ArticleClinics (Sao Paulo, Brazil)2026
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.
Article in Clinics (Sao Paulo, Brazil), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThis study aimed to develop and validate prognostic models for Lymph Node (LN)-negative pancreatic cancer patients.
methodsData 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.
resultsAmong 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.
conclusionWe 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.
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