胰腺癌（PC）患者预后较差。放射治疗（RT）是临床上标准的姑息治疗，目前尚无有效的临床预测模型来预测接受放射治疗的PC患者的预后。本研究旨在分析 PC 的临床特征，寻找影响 PC 患者预后的因素，并构建可视化列线图来预测总生存期 (OS)。使用 SEER*Stat 软件从监测、流行病学和最终结果中收集临床数据(SEER) 数据库包含 3570 名接受放疗治疗的患者。同时收集郑州大学附属肿瘤医院115例患者的相关临床资料。 SEER数据库数据按7:3的比例随机分为训练队列和内部验证队列，其中郑州大学附属肿瘤医院的所有患者作为外部验证队列。使用套索回归来筛选相关变量。所有非零变量都包含在多变量分析中。使用多变量Cox比例风险回归分析来确定独立的预后因素。采用Kaplan-Meier（K-M）方法绘制不同治疗（手术、放疗、化疗和联合治疗）的生存曲线并计算中位OS。构建列线图来预测 1 年、3 年和 5 年的生存率，并用计算的曲线绘制随时间变化的受试者工作特征曲线 (ROC)。计算曲线下面积（AUC），采用Bootstrap法绘制校准曲线，采用决策曲线分析（DCA）评价预测模型的临床疗效。中位OS分别为25.0、18.0、11.0、手术联合放化疗 (SCRT)、手术联合放疗、放化疗 (CRT) 和单独放疗队列分别为 4.0 个月。多因素Cox回归分析显示，年龄、N分期、M分期、化疗、手术、淋巴结手术、分级是患者的独立预后因素。构建列线图模型来预测患者的 OS。绘制 1 年、3 年和 5 年时间依赖性 ROC 曲线，并计算 AUC 值。结果表明，训练队列的 AUC 分别为 0.77、0.79 和 0.79，内部验证队列的 AUC 分别为 0.79、0.82 和 0.81，外部验证队列的 AUC 分别为 0.73、0.93 和 0.88。校准曲线显示模型预测概率与实际观测概率高度吻合，DCA曲线显示出较高的净回报。SCRT显着提高了PC患者的OS。我们开发并验证了列线图来预测接受 RT 的 PC 患者的 OS。版权所有 © 2023 Dong、Wang、Yang、Cheng、Li、Hou、Chang 和 Yuan。

Patients with pancreatic cancer (PC) have a poor prognosis. Radiotherapy (RT) is a standard palliative treatment in clinical practice, and there is no effective clinical prediction model to predict the prognosis of PC patients receiving radiotherapy. This study aimed to analyze PC's clinical characteristics, find the factors affecting PC patients' prognosis, and construct a visual Nomogram to predict overall survival (OS).SEER*Stat software was used to collect clinical data from the Surveillance, Epidemiology, and End Results (SEER) database of 3570 patients treated with RT. At the same time, the relevant clinical data of 115 patients were collected from the Affiliated Cancer Hospital of Zhengzhou University. The SEER database data were randomly divided into the training and internal validation cohorts in a 7:3 ratio, with all patients at The Affiliated Cancer Hospital of Zhengzhou University as the external validation cohort. The lasso regression was used to screen the relevant variables. All non-zero variables were included in the multivariate analysis. Multivariate Cox proportional risk regression analysis was used to determine the independent prognostic factors. The Kaplan-Meier(K-M) method was used to plot the survival curves for different treatments (surgery, RT, chemotherapy, and combination therapy) and calculate the median OS. The Nomogram was constructed to predict the survival rates at 1, 3, and 5 years, and the time-dependent receiver operating characteristic curves (ROC) were plotted with the calculated curves. Calculate the area under the curve (AUC), the Bootstrap method was used to plot the calibration curve, and the clinical efficacy of the prediction model was evaluated using decision curve analysis (DCA).The median OS was 25.0, 18.0, 11.0, and 4.0 months in the surgery combined with chemoradiotherapy (SCRT), surgery combined with radiotherapy, chemoradiotherapy (CRT), and RT alone cohorts, respectively. Multivariate Cox regression analysis showed that age, N stage, M stage, chemotherapy, surgery, lymph node surgery, and Grade were independent prognostic factors for patients. Nomogram models were constructed to predict patients' OS. 1-, 3-, and 5-year Time-dependent ROC curves were plotted, and AUC values were calculated. The results suggested that the AUCs were 0.77, 0.79, and 0.79 for the training cohort, 0.79, 0.82, and 0.81 for the internal validation cohort, and 0.73, 0.93, and 0.88 for the external validation cohort. The calibration curves Show that the model prediction probability is in high agreement with the actual observation probability, and the DCA curve shows a high net return.SCRT significantly improves the OS of PC patients. We developed and validated a Nomogram to predict the OS of PC patients receiving RT.Copyright © 2023 Dong, Wang, Yang, Cheng, Li, Hou, Chang and Yuan.