Extent of lymph node dissection improves survival in prostate cancer patients treated with radical prostatectomy without lymph node invasion
Bibliographic record
Abstract
PURPOSE: To assess the effect of pelvic lymph node dissection (PLND) extent on cancer-specific mortality (CSM) in prostate cancer (PCa) patients without lymph node invasion (LNI) treated with radical prostatectomy (RP). METHODS: Within the Surveillance, Epidemiology, and End results (SEER) database (2004-2014), we identified patients with D'Amico intermediate- or high-risk characteristics who underwent RP with PLND, without evidence of LNI. First, multivariable logistic regression models tested for predictors of more extensive PLND, defined as removed lymph node count (NRN) ≥75th percentile. Second, Kaplan-Meier analyses and multivariable Cox regression models tested the effect of NRN ≥75th percentile on CSM. Finally, survival analyses were repeated using continuously coded NRN. RESULTS: In 28 147 RP and PLND patients without LNI, 67.3% versus 32.7% exhibited D'Amico intermediate- or high-risk characteristics. The median NRN was 6 (IQR 3-10), the 75th percentile defined patients with NRN ≥11. Patients with NRN ≥11 had higher rate of cT2/3 stage (29.8 vs 26.1%), GS ≥8 (25.7 vs 22.4%), and respectively more frequently exhibited D'Amico high-risk characteristics (34.6 vs 32.1%). In multivariable logistic regression models predicting the probability of more extensive PLND (NRN ≥11), higher biopsy GS, higher cT stage, higher PSA, more recent year of diagnosis, and younger age at diagnosis represented independent predictors. At 72 months after RP, CSM-free rates were 99.5 versus 98.1% for NRN ≥11 and NRN ≤10, respectively and resulted in a HR of 0.50 (P = 0.01), after adjustment for all covariates. Similarly, continuously coded NRN achieved independent predictor status (HR: 0.955, P = 0.01), where each additional removed lymph node reduced CSM risk by 4.5%. CONCLUSION: More extensive PLND at RP provides improved staging information and consequently is associated with lower CSM in D'Amico intermediate- and high-risk PCa patients without evidence of LNI. Hence, more extensive PLND should be recommended in such individuals.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".