Department of Gastroenterology, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China.
Department of Digestive Endoscopy, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China.
PLoS One. 2021 May 5;16(5):e0249911. doi: 10.1371/journal.pone.0249911. eCollection 2021.
As a malignant tumor with poor prognosis, accurate and effective prediction of the prognosis of pancreatic cancer (PC) is crucial.
A total of 12,909 patients diagnosed with pancreatic cancer were selected from the Surveillance, Epidemiology, and End Results program between 2004 and 2016. The sex, age, ethnicity, marital status, metastasis status, radiotherapy, chemotherapy, tumor size, regional nodes examined, regional nodes positive of each patient were recorded. Univariate and multivariate Cox regression analyses were used to identify prognostic factors with a threshold of P<0.05, and a nomogram was constructed. Harrell's concordance indexes and calibration plots were used to verify the predictive power of the model. The risk groups were also stratified by quartile of the total score. Survival rates were estimated by the Kaplan-Meier method.
Age, year of diagnosis, sex, grade, histologic, marital, TNM stage, surgery of the primary site, tumor size, regional nodes positive and regional nodes examined ratio (LNR), lymph node dissection, radiotherapy, and chemotherapy were identified as prognostic factors for the construction of the nomogram. The nomogram exhibited a clinical predictive ability of 0.675(95% CI, 0.669~0.681) in the internal verification. The predicted calibration curve was similar to the standard curve. Decision curve analysis showed that the nomogram had value in terms of clinical application. Besides, the nomogram was able to divide the patients into different groups according to total points.
Hence, our nomogram was highly effective in predicting overall survival in patients with PC, which may provide a reference tool for clinicians to guide individualized treatment and follow-ups for patients with PC, accurately determine the 1-,3- and 5-year overall survival of patients.
胰腺癌（PC）是一种预后不良的恶性肿瘤，准确有效地预测其预后至关重要。
从 2004 年至 2016 年，从监测、流行病学和最终结果计划中选择了 12909 名被诊断患有胰腺癌的患者。记录了每位患者的性别、年龄、种族、婚姻状况、转移状态、放疗、化疗、肿瘤大小、检查的区域淋巴结、区域淋巴结阳性的情况。使用单因素和多因素 Cox 回归分析确定具有 P<0.05 阈值的预后因素，并构建列线图。使用 Harrell 的一致性指数和校准图来验证模型的预测能力。还根据总分的四分位数对风险组进行分层。通过 Kaplan-Meier 方法估计生存率。
年龄、诊断年份、性别、分级、组织学、婚姻状况、TNM 分期、原发部位手术、肿瘤大小、区域淋巴结阳性和区域淋巴结检查比（LNR）、淋巴结清扫、放疗和化疗被确定为构建列线图的预后因素。该列线图在内部验证中表现出 0.675（95%CI，0.669~0.681）的临床预测能力。预测校准曲线与标准曲线相似。决策曲线分析表明，该列线图在临床应用方面具有价值。此外，该列线图能够根据总积分将患者分为不同的组。
因此，我们的列线图在预测 PC 患者的总生存率方面非常有效，它可以为临床医生提供一种参考工具，以指导 PC 患者的个体化治疗和随访，准确确定患者的 1 年、3 年和 5 年总生存率。
