Zheng Honghong, Li Zhehong, Li Jianjun, Zheng Shuai, Zhao Enhong
Department of Gastrointestinal Surgery, Affiliated Hospital of Chengde Medical University, Chengde, China.
Department of Orthopedic, Affiliated Hospital of Chengde Medical University, Chengde, China.
J Oncol. 2021 Nov 1;2021:5495267. doi: 10.1155/2021/5495267. eCollection 2021.
The lung is one of the most common sites of metastasis in gastric cancer. Our study developed two nomograms to achieve individualized prediction of overall survival (OS) and cancer-specific survival (CSS) in patients with gastric cancer and lung metastasis (GCLM) to better guide follow-up and planning of subsequent treatment.
We reviewed data of patients diagnosed with GCLM in the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. The endpoints of the study were the OS and CSS. We used the "caret" package to randomly divide patients into training and validation cohorts in a 7 : 3 ratio. Multivariate Cox regression analysis was performed using univariate Cox regression analysis to confirm the independent prognostic factors. Afterward, we built the OS and CSS nomograms with the "rms" package. Subsequently, we evaluated the two nomograms through calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA). Finally, two web-based nomograms were built on the basis of effective nomograms.
The OS analysis included 640 patients, and the results of the multivariate Cox regression analysis showed that grade, chemotherapy, and liver metastasis were independent prognostic factors for patients with GCLM. The CSS analysis included 524 patients, and the results of the multivariate Cox regression analysis showed that the independent prognostic factors for patients with GCLM were chemotherapy, liver metastasis, marital status, and tumor site. The ROC curves, calibration curves, and DCA revealed favorable predictive power in the OS and CSS nomograms. We created web-based nomograms for OS (https://zhenghh.shinyapps.io/aclmos/) and CSS (https://zhenghh.shinyapps.io/aslmcss/).
We created two web-based nomograms to predict OS and CSS in patients with GCLM. Both web-based nomograms had satisfactory accuracy and clinical usefulness and may help clinicians make individualized treatment decisions for patients.
肺是胃癌最常见的转移部位之一。我们的研究开发了两个列线图，以实现对胃癌肺转移（GCLM）患者总生存期（OS）和癌症特异性生存期（CSS）的个体化预测，从而更好地指导随访及后续治疗方案的制定。
我们回顾了2010年至2015年监测、流行病学和最终结果（SEER）数据库中诊断为GCLM的患者数据。研究的终点为OS和CSS。我们使用“caret”软件包将患者以7∶3的比例随机分为训练队列和验证队列。通过单变量Cox回归分析进行多变量Cox回归分析，以确定独立预后因素。之后，我们使用“rms”软件包构建OS和CSS列线图。随后，我们通过校准曲线、受试者操作特征（ROC）曲线和决策曲线分析（DCA）对这两个列线图进行评估。最后，基于有效的列线图构建了两个基于网络的列线图。
OS分析纳入了640例患者，多变量Cox回归分析结果显示，分级、化疗和肝转移是GCLM患者的独立预后因素。CSS分析纳入了524例患者，多变量Cox回归分析结果显示，GCLM患者的独立预后因素为化疗、肝转移、婚姻状况和肿瘤部位。ROC曲线、校准曲线和DCA显示OS和CSS列线图具有良好的预测能力。我们创建了基于网络的OS列线图（https://zhenghh.shinyapps.io/aclmos/）和CSS列线图（https://zhenghh.shinyapps.io/aslmcss/）。
我们创建了两个基于网络的列线图来预测GCLM患者的OS和CSS。两个基于网络的列线图均具有令人满意的准确性和临床实用性，可能有助于临床医生为患者做出个体化治疗决策。
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