PDF(Pubmed)
Abstract：
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
摘要：
■胰腺导管腺癌（PDAC），占胰腺癌（PC）的绝大多数，是一种高度侵袭性的恶性肿瘤，预后不佳。年龄被证明是影响PDAC患者生存结果的独立因素。我们的研究旨在确定预后因素并构建列线图来预测年龄≥60岁的PDAC患者的生存。
■从监测中收集年龄≥60岁的PDAC患者的数据，流行病学,和结束结果(SEER)数据库。多因素Cox回归分析用于确定总生存期（OS）和癌症特异性生存期（CSS）的预后因素。并通过校准图构建和验证了两个列线图，一致性指数(C指数)和决策曲线分析(DCA)。此外,温州医科大学附属第一医院432例患者作为外部队列。应用Kaplan-Meier曲线进一步验证列线图的临床有效性。
■确定了十个独立的预后因素来建立列线图。基于OS列线图的训练组和验证组的C指数分别为0.759和0.760，高于肿瘤淋巴结转移（TNM）分期系统的C指数（分别为0.638和0.636）。校准曲线显示预测和观察之间的高度一致性。与TNM系统相比，还获得了更好的接收器操作特征（ROC）曲线下面积（AUC）值和DCA。基于列线图的风险分层可以区分具有不同生存风险的患者。
■我们构建并外部验证了年龄≥60岁的PDAC患者的基于人群的生存预测列线图。新模型可以帮助临床医生个性化生存预测和风险评估。
■从监测中收集年龄≥60岁的PDAC患者的数据，流行病学,和结束结果(SEER)数据库。多因素Cox回归分析用于确定总生存期（OS）和癌症特异性生存期（CSS）的预后因素。并通过校准图构建和验证了两个列线图，一致性指数(C指数)和决策曲线分析(DCA)。此外,温州医科大学附属第一医院432例患者作为外部队列。应用Kaplan-Meier曲线进一步验证列线图的临床有效性。
■确定了十个独立的预后因素来建立列线图。基于OS列线图的训练组和验证组的C指数分别为0.759和0.760，高于肿瘤淋巴结转移（TNM）分期系统的C指数（分别为0.638和0.636）。校准曲线显示预测和观察之间的高度一致性。与TNM系统相比，还获得了更好的接收器操作特征（ROC）曲线下面积（AUC）值和DCA。基于列线图的风险分层可以区分具有不同生存风险的患者。
■我们构建并外部验证了年龄≥60岁的PDAC患者的基于人群的生存预测列线图。新模型可以帮助临床医生个性化生存预测和风险评估。
