Sci‐Sat AM(2): Brachy‐06: A Comparison of MR/CT fusion versus CT alone for assessment of implant quality in permanent prostate brachytherapy
Bibliographic record
Abstract
Low dose-rate permanent implant brachytherapy is widely used in the management of patients with early stage prostate cancer. An assessment of the implant quality is usually carried out 30 days after the implant is delivered, using computed tomography (CT) to identify the prostate and seeds. This is difficult due to poor contrast of the prostate and the superposition of seeds in the CT images. Magnetic resonance (MR) imaging offers superior contrast but inferior visualization of seeds. At our centre, patients are imaged using both CT and T2 weighted MR 30 days after an implant, and the image sets are fused using a commercial software package. The seeds are identified on CT and the prostate volumes are contoured on MR, with fusion performed by matching seeds on CT with seed signal voids on MR. The purpose of this study was to compare standard prostate post-implant dosimetric parameters (D90, V100, etc.) for prostates contoured on CT alone (MR blinded) versus MR/CT fusion. 25 patients were evaluated with all contouring performed by the same physician. We found that the prostate volume was overestimated using CT alone as compared to MR/CT fusion (mean: 37.2cc vs. 35.0cc respectively, p = 0.033). We also found that dosimetric parameters were underestimated for CT alone compared to MR/CT fusion, including D90 (mean: 144.3Gy vs. 150.8Gy respectively, p = 0.005) and V100 (mean: 89.2% vs. 91.0% respectively, p = 0.01). Centres using CT alone for post-implant dosimetry may therefore be underestimating their implant quality.
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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 | 0.002 | 0.004 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".