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Updated: Jun 11, 2026
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
Published on: April 18, 2025
Development and evaluation of a nomogram to predict inguinal lymph node metastasis in patients with penile cancer and
Yao Zhu1, Hai-Liang Zhang, Xu-Dong Yao
1Department of Urology, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China.
Purpose:
Optimal management for penile cancer in patients with clinically negative lymph nodes is still under debate. We developed and evaluated a nomogram to stratify patients who are suitable candidates for further treatment.
Materials And Methods:
This study included 110 men with penile cancer and clinically negative lymph nodes from 1990 to 2008. All patients underwent primary tumor resection and regional lymphadenectomy. We retrospectively reviewed medical records and tumor slides. Statistical analysis was done in R with library rms.
Results:
The lymph node metastasis rate in the entire cohort was 23.6%. The final model, presented as a nomogram, included T stage, grade, lymphovascular invasion and p53 expression. Only lymphovascular invasion showed independent prognostic value on multivariate analysis (p = 0.024). The model also showed good calibration (bootstrap corrected concordance index 0.79). To determine the clinical usefulness of the nomogram we compared it with the European Association of Urology risk classification using decision curve analysis. At a 10% probability threshold our nomogram led to 1 positive result per 100 patients without an increase in the number of false-positive results. At this probability threshold the model also decreased 13 unnecessary interventions per 100 patients without missing more metastatic disease.
Conclusions:
We generated a nomogram in patients with clinically node negative penile cancer based on readily available pathological factors. The clinical usefulness of the nomogram was evidenced by decision curve analysis.