| 摘要: |
| 目的 寻找青年胃癌患者预后影响因素，构建预后预测模型列线图，为患者的个体化预后评估提供更精确的工具。方法 通过监测、流行病学和最终结果（SEER）数据库客户端SEER*Stat 8.3.8收集2004-2015年确诊的2 673例年龄为18~44岁的青年胃癌患者信息，使用R 4.0.3软件将2 673例病例按照约7：3的比例随机分成训练集（1 873例）与验证集（800例）。以癌症特异性生存（CSS）率为关注点，在训练集中使用Fine-Gray竞争风险模型进行单因素和多因素分析，寻找青年胃癌患者CSS的影响因素，根据影响因素建立预后预测模型并绘制列线图。使用ROC曲线和校准曲线在训练集与验证集数据中对模型的预测效果进行验证。结果 训练集数据多因素分析结果表明肿瘤分级、T分期、N分期、M分期、原发灶手术情况、区域淋巴结手术情况、放化疗情况是青年胃癌患者CSS的独立影响因素。训练集中青年胃癌患者的1、3和5年累积CSS率分别为54.56%、29.70%和23.96%。根据独立预后影响因素构建的列线图，在训练集中1、3和5年CSS率的ROC曲线AUC值分别为0.817、0.864和0.887，在验证集中分别为0.820、0.899和0.890；校准曲线显示在训练集与验证集中1、3、5年CSS率预测模型的预测概率与实际概率基本一致。结论 Fine-Gray竞争风险模型能有效识别青年胃癌患者的预后影响因素，以此为依据构建的预后预测模型能有效预测患者的CSS，可为临床医师做出治疗决策提供参考。 |
| 关键词: 胃肿瘤 癌症特异性生存 预后 影响因素 列线图 SEER数据库 |
| DOI：10.16781/j.0258-879x.2021.10.1140 |
| 投稿时间：2021-03-20修订日期：2021-06-28 |
| 基金项目:湖南省学位与研究生教育改革研究项目（2019JGZX009）. |
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| Prognostic factors of young patients with gastric cancer based on Fine-Gray competitive risk model |
| ZHU Xu1,CHEN Shu1,WEI Xin-ran2,WEI Gao-wen1* |
(1. Department of Epidemiology and Health Statistics, College of Integrated Traditional Chinese and Western Medicine, Hunan University of Chinese Medicine, Changsha 410208, Hunan, China;
2. Department of Acupuncture and Massage Rehabilitation, the First Hospital of Hunan University of Chinese Medicine, Changsha 410007, Hunan, China
*Corresponding author) |
| Abstract: |
| Objective To search for the prognostic factors of young patients with gastric cancer, and construct a prognostic prediction model nomogram, so as to provide a more accurate tool for the individualized prognostic evaluation of patients. Methods The information of 2 673 young gastric cancer patients aged 18-44 years diagnosed from 2004 to 2015 was collected from the Surveillance, Epidemiology, and End Results (SEER) database client SEER*Stat 8.3.8. The patients were randomly divided into training set (1 873 cases) and validation set (800 cases) in a ratio of about 7:3 using R 4.0.3 software. Focusing on the cancer-specific survival (CSS) rate, univariate and multivariate analyses were performed using Fine-Gray competitive risk model in the training set to find the influencing factors of CSS in young gastric cancer patients. According to the influencing factors, the prognosis prediction model was established and the nomogram was drawn. Receiver operating characteristic (ROC) curve and calibration curve were used to verify the prediction effect of the model in the training set and validation set. Results The results of multivariate analysis in the training set showed that tumor grade, T stage, N stage, M stage, primary surgery, regional lymph node surgery and chemoradiotherapy were the independent influencing factors of CSS in young patients with gastric cancer. The cumulative 1-, 3- and 5-year CSS rates of young gastric cancer patients in the training set were 54.56%, 29.70% and 23.96%, respectively. The area under curve (AUC) values of ROC curves of 1-, 3- and 5-year CSS rates of nomogram constructed based on independent prognostic factors were 0.817, 0.864 and 0.887 in the training set, respectively, while those were 0.820, 0.899 and 0.890 in the validation set, respectively. The calibration curves showed that the prediction probabilities of the 1-, 3- and 5-year CSS rates in the training set and validation set were basically consistent with the observed probabilities. Conclusion The Fine-Gray competitive risk model can effectively identify the prognostic factors of young patients with gastric cancer, and the prognostic prediction model can effectively predict the CSS of patients, which can help clinicians to make treatment decisions. |
| Key words: stomach neoplasms cancer-specific survival prognosis influencing factors nomogram SEER database |
