Department of Oncology, The First Affiliated Hospital of Xinxiang Medical University, 88 Jiankang Road, Xinxiang, 453100, Henan, China.
Department of Otolaryngology, AnYang District Hospital, Anyang, 455000, Henan, China.
J Cancer Res Clin Oncol. 2023 Oct;149(13):12469-12477. doi: 10.1007/s00432-023-05048-8. Epub 2023 Jul 14.
Lymph node metastasis (LNM) is a critical prognostic factor in resectable pancreatic cancer (PC) patients, determining treatment strategies. This study aimed to develop a clinical model to adequately and accurately predict the risk of LNM in PC patients.
13,200 resectable PC patients were enrolled from the SEER (Surveillance, Epidemiology, and End Results) database, and randomly divided into a training group and an internal validation group at a ratio of 7:3. An independent group (n = 62) obtained from The First Affiliated Hospital of Xinxiang Medical University was enrolled as the external validation group. The univariate and multivariate logistic regression analyses were used to screen independent risk factors for LNM. The minimum Akaike's information criterion (AIC) was performed to select the optimal model parameters and construct a nomogram for assessing the risk of LNM. The performance of the nomogram was assessed by the receiver operating characteristics (ROC) curve, calibration plot, and decision curve analysis (DCA). In addition, an online web calculator was designed to assess the risk of LNM.
A total of six risk predictors (including age at diagnosis, race, primary site, grade, histology, and T-stage) were identified and included in the nomogram. The areas under the curves (AUCs) [95% confidential interval (CI)] were 0.711 (95%CI: 0.700-0.722), 0.700 (95%CI: 0.683-0.717), and 0.845 (95%CI: 0.749-0.942) in the training, internal validation and external validation groups, respectively. The calibration curves showed satisfied consistency between nomogram-predicted LNM and actual observed LNM. The concordance indexes (C-indexes) in the training, internal, and external validation sets were 0.689, 0.686, and 0.752, respectively. The DCA curves of the nomogram demonstrated good clinical utility.
We constructed a nomogram model for predicting LNM in pancreatic cancer patients, which may help oncologists and surgeons to choose more individualized clinical treatment strategies and make better clinical decisions.
淋巴结转移（LNM）是可切除胰腺癌（PC）患者的一个关键预后因素，决定着治疗策略。本研究旨在开发一种临床模型，以充分准确地预测 PC 患者发生 LNM 的风险。
从 SEER（监测、流行病学和最终结果）数据库中纳入 13200 例可切除 PC 患者，按照 7:3 的比例随机分为训练组和内部验证组。新乡医学院第一附属医院的 62 例独立患者（n=62）纳入外部验证组。采用单因素和多因素 logistic 回归分析筛选 LNM 的独立危险因素。采用最小 Akaike 信息准则（AIC）选择最佳模型参数，并构建用于评估 LNM 风险的列线图。通过受试者工作特征（ROC）曲线、校准图和决策曲线分析（DCA）评估列线图的性能。此外，设计了一个在线网络计算器来评估 LNM 的风险。
共确定了 6 个风险预测因子（包括诊断时的年龄、种族、原发部位、分级、组织学和 T 分期），并纳入了列线图。在训练组、内部验证组和外部验证组中，曲线下面积（AUC）分别为 0.711（95%CI：0.700-0.722）、0.700（95%CI：0.683-0.717）和 0.845（95%CI：0.749-0.942）。校准曲线显示，列线图预测的 LNM 与实际观察到的 LNM 之间具有良好的一致性。在训练组、内部验证组和外部验证组中，一致性指数（C-index）分别为 0.689、0.686 和 0.752。列线图的 DCA 曲线显示出良好的临床实用性。
我们构建了一个用于预测 PC 患者 LNM 的列线图模型，这可能有助于肿瘤学家和外科医生选择更个体化的临床治疗策略，并做出更好的临床决策。
