Neoadjuvant Androgen Receptor Signaling Inhibitors before Radical Prostatectomy for Non-Metastatic Advanced Prostate Cancer: A Systematic Review
Bibliographic record
Abstract
(1) Background: Several phase II studies, including randomized controlled trials (RCTs), assessed the efficacy of adding androgen receptor signaling inhibitors (ARSIs) to androgen deprivation therapy (ADT) as a neoadjuvant treatment in patients treated with radical prostatectomy (RP) for prostate cancer (PCa). Summarizing the early results of these studies could help in designing phase III trials and patient counseling. (2) Methods: We queried three databases in January 2023 for studies that included PCa patients treated with neoadjuvant ARSI-based combination therapy before RP. The outcomes of interest were oncologic outcomes and pathologic responses, such as pathologic complete response (pCR) and minimal residual disease (MRD). (3) Results: Overall, twenty studies (eight RCTs) were included in this systematic review. Compared to ADT or ARSI alone, ARSI + ADT was associated with higher pCR and MRD rates; this effect was less evident when adding a second ARSI or chemotherapy. Nevertheless, ARSI + ADT resulted in relatively low pCR rates (0-13%) with a high proportion of ypT3 (48-90%) in the resected specimen. PTEN loss, ERG positive, or intraductal carcinoma seem to be associated with worse pathologic response. One study that adjusted for the effects of possible confounders reported that neoadjuvant ARSI + ADT improved time to biochemical recurrence and metastasis-free survival compared to RP alone. (4) Conclusions: Neoadjuvant ARSI + ADT combination therapy results in improved pathologic response compared to either alone or none in patients with non-metastatic advanced PCa. Ongoing phase III RCTs with long-term oncologic outcomes, as well as biomarker-guided studies, will clarify the indication, oncologic benefits, and adverse events of ARSI + ADT in patients with clinically and biologically aggressive 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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".