North American Population‐Based Validation of the National Comprehensive Cancer Network Practice Guideline Recommendation of Pelvic Lymphadenectomy in Contemporary Prostate Cancer
Bibliographic record
Abstract
BACKGROUND: National Comprehensive Cancer Network (NCCN) guidelines recommend a pelvic lymph node dissection (PLND) in prostate cancer (PCa) patients treated with radical prostatectomy (RP) if a nomogram predicted risk of lymph node invasion (LNI) is ≥2%. We examined this and other thresholds, including nomogram validation. METHODS: We examined records of 26,713 patients treated with RP and PLND between 2010 and 2013, within the Surveillance, Epidemiology, and End Results database. Nomogram thresholds of 2-5% were tested and external validation was performed. RESULTS: LNI was recorded in 4.7% of patients. Nomogram accuracy was 80.4% and maintained minimum accuracy of 75.6% in subgroup analyses, according to age, race, and nodal yield >10. With the NCCN recommended 2% nomogram threshold, PLND could be avoided in 22.3% of patients at the expense of missing 3.0% of individuals with LNI. Alternative thresholds of 3%, 4%, and 5% yielded respective PLND avoidance rates of 60.4%, 71.0%, and 79.8% at the expense of missing 17.8%, 27.2%, and 36.6% of patients with LNI. NCCN cut-off recommendation was best satisfied with a threshold of <2.6%, at which PLND could be avoided in 13,234 patients (49.5%) versus missing 141 patients with LNI (11.2%). CONCLUSION: NCCN LNI nomogram remains accurate in contemporary patients. However, the 2% threshold appears to be too strict, since only 22.3% of PLNDs can be avoided, instead of the stipulated 47.7%. The optimal 2.6% threshold allows a higher rate of PLND avoidance (49.5%), at the cost of 11.2% missed instances of LNI, as recommended by NCCN guidelines. PATIENT SUMMARY. External validation in contemporary SEER prostate cancer patients showed that the NCCN nomogram remains accurate for predicting lymph node invasion and seems to be optimal at an alternative 2.6% threshold, with best ratio of avoided pelvic lymph node dissections (49.5%) and missed LNIs (11.2%), as recommended by NCCN guideline. Prostate 77:542-548, 2017. © 2017 Wiley Periodicals, Inc.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".