Validation of lymphovascular invasion as a predictor of lymph-node invasion in squamous cell carcinoma of the penis
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to validate lymphovascular invasion (LVI) as a predictor of lymph-node invasion (LNI) in squamous cell carcinoma of the penis (SCCP). METHODS: Within the Surveillance, Epidemiology, and End Results database (2010-2020), we identified SCCP patients who underwent lymphadenectomy with known LVI status. Univariable logistic regression models (LRMs) addressed LNI. Harrell's concordance index (c-index) quantified accuracy after 2000 bootstrap resamples for internal validation. Multivariable LRMs included the most informative, statistically significant predictors. Subgroup analyses were repeated in organ-confined (T1b-T2) and non-organ confined (T3-T4) stages. RESULTS: Of 586 SCCP patients, 219 (37%) had LVI. LVI was associated with higher rate of LNI (66 vs. 43%; P<0.001). Positive predictive value of LVI was 66 vs. 57% for negative predictive value. In multivariable LRMs, LVI independently predicted LNI (Odds ratio [OR]: 2.41; P<0.001). Bootstrap-adjusted c-index of multivariable model was 0.570 without LVI vs. 0.639 with LVI. In subgroup analyses, LVI independently predicted LNI in organ-confined (OR: 2.23; P<0.001) and in non-organ confined stages (OR: 3.10; P<0.001). In subgroup analyses, addition of LVI increased c-index from 0.530 to 0.595 in organ-confined and from 0.599 to 0.682 in non-organ confined. CONCLUSIONS: The current study validates LVI as an independent predictor of LNI in SCCP. LVI increases the accuracy of LNI predictions in the overall cohort as well as in organ-confined and non-organ confined stages. However, stage and grade even with the added consideration of LVI are not accurate enough to provide LNI prediction in individual patients.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".