跳转至内容
Merck
CN
  • Prospective and prognostic factors for hepatic metastasis of gastric carcinoma: A retrospective analysis.

Prospective and prognostic factors for hepatic metastasis of gastric carcinoma: A retrospective analysis.

Journal of cancer research and therapeutics (2019-04-10)
Jin Cheng Song, Xiao Lei Ding, Yang Zhang, Xian Zhang, Xiu Hua Sun
摘要

The aim of the study was to prospectively explore the prognostic factor for gastric cancer with liver metastasis (GCLM), since no prognostic factor was reported to be consistently significant across studies. One hundred and five patients with GCLM treated at our center between January 1, 2010, and March 31, 2016, were included and their clinical data were retrospectively analyzed. The univariate analyses were first applied for identify the potential independent prognostic and predictive factors for liver metastasis. These factors were further evaluated with Cox proportional-hazard regression model testing. Finally, survival curves were estimated. The Eastern Cooperative Oncology Group (ECOG) score, number of other distant metastases, levels of cancer antigen (CA), and carcinoembryonic antigen (CEA) were independent prognostic factors (adjusted relative risk [RR]: 1.362-2.887; P = 0.000-0.027). The survival of patients who received radical gastrectomy would be associated with the ECOG score, staging (T stage and N stage), CA 19-9, and CEA levels (RR: 2.169-3.787; P = 0.000-0.027). Patients with following indicators 1 month postoperatively were prone to liver metastasis after radical gastrectomy (median, 6.9-12.03 months; P = 0.007-0.042): Venous/lymphatic invasion, pathological Stage IV (especially combined with T4 stage), intestinal Lauren type, and combined elevation of CEA and CA 19-9 levels. The therapy design for patients with GCLM should consider the general conditions and personal clinicopathological characters of patients. After balancing the benefit and risk factors, multidisciplinary treatment and individual treatment should be developed based on evidence-based medicine model for each patient.