Nuclear Localization of Nuclear Factor-κB p65 in Primary Prostate Tumors Is Highly Predictive of Pelvic Lymph Node Metastases
Bibliographic record
Abstract
PURPOSE: Lymph node invasion (LNI) is associated with increased risk of prostate cancer progression. Unfortunately, pelvic lymph node dissections are fraught with a high rate of false-negative findings, emphasizing the need for highly accurate markers of LNI. Because nuclear factor-kappaB (NF-kappaB) is a candidate marker of prostate cancer progression, we tested the association between nuclear localization of NF-kappaB in radical prostatectomy specimens and the presence of LNI. EXPERIMENTAL DESIGN: NF-kappaB expression in radical prostatectomy specimens was assessed with a monoclonal NF-kappaB p65 antibody, in 20 patients with LNI and in 31 controls with no LNI and no biochemical relapse 5 years after radical prostatectomy. Univariate and multivariate logistic regression models were used. The accuracy of multivariate predictions with and without NF-kappaB was quantified with the area under the receiver operating characteristics curve and 200 bootstrap resamples were used to reduce overfit bias. RESULTS: Univariate regression models showed a 7% increase in the odds of observing LNI for each 1% increase in NF-kappaB nuclear staining (odds ratio, 1.07; P = 0.003). In multivariate models, each 1% increase in NF-kappaB was associated with an 8% increase in the odds of LNI (odds ratio, 1.08; P = 0.03) and its statistical significance was only surpassed by the presence of seminal vesicle invasion (P = 0.003). Addition of NF-kappaB to all other predictors increased the accuracy of LNI prediction by 2.3% (from 84.8% to 87.1%; P < 0.001). CONCLUSION: This is the first study that shows that the extent of nuclear localization of NF-kappaB in primary prostate tumors is highly accurately capable of predicting the probability of locoregional spread of prostate cancer.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".