ArticleScientific reports2026
Construction and validation of a nomogram for overall survival prognosis in patients with advanced (stage III/IV) pancreatic cancer.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Multi-Omics-Enabled Precision Strategies for Overcoming CAR-T Therapy Limitations in Gastrointestinal Malignancies.BioFactors (Oxford, England)Review
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Authors and funding
10 authors.
Funding
Abstract
In order to provide an additional tool for clinical prognosis evaluation, this research attempts to build a nomogram for predicting the survival of patients with advanced (stage Ⅲ/Ⅳ) pancreatic cancer and to preliminary evaluate its predictive impact. Data from 336 patients diagnosed with stage III/IV pancreatic cancer (2018-2025) across two Chinese hospitals were analyzed. Multivariate Cox regression identified independent predictors in the training set, which were used to construct a nomogram estimating 6-, 12-, and 24-month overall survival. The model underwent internal and external validation via ROC curves, calibration plots, and decision curve analysis. Multivariate Cox regression analysis of the training set revealed that serum albumin (P = 0.001), liver metastasis (P = 0.023), ALT≥40U/L (P = 0.010), and CA199 level (P = 0.037) were independent predictors of overall survival. Based on this, a nomogram model was constructed in the training cohort, with a C-index of 0.741. In the internal validation, the AUC values for predicting 6, 12, and 24-month survival rates were 0.806, 0.753, and 0.628, respectively, and in the external validation, they were 0.922, 0.662, and 0.650, respectively. The calibration curve showed that the predicted probabilities were in good agreement with the actual observed results. The discrimination and calibration of this model in internal verification are acceptable, but its incremental value for clinical decision-making is limited, and large-scale multi-center studies are required to further verify its generalizability.
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