Temporal trends, tumor characteristics and stage-specific survival in penile non-squamous cell carcinoma vs. squamous cell carcinoma
Bibliographic record
Abstract
PURPOSE: To compare Cancer-specific mortality (CSM) in patients with Squamous cell carcinoma (SCC) vs. non-SCC penile cancer, since survival outcomes may differ between histological subtypes. METHODS: Within the Surveillance, Epidemiology and End Results database (2004-2016), penile cancer patients of all stages were identified. Temporal trend analyses, cumulative incidence and Kaplan-Meier plots, multivariable Cox regression and Fine and Gray competing-risks regression analyses tested for CSM differences between non-SCC vs. SCC penile cancer patients. RESULTS: Of 4,120 eligible penile cancer patients, 123 (3%) harbored non-SCC vs. 4,027 (97%) SCC. Of all non-SCC patients, 51 (41%) harbored melanomas, 42 (34%) basal cell carcinomas, 10 (8%) adenocarcinomas, eight (6.5%) skin appendage malignancies, six (5%) epithelial cell neoplasms, two (1.5%) neuroendocrine tumors, two (1.5%) lymphomas, two (1.5%) sarcomas. Stage at presentation differed between non-SCC vs. SCC. In temporal trend analyses, non-SCC diagnoses neither decreased nor increased over time (p > 0.05). After stratification according to localized, locally advanced, and metastatic stage, no CSM differences were observed between non-SCC vs. SCC, with 5-year survival rates of 11 vs 11% (p = 0.9) for localized, 33 vs. 37% (p = 0.4) for locally advanced, and 1-year survival rates of 37 vs. 53% (p = 0.9) for metastatic penile cancer, respectively. After propensity score matching for patient and tumor characteristics and additional multivariable adjustment, no CSM differences between non-SCC vs. SCC were observed. CONCLUSION: Non-SCC penile cancer is rare. Although exceptions exist, on average, non-SCC penile cancer has comparable CSM as SCC penile cancer patients, after stratification for localized, locally invasive, and metastatic disease.
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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) | 0.001 | 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) | 0.001 | 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 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".