Yu Heng, Jiang Hang, Lu Xiaofeng, Bai Chunhua, Song Peng, Sun Feng, Ai Shichao, Yin Yi, Hu Qiongyuan, Liu Song, Chen Xin, Du Junfeng, Shen Xiaofei, Guan Wenxian
Department of General Surgery, Nanjing Drum Tower Clinical College of Xuzhou Medical University, Nanjing, 210008, China.
Department of General Surgery, Drum Tower Clinical Medical College of Nanjing Medical University, Nanjing, 210008, China.
Discov Oncol. 2024 Aug 21;15(1):363. doi: 10.1007/s12672-024-01246-z.
To retrospectively analyze the risk factors of liver metastases in patients with gastric cancer in a single center, and to establish a Nomogram prediction model to predict the occurrence of liver metastases.
A total of 96 patients with gastric cancer who were also diagnosed with liver metastasis (GCLM) and treated in our center from January 1, 2010 to December 31, 2020 were included. The clinical data of 1095 patients with gastric cancer who were diagnosed without liver metastases (GC) in our hospital from January 1, 2014 to December 31, 2017 were retrospectively compared by univariate and multivariate logistic regression. 309 patients diagnosed with gastric cancer in another medical center from January 1, 2014 to December 31, 2018 were introduced as external validation cohorts.
Based on the training cohort, multivariate analysis revealed that tumor site (OR = 0.55, P = 0.046), N stage (OR = 4.95, P = 0.004), gender (OR = 0.04, P = 0.001), OPNI (OR = 0.95, P = 0.041), CEA (OR = 1.01, P = 0.018), CA724 (OR = 1.01, P = 0.006), CA242 (OR = 1.01, P = 0.006), WBC (OR = 1.13, P = 0.024), Hb (OR = 0.98, P < 0.001) were independent risk factors for liver metastasis in patients with gastric cancer, and Nomogram was established based on this analysis (C-statistics = 0.911, 95%CI 0.880-0.958), and the C-statistics of the external validation cohorts achieved 0.926. ROC analysis and decision curve analysis (DCA) revealed that the nomogram provided superior diagnostic value than single variety.
By innovatively introducing a new tumor location classification method, systemic inflammatory response indicators such as NLR and PLR, and nutritional index OPNI, the risk factors of gastric cancer liver metastasis were determined and a predictive Nomogram model was established, which can provide clinical prediction for patients with gastric cancer liver metastasis.
回顾性分析单中心胃癌患者肝转移的危险因素,并建立列线图预测模型以预测肝转移的发生。
纳入2010年1月1日至2020年12月31日在本中心接受治疗且同时诊断为肝转移的96例胃癌患者(GCLM)。回顾性比较2014年1月1日至2017年12月31日在我院诊断为无肝转移的1095例胃癌患者(GC)的临床资料,采用单因素和多因素逻辑回归分析。引入2014年1月1日至2018年12月31日在另一医疗中心诊断为胃癌的309例患者作为外部验证队列。
基于训练队列,多因素分析显示肿瘤部位(OR = 0.55,P = 0.046)、N分期(OR = 4.95,P = 0.004)、性别(OR = 0.04,P = 0.001)、OPNI(OR = 0.95,P = 0.041)、CEA(OR = 1.01,P = 0.018)、CA724(OR = 1.01,P = 0.006)、CA242(OR = 1.01,P = 0.006)、WBC(OR = 1.13,P = 0.024)、Hb(OR = 0.98,P < 0.001)是胃癌患者肝转移的独立危险因素,并基于此分析建立列线图(C统计量 = 0.911,95%CI 0.880 - 0.958),外部验证队列的C统计量达到0.926。ROC分析和决策曲线分析(DCA)显示列线图比单一指标具有更高的诊断价值。
通过创新性引入新的肿瘤位置分类方法、NLR和PLR等全身炎症反应指标以及营养指标OPNI,确定了胃癌肝转移的危险因素并建立了预测列线图模型,可为胃癌肝转移患者提供临床预测。