Neoadjuvant versus Concurrent Androgen Deprivation Therapy in Localized Prostate Cancer Treated with Radiotherapy: A Systematic Review of the Literature
Bibliographic record
Abstract
BACKGROUND: There is an ongoing debate on the optimal sequencing of androgen deprivation therapy (ADT) and radiotherapy (RT) in patients with localized prostate cancer (PCa). Recent data favors concurrent ADT and RT over the neoadjuvant approach. METHODS: We conducted a systematic review in PubMed, EMBASE, and Cochrane Databases assessing the combination and optimal sequencing of ADT and RT for Intermediate-Risk (IR) and High-Risk (HR) PCa. FINDINGS: Twenty randomized control trials, one abstract, one individual patient data meta-analysis, and two retrospective studies were selected. HR PCa patients had improved survival outcomes with RT and ADT, particularly when a long-course Neoadjuvant-Concurrent-Adjuvant ADT was used. This benefit was seen in IR PCa when adding short-course ADT, although less consistently. The best available evidence indicates that concurrent over neoadjuvant sequencing is associated with better metastases-free survival at 15 years. Although most patients had IR PCa, HR participants may have been undertreated with short-course ADT and the absence of pelvic RT. Conversely, retrospective data suggests a survival benefit when using the neoadjuvant approach in HR PCa patients. INTERPRETATION: The available literature supports concurrent ADT and RT initiation for IR PCa. Neoadjuvant-concurrent-adjuvant sequencing should remain the standard approach for HR PCa and is an option for IR PCa.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".