Using adjuvant radiotherapy to improve cancer-specific survival in patients with highly aggressive prostate cancer: Examining recently released criteria.
Bibliographic record
Abstract
30 Background: Although adjuvant radiotherapy (aRT) after radical prostatectomy (RP) improves biochemical recurrence (BCR)-free survival rates, its effect on cancer-specific mortality (CSM) in patients with prostate cancer (PCa) is still controversial. The aim of our study was to test the effect of aRT on CSM according to a risk score based on the number and nature of adverse pathological characteristics (Gleason score 8-10; pT3b/4, lymph node invasion [LNI]). Methods: Overall, 7,616 patients with pT3/4 N0/1 PCa treated with RP between 1995 and 2009 within the Surveillance Epidemiology and End Results Medicare-linked database were included in the study. Patients were stratified according to the risk score (less than 2 vs. 2 or more adverse characteristics), and the impact of aRT on CSM was examined in each sub-group. Additionally, to evaluate the effectiveness of aRT, we calculated the number needed to treat (NNT), defined as the average number of patients who must be treated to prevent one detrimental outcome. Subsequently, competing-risks regression models were used to test the effect of aRT on CSM rates in the overall population and after stratifying patients according to their risk score (less than 2 vs. 2 or more). Results: The risk score was associated with increasing 10-year CSM rates (P<0.001). When focusing on patients with a risk score 2 or more, 10-year CSM rates were significantly lower for individuals undergoing aRT compared to their counterpart not receiving aRT (6.9 vs. 16.2%, respectively; P=0.002). The corresponding NNT to prevent one death from PCa was 10. Adjuvant RT was not associated with lower CSM rates overall and in patients with a risk score less than 2. This was confirmed in multivariable analyses, where aRT decreased the risk of CSM only in patients with a risk score 2 or more (P≤0.02). Conclusions: Our findings confirm the validity of the previously reported risk score in selecting the most optimal candidates for aRT after surgery in a large contemporary population-based cohort of patients with pT3/4 N0/1 PCa. Patients with two or more adverse pathological characteristics at RP might benefit the most from aRT in terms of reduced CSM.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".