Focal application of low-dose-rate brachytherapy for prostate cancer: a pilot study
Bibliographic record
Abstract
PURPOSE: To evaluate the feasibility and to report the early outcomes of focal treatment of prostate cancer using low-dose-rate brachytherapy (LDR-PB). MATERIAL AND METHODS: Seventeen patients were screened with multi-parametric magnetic resonance imaging (mpMRI), 14 of whom proceeded to receive trans-perineal template mapping biopsy (TTMB). Focal LDR-PB was performed on five eligible patients using dual air kerma strength treatment plans based on planning target volumes derived from cancer locations and determined by TTMB. Patient follow-up includes prostate specific antigen (PSA) measurements, urinary and sexual function questionnaires, repeated imaging and TTMB at specific intervals post-treatment. RESULTS: Feasibility of focal LDR-PB was shown and short-term outcomes are promising. While the detection rate of tumors, a majority of which were low grade GS 3 + 3, was found to be low on mpMRI (sensitivity of 37.5%), our results suggest the potential of mpMRI in detecting the presence of higher grade (GS ≥ 3 + 4), and bilateral disease indicating its usefulness as a screening tool for focal LDR-PB. CONCLUSIONS: Low-dose-rate brachytherapy is a favorable ablation option for focal treatment of prostate cancer, requiring minimal modification to the standard (whole gland) LDR-PB treatment, and appears to have a more favorable side effect profile. Further investigation, in the form of a larger study, is needed to assess the methods used and the long-term outcomes of focal LDR-PB.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.003 | 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".