Nuclear Localization of Nuclear Factor-κB p65 in Primary Prostate Tumors Is Highly Predictive of Pelvic Lymph Node Metastases
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it