Impact of intraoperative MRI/TRUS fusion on dosimetric parameters in cT3a prostate cancer patients treated with high-dose-rate real-time brachytherapy
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to evaluate the impact of intraoperative MRI/TRUS fusion procedure in cT3a prostate cancer patients treated with high-dose-rate (HDR) real-time brachytherapy. MATERIAL AND METHODS: Prostate gland, dominant intraprostatic lesions (DILs), and extracapsular extension (ECE) were delineated in the pre-brachytherapy magnetic resonance images (MRI) of 9 consecutive patients. The pre-implant P-CTVUS (prostate clinical target volume) was defined as the prostate seen in the transrectal ultrasound (TRUS) images. The CTVMR includedthe prostate with the ECE image (ECE-CTV) as defined on the MRI. Two virtual treatment plans were performed based on the MRI/TRUS fusion images, the first one prescribing 100% of the dose to the P-PTVUS, and the second prescribing to the PTVMR. The implant parameters and dose-volume histogram (DVH) related parameters of the prostate, OARs, and ECE were compared between both plans. RESULTS: Mean radial distance of ECE was 3.6 mm (SD: 1.1). No significant differences were found between prostate V100, V150, V200, and OARs DVH-related parameters between the plans. Mean values of ECE V100, V150, and V200 were 85.9% (SD: 15.1), 18.2% (SD: 17.3), and 5.85% (SD: 7) when the doses were prescribed to the PTVUS, whereas ECE V100, V150, and V200 were 99.3% (SD: 1.2), 45.8% (SD: 22.4), and 19.6% (SD: 12.6) when doses were prescribed to PTVMR (p = 0.028, p = 0.002 and p = 0.004, respectively). CONCLUSIONS: TRUS/MRI fusion provides important information for prostate brachytherapy, allowing for better coverage and higher doses to extracapsular disease in patients with clinical stage T3a.
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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 | 0.000 |
| 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) | 0.001 | 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".