Development and external validation of a prognostic nomogram to predict survival in patients aged ≥60 years with pancreatic ductal adenocarcinoma.
[BACKGROUND] Pancreatic ductal adenocarcinoma (PDAC), which accounts for the vast majority of pancreatic cancer (PC), is a highly aggressive malignancy with a dismal prognosis.
APA
Zheng B, Ding G, et al. (2024). Development and external validation of a prognostic nomogram to predict survival in patients aged ≥60 years with pancreatic ductal adenocarcinoma.. Translational cancer research, 13(6), 2751-2766. https://doi.org/10.21037/tcr-24-5
MLA
Zheng B, et al.. "Development and external validation of a prognostic nomogram to predict survival in patients aged ≥60 years with pancreatic ductal adenocarcinoma.." Translational cancer research, vol. 13, no. 6, 2024, pp. 2751-2766.
PMID
38988930
Abstract
[BACKGROUND] Pancreatic ductal adenocarcinoma (PDAC), which accounts for the vast majority of pancreatic cancer (PC), is a highly aggressive malignancy with a dismal prognosis. Age is shown to be an independent factor affecting survival outcomes in patients with PDAC. Our study aimed to identify prognostic factors and construct a nomogram to predict survival in PDAC patients aged ≥60 years.
[METHODS] Data of PDAC patients aged ≥60 years were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Multivariate Cox regression analysis was used to determined prognostic factors of overall survival (OS) and cancer-specific survival (CSS), and two nomograms were constructed and validated by calibration plots, concordance index (C-index) and decision curve analysis (DCA). Additionally, 432 patients from the First Affiliated Hospital of Wenzhou Medical University were included as an external cohort. Kaplan-Meier curves were applied to further verify the clinical validity of the nomograms.
[RESULTS] Ten independent prognostic factors were identified to establish the nomograms. The C-indexes of the training and validation groups based on the OS nomogram were 0.759 and 0.760, higher than those of the tumor-node-metastasis (TNM) staging system (0.638 and 0.636, respectively). Calibration curves showed high consistency between predictions and observations. Better area under the receiver operator characteristic (ROC) curve (AUC) values and DCA were also obtained compared to the TNM system. The risk stratification based on the nomogram could distinguish patients with different survival risks.
[CONCLUSIONS] We constructed and externally validated a population-based survival-predicting nomogram for PDAC patients aged ≥60 years. The new model could help clinicians personalize survival prediction and risk assessment.
[METHODS] Data of PDAC patients aged ≥60 years were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Multivariate Cox regression analysis was used to determined prognostic factors of overall survival (OS) and cancer-specific survival (CSS), and two nomograms were constructed and validated by calibration plots, concordance index (C-index) and decision curve analysis (DCA). Additionally, 432 patients from the First Affiliated Hospital of Wenzhou Medical University were included as an external cohort. Kaplan-Meier curves were applied to further verify the clinical validity of the nomograms.
[RESULTS] Ten independent prognostic factors were identified to establish the nomograms. The C-indexes of the training and validation groups based on the OS nomogram were 0.759 and 0.760, higher than those of the tumor-node-metastasis (TNM) staging system (0.638 and 0.636, respectively). Calibration curves showed high consistency between predictions and observations. Better area under the receiver operator characteristic (ROC) curve (AUC) values and DCA were also obtained compared to the TNM system. The risk stratification based on the nomogram could distinguish patients with different survival risks.
[CONCLUSIONS] We constructed and externally validated a population-based survival-predicting nomogram for PDAC patients aged ≥60 years. The new model could help clinicians personalize survival prediction and risk assessment.
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