ArticleDiscover oncology2026
Development and validation of a nomogram for predicting distant metastasis and prognosis in elderly T1-T2 pancreatic ductal adenocarcinoma patients.
Article in Discover oncology, 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
backgroundElderly patients with early stage T pancreatic ductal adenocarcinoma (PDAC) have a poor prognosis for distant metastasis (DM). In this study, we aimed to construct and validate a novel nomogram for predicting distant metastasis and prognosis in elderly patients with T1-T2 PDAC.
methodsIn this study, patients diagnosed with pancreatic ductal adenocarcinoma were extracted from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2017. Univariate and multivariate logistic regression analyses were used to determine independent risk factors for distant metastasis in elderly patients with T1-T2 PDAC. Univariate and stepwise multivariate Cox regression analyses were used to determine independent prognostic factors in elderly patients with T1-T2 PDAC. two novel nomograms were developed, and the results were evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).
resultsMultivariate logistic regression analysis demonstrated that the independent risk factors for DM in elderly patients with T1-T2 PDAC included primary site, grade, N stage, T stage and sex. Stepwise multivariate Cox regression analysis indicated that age, grade, primary site, tumour size, liver metastasis, surgery and chemotherapy were independent prognostic factors. The performance of the two prediction models was further validated by the analysis of the ROC curves of the training and validation sets, calibration, DCA and Kaplan-Meier (K-M) survival curves, which confirmed their capacity to accurately predict the risk and prognosis of DM in elderly patients with T1-T2 PDAC.
conclusionThe two nomograms are expected to serve as effective tools for predicting the risk of DM in elderly patients with T1-T2 PDAC and for providing personalized prognosis prediction in elderly T1-T2 PDAC patients with DM, which may significantly improve clinical decision-making and patient management.
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