable tool in postimplant assessment, MRI is costly and not always available. Our purpose is to compare dosimetry obtained using fusion of postimplant CT with preimplant transrectal ultrasound (TRUS) vs. CT-MR fusion. METHODS AND MATERIALS: Twenty patients receiving permanent (125)I seed prostate brachytherapy underwent preimplant TRUS with urethrography, 1-month CT with a Foley catheter, and 1-month MRI. No patient received androgen deprivation therapy or external beam radiotherapy. The prescription dose of (125)I implant monotherapy was 144Gy. The preimplant TRUS and postimplant CT images were fused based on urethral position, and the CT-TRUS images were subsequently fused to the MRI using a seed-to-seed match. Dosimetric parameters for the ultrasound- and MR-derived prostate were compared. RESULTS: The mean absolute difference between dosimetry from MRI or CT-TRUS fusion for D(90) was 3.2% and in V(100) was 1.2%. Only 1 patient had a difference in MR- and ultrasound-derived D(90) of more than 10% (11.4%) and only 1 had a difference in V(100) of more than 5%. CONCLUSIONS: Fusion of preimplant TRUS with 1-month postimplant CT appears to lead to acceptable agreement with MR-based dosimetric parameters in postplan evaluation. TRUS-based volumes may be a reasonable alternative to MRI in settings where MRI is not available.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".