An Wenxiu, Bao Lijie, Wang Chenyu, Zheng Mingxin, Zhao Yan
Cancer Hospital of Dalian University of Technology (Liaoning Cancer Hospital & Institute), Shenyang City, Liaoning Province, People's Republic of China.
Liaoning Provincial Key Laboratory of Interdisciplinary Research on Gastrointestinal Tumor Combining Medicine with Engineering, Shenyang City, Liaoning Province, People's Republic of China.
Int J Gen Med. 2023 Dec 19;16:5969-5978. doi: 10.2147/IJGM.S434952. eCollection 2023.
Gastric cancer (GC) has a poor prognosis, particularly in patients with liver metastasis (LM). This study aims to identify relevant factors associated with the occurrence of LM in GC patients and factors influencing the prognosis of gastric cancer with liver metastasis (GCLM) patients, in addition to developing diagnostic and prognostic nomograms specifically.
Overall, 6184 training data were from the Surveillance, Epidemiology, and End Results (SEER) database from 2011 to 2015. 1527 validation data were from our hospital between January 2018 and December 2022. Logistic regression was used to identify the risk factors associated with the occurrence of LM in GC patients, Cox regression was used to confirm the prognostic factors of GCLM patients. Two nomogram models were established to predict the risk and overall survival (OS) of patients with GCLM. The performance of the two models was evaluated using the area under the curve (AUC), concordance index (C-index), and calibration curves.
A nomogram included five independent factors from multivariate logistic regression: sex, lymph node removal, chemotherapy, T stage and N stage were constructed to calculate the possibility of LM. Internal and external verifications of AUC were 0.786 and 0.885, respectively. The other nomogram included four independent factors from multivariate Cox regression: surgery at primary site, surgery at other site, chemotherapy, and N stage were constructed to predict OS. C-index for internal and external validations were 0.714 and 0.702, respectively, and the calibration curves demonstrated the robust discriminative ability of the models.
Based on the SEER database and validation data, we defined effective nomogram models to predict risk and OS in patients with GCLM. They have important value in clinical decision-making and personalized treatment.
胃癌（GC）预后较差，尤其是发生肝转移（LM）的患者。本研究旨在确定与GC患者发生LM相关的因素以及影响胃癌伴肝转移（GCLM）患者预后的因素，此外还专门开发诊断和预后列线图。
总体而言，6184条训练数据来自2011年至2015年的监测、流行病学和最终结果（SEER）数据库。1527条验证数据来自2018年1月至2022年12月我院。采用逻辑回归确定与GC患者发生LM相关的危险因素，采用Cox回归确定GCLM患者的预后因素。建立了两个列线图模型来预测GCLM患者的风险和总生存期（OS）。使用曲线下面积（AUC）、一致性指数（C-index）和校准曲线评估这两个模型的性能。
一个列线图纳入了多变量逻辑回归的五个独立因素：性别、淋巴结清扫、化疗、T分期和N分期，用于计算LM的可能性。AUC的内部验证和外部验证分别为0.786和0.885。另一个列线图纳入了多变量Cox回归的四个独立因素：原发部位手术、其他部位手术、化疗和N分期，用于预测OS。内部验证和外部验证的C-index分别为0.714和0.702，校准曲线显示了模型强大的鉴别能力。
基于SEER数据库和验证数据，我们定义了有效的列线图模型来预测GCLM患者的风险和OS。它们在临床决策和个性化治疗中具有重要价值。
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