Does neoadjuvant hormone therapy improve outcome in prostate cancer patients receiving radiotherapy after radical prostatectomy?
Bibliographic record
Abstract
PURPOSE: To assess outcome and predictive factors in men with prostate cancer who receive post radical prostatectomy (RP) radiotherapy (RT) either in the adjuvant or salvage setting, with or without neoadjuvant androgen deprivation therapy (NADT). METHODS: A retrospective analysis was performed on 175 patients with clinically localized prostate cancer treated with RP who subsequently received RT (dose range 50 Gy-68 Gy). Twenty-two patients received adjuvant RT (ART), 57 received NADT + ART, 15 received salvage RT (SRT), and 81 received NADT + SRT. Outcome was assessed by biochemical disease free survival (BDFS), prostate cancer specific survival and overall survival (OS). RESULTS: Although BDFS favored patients who received NADT with 5 year rates of 67%, 80%, 27% and 62% for the ART, NADT + ART, SRT, and NADT + SRT groups respectively; this was not a significant predictor on multivariable analysis. Significant independent predictive factors of improved BDFS were pre-RT PSA < or = 0.2 ng/ml, low Gleason score and positive surgical margins. Age and Gleason score were independent predictors of OS. CONCLUSIONS: Pre-RT PSA is an important predictor of outcome. NADT appears to benefit patients who presented with a pre-RT PSA > 0.2 ng/ml, particularly for patients receiving SRT. NADT can be considered for patients receiving RT after RP who present with a high pre-RT PSA but may not be necessary for patients without. Results of ongoing randomized studies such as RADICALS will also help clarify the role of hormone therapy in conjunction with RT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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".