Effect of external beam radiotherapy on second primary cancer risk after radical prostatectomy
Bibliographic record
Abstract
INTRODUCTION: We aimed to investigate the effect of radiotherapy (RT) in contemporary patients treated with radical prostatectomy (RP) compared to RP alone for non-metastatic prostate cancer (PCa) on the incidence of second primary cancers (SPCs). METHODS: Within the Surveillance, Epidemiology, and End Results (SEER) database (2004-2015), we identified patients with PCa as the only or first primary cancer, who underwent RP and RT or RP alone. Cumulative incidence plots and multivariable Cox regression models tested for SPC rate differences according to treatment type: RP and RT vs. RP alone. Subgroup analyses focused on pelvic, primary pelvic, and non-pelvic SPCs, as well as on late SPCs (>5 years after PCa diagnosis). RESULTS: Of 152 161 patients, 7.1% (n=10 870) received RP and RT. Overall, 6.6 vs. 5.0% developed SPCs after RP and RT vs. RP alone, respectively (p<0.001). Cumulative incidence rates at 10 years after PCa diagnosis for RP and RT vs. RP were 12.0 vs. 8.7% (p<0.001), 2.0 vs. 1.2% (p<0.001), 2.1 vs. 1.3% (p<0.001), and 9.9 vs. 7.4% (p<0.001) for overall SPCs, primary pelvic SPCs, overall pelvic SPCs, and non-pelvic SPCs, respectively. Multivariable Cox regression models revealed an increased risk after RP and RT vs. RP alone for overall (hazard ratio [HR] 1.2; p<0.001), primary pelvic (HR 1.5; p<0.01), pelvic (HR1.4; p<0.001), non-pelvic (HR1.1; p<0.01), late overall (HR 1.2; p=0.01), and late non-pelvic SPCs (HR1.2; p=0.03). CONCLUSIONS: RP with RT was associated with moderately increased risk of SPCs compared to RP alone. This observation should be thoroughly discussed at informed consent and considered during followup.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| 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 | 0.001 |
| 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".