External validation of the updated briganti nomogram to predict lymph node invasion in prostate cancer patients undergoing extended lymph node dissection
Bibliographic record
Abstract
PURPOSE: We aimed to test accuracy and generalizability of a recently updated nomogram to assess the probability of lymph node invasion (LNI), when applied to a different European cohort of men undergoing radical prostatectomy (RP) with extended pelvic lymph node dissection (ePLND). MATERIALS AND METHODS: The study cohort consisted of 1,282 men with clinically localized PCa who underwent RP and ePLND, including removal of obturator, external iliac, and hypogastric lymph nodes, between 01/2007 and 08/2011. Descriptive measurements included preoperative clinical and biopsy variables, such as prostate-specific antigen (PSA), clinical stage (CS), primary and secondary biopsy Gleason pattern, and percentage of positive cores. We used the area under curve (AUC) of the receiver operator characteristic analysis to quantify accuracy of the model to predict LNI. The extent of over- or under-estimation was explored graphically within loess calibration plots. RESULTS: The median number of removed lymph nodes was 15 with an interquartile range of 12-20. Twelve percent (n = 155) of men had LNI. Preoperative clinical and biopsy characteristics differed significantly (all P ≤ 0.002) between men with LNI and those without. External validation of the previously reported updated LNI nomogram showed very good accuracy (AUC: 0.829). A nomogram-derived cut-off of 4% could lead to a reduction of 48% of lymph node dissection, while missing 10% of patients with LNI. CONCLUSIONS: We report the external validation of an updated LNI nomogram, demonstrating accuracy and applicability in a different European cohort. A nomogram-derived cut-off of 4% confirmed good performance characteristics within a different external validation cohort.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".