Local failure, distant metastasis, and survival after definitive radiotherapy for intermediate- and high-risk prostate cancer: An individual patient-level meta-analysis of 18 randomized trials.
Bibliographic record
Abstract
277 Background: The prognostic importance of local failure (LF) after definitive radiotherapy (RT) in patients with NCCN intermediate- (IRPCa) and high-risk prostate cancer (HRPCa) remains unclear, particularly given the likelihood that occult distant metastases (DMs) at presentation may be the true driver of natural history. Here, we leverage individual patient data (IPD) from 18 randomized control trials (RCTs) to evaluate the prognostic impact of LF and the kinetics of DM after RT. Methods: IPD for 18 RCTs were obtained from the Meta-Analysis of Randomized trials in Cancer of the Prostate (MARCAP) Consortium, comprising a total of 12533 patients (6288 HRPCa & 6245 IRPCa). Multivariable Cox proportional hazards (PH) models were developed to evaluate the relationship between overall survival (OS), PCa-specific survival (PCSS), DM-free survival (DMFS) & LF as a time-dependent covariate, adjusted for clinicodemographic parameters. Markov PH models, defined via transitions between 4 states, were developed to evaluate the aforementioned relationship. Proportional hazards assumption was imposed and examined for both models. Time is from randomization. Results: Median follow-up was 9.1 years. There were 795 (13%) LF & 1288 (21%) DM events for patients with HRPCa; these numbers were 449 (7%) & 451 (7%) for IRPCa. For HRPCa & IRPCa, 81% and 81% of DMs developed from a clinically relapse-free state (cRFS), with a median time of 46 and 60 months, respectively (p < 0.0001). 39% & 13% of DM events occurred within 2 years after RT for HRPCa & IRPCa, respectively. At later time points, DM events were more likely to emerge after an LF event for both HRPCa (9% vs. 34% between 0-2 vs. 8-10 years post-RT, p = 0.001) and IRPCa (10% vs. 34% between 0-2 vs. 8-10 years post-RT, p = 0.008). LF was significantly associated with OS (hazard ratio [HR] 1.17, 95% confidence interval [CI] 1.06–1.30), PCSS (HR 2.02, 95% CI 1.75-2.33) & DMFS (HR 1.94, 95% CI 1.75–2.15) (p < 0.01 for all) in patients with HRPCa. LF was also significantly associated with DMFS (HR 1.57, 95% CI 1.36–1.81) but not OS in patients with IRPCa. Patients who had not transitioned to the LF state had a significantly lower HR of transitioning to a PCa-specific death state than those who transitioned to the LF state (HR 0.32, 95% CI 0.21–0.50, p < 0.001). Conclusions: LF is an independent prognosticator of OS, PCSS & DMFS in HRPCa and of DMFS in IRPCa. The predominant mode of DM development is from the cRFS state, underscoring the importance of accurate upfront staging & systemic therapy. However, particularly at late time points, an increasing proportion of DM events originated after diagnosis of a LF, constituting a “second wave” of DM events. This suggests that optimizing local control is also important, though the majority of DM events appear prior to a clinically-detected LF.
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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 | 0.024 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.045 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".