Single-fraction high-dose-rate brachytherapy using real-time transrectal ultrasound based planning in combination with external beam radiotherapy for prostate cancer: dosimetrics and early clinical results
Bibliographic record
Abstract
PURPOSE: To validate the feasibility of a single-fraction high-dose-rate brachytherapy (HDRBT) boost for prostate cancer using real-time transrectal ultrasound (TRUS) based planning. MATERIAL AND METHODS: From August 2012 to September 2015, 126 patients underwent a single-fraction HDRBT boost of 15 Gy using real-time TRUS based planning. External beam radiation therapy (EBRT) (37.5 Gy/15 fractions, 44 Gy/22 fractions, or 45 Gy/25 fractions) was performed before (31%) or after (69%) HDRBT boost. Genito-urinary (GU) and gastro-intestinal (GI) toxicity were assessed 4 and 12 months after the end of combined treatment using the international prostate symptom score scale (IPSS) and the common terminology criteria for adverse events (CTCAE) v3.0. RESULTS: All dose-planning objectives were achieved in 90% of patients. Prostate D90 ≥ 105% and ≤ 115% was achieved in 99% of patients, prostate V150 ≤ 40% in 99%, prostate V200 < 11% in 96%, urethra D10 < 120% for 99%, urethra V125 = 0% in 100%, and rectal V75 < 1 cc in 93% of patients. Median IPSS score was 4 at baseline and did not change at 4 and 12 months after combined treatment. No patients developed ≥ grade 2 GI toxicity. With a median follow-up of 10 months, only two patients experienced biochemical failure. Among patients who didn't receive ADT, cumulative percentage of patients with PSA ≤ 1 ng/ml at 4 and 18 months was respectively 23% and 66%. CONCLUSIONS: Single-fraction HDRBT boost of 15 Gy using real-time TRUS based planning achieves consistently high dosimetry quality. In combination with EBRT, toxicity outcomes appear promising. A longer follow-up is needed to assess long-term outcome and toxicities.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".