Web-based dynamic nomogram for forecasting overall survival and cancer-specific survival among individuals with lymph node-negative pancreatic cancer: based upon the SEER database.

Authors

Zheyuan Wang, Lu Zhang, Zengyou Li, Huihan Zhang

Published in

Clinics (Sao Paulo, Brazil). Volume 81. Pages 101100. Sep 05, 2026. Epub Sep 05, 2026.

Abstract

This study aimed to develop and validate prognostic models for Lymph Node (LN)-negative pancreatic cancer patients.
Data were extracted from the SEER database (2004‒2015). The included participants were randomly divided into training (70%) and validation (30%) sets. Independent prognostic factors for Overall Survival (OS) and Cancer-Specific Survival (CSS) were identified using Cox and Fine-Gray models to construct predictive nomograms for 1-, 3-, and 5-year outcomes.
Among 5970 included patients, 4270 deaths occurred. The nomogram for OS included 11 variables and the nomogram for CSS included 10. For OS, the C-indices in the training and validation cohorts were 0.740 (95% CI 0.722-0.758) and 0.740 (95% CI 0.712-0.768), respectively. Similarly, the C-indices for CSS were 0.737 (95% CI 0.719-0.756) and 0.736 (95% CI 0.708-0.764), respectively. The AUCs for 1-, 3-, and 5-year OS were 0.794 (95% CI 0.770-0.818), 0.819 (95% CI 0.800-0.839), and 0.836 (95% CI: 0.816-0.855). Meanwhile, the AUCs for 1-, 3-, and 5-year CSS were 0.796 (95% CI: 0.781-0.812), 0.829 (95% CI: 0.816-0.841), and 0.850 (95% CI: 0.838-0.862), indicating strong predictive performance. Calibration curves confirmed good accuracy. Competing risk analysis showed conventional methods overestimated CSS, supporting the accuracy of the Fine-Gray model.
We developed and internally validated two clinically practical nomograms for OS and CSS for patients with LN-negative pancreatic cancer. These models show favorable discrimination and calibration, enabling clinical risk stratification and prognosis assessment. Future external validation using independent cohorts is needed to confirm the generalizability of these models.

PMID:
42700544
Bibliographic data and abstract were imported from PubMed on 06 Sep 2026.

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