基于 SEER 数据库和中国队列研究的列线图预测胰腺癌患者放疗后总生存的研究。

OBJECTIVE: 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). METHODS: 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). RESULTS: 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. CONCLUSION: SCRT significantly improves the OS of PC patients. We developed and validated a Nomogram to predict the OS of PC patients receiving RT.

目的：胰腺癌（PC）患者预后较差。放射治疗（RT）是临床实践中的标准姑息治疗方法，但目前尚无有效的临床预测模型来预测接受放疗的 PC 患者的预后。本研究旨在分析 PC 的临床特征，找出影响 PC 患者预后的因素，并构建一个可视化诺莫图来预测总生存期（OS）。

方法：使用 SEER*Stat 软件从监测、流行病学和结果（SEER）数据库中收集了 3570 名接受 RT 治疗的患者的临床数据。同时，从郑州大学附属肿瘤医院收集了 115 名患者的相关临床数据。SEER 数据库数据以 7:3 的比例随机分为训练和内部验证队列，郑州大学附属肿瘤医院的所有患者为外部验证队列。使用套索回归筛选相关变量。所有非零变量均纳入多变量分析。多变量 Cox 比例风险回归分析确定独立预后因素。采用 Kaplan-Meier（K-M）法绘制不同治疗方法（手术、RT、化疗和联合治疗）的生存曲线，并计算中位 OS。构建诺莫图预测 1、3 和 5 年的生存率，计算计算曲线的时间依赖性接受者操作特征曲线（ROC）。计算曲线下面积（AUC），使用 Bootstrap 方法绘制校准曲线，并使用决策曲线分析（DCA）评估预测模型的临床疗效。

结果：手术联合放化疗（SCRT）、手术联合放疗、放化疗（CRT）和 RT 单独治疗队列的中位 OS 分别为 25.0、18.0、11.0 和 4.0 个月。多变量 Cox 回归分析显示，年龄、N 分期、M 分期、化疗、手术、淋巴结手术和分级是患者的独立预后因素。构建了预测患者 OS 的诺莫图模型。绘制了 1、3 和 5 年的时间依赖性 ROC 曲线，并计算了 AUC 值。结果表明，训练队列的 AUC 值分别为 0.77、0.79 和 0.79，内部验证队列为 0.79、0.82 和 0.81，外部验证队列为 0.73、0.93 和 0.88。校准曲线表明，模型预测概率与实际观察概率高度一致，DCA 曲线显示高净收益。

结论：SCRT 显著提高了 PC 患者的 OS。我们开发并验证了一个诺莫图来预测接受 RT 的 PC 患者的 OS。