Wu L L, Cai M Z, Wang B G, Deng J Y, Ke B, Zhang R P, Liang H, Wang X N
Department of Gastric Cancer, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin 300060, China.
Zhonghua Wei Chang Wai Ke Za Zhi. 2023 Jul 25;26(7):680-688. doi: 10.3760/cma.j.cn441530-20221018-00415.
To investigate the prognostic value of preoperative inflammatory and nutritional condition detection in the postoperative survival, and establish a prognostic model for predicting the survival of patients with gastric cancer. The clinicopathological data of 1123 patients with gastric cancer who had undergone radical gastrectomy in Tianjin Medical University Cancer Institute & Hospital from January 2005 to December 2014 were retrospectively analyzed. Patients with history of other malignancy, with history of gastrectomy, who had received preoperative treatment, who died during the initial hospital stay or first postoperative month, and missing clinical and pathological information were excluded. Cox univariate and multivariate analyses were used to identify independent clinicopathological factors associated with the survival of these gastric cancer patients. Cox univariate analysis was used to identify preoperative inflammatory and nutritional indexes related to the survival of patients with gastric cancer after radical gastrectomy. Moreover, the Cox proportional regression model for multivariate survival analysis (forward stepwise regression method based on maximum likelihood estimation) was used. The independent clinicopathological factors that affect survival were incorporated into the following three new prognostic models: (1) an inflammatory model: significant preoperative inflammatory indexes identified through clinical and univariate analysis; (2) a nutritional model: significant preoperative nutritional indexes identified through clinical and univariate analysis; and (3) combined inflammatory/nutritional model: significant preoperative inflammatory and nutritional indexes identified through clinical and univariate analysis. A model that comprised only pT and pN stages in tumor TNM staging was used as a control model. The integrated area under the receiver operating characteristic curve (iAUC) and C-index were used to evaluate the discrimination of the model. Model fitting was evaluated by Akaike information criterion analysis. Calibration curves were used to assess agreement between the predicted probabilities and actual probabilities at 3-year or 5-year overall survival (OS). The study cohort comprised 1 123 patients with gastric cancer. The mean age was 58.9±11.6 years, and 783 were males. According to univariate analysis, age, surgical procedure, extent of lymph node dissection, tumor location, maximum tumor size, number of examined lymph nodes, pT stage, pN stage, and nerve invasion were associated with 5-year OS after radical gastrectomy for gastric cancer (all <0.050). Multivariate analysis further identified age (HR: 1.18, 95%CI: 1.03-1.36, =0.019), maximum tumor size (HR: 1.19, 95%CI: 1.03-1.38, =0.022), number of examined lymph nodes (HR: 0.79, 95%CI: 0.68-0.92, =0.003), pT stage (HR: 1.40, 95%CI: 1.26-1.55, <0.001) and pN stage (HR: 1.28, 95%CI: 1.21-1.35, <0.001) as independent prognostic factors for OS of gastric cancer patients. Additionally, according to univariate survival analysis, the preoperative inflammatory markers of neutrophil count, percentage of neutrophils, neutrophil/lymphocyte ratio, platelet/neutrophil ratio and preoperative nutritional indicators of serum albumin and body mass index were potential prognostic factors for gastric cancer (all <0.05). On the basis of the above results, three models for prediction of prognosis were constructed. Variables included in the three models are as follows. (1) Inflammatory model: age, maximum tumor size, number of examined lymph nodes, pT stage, pN stage, percentage of neutrophils, and neutrophil-lymphocyte ratio; (2) nutritional model: age, maximum tumor size, number of examined lymph nodes, pT stage, pN stage, and serum albumin; and (3) combined inflammatory/nutritional model: age, maximum tumor size, number of examined lymph nodes, pT stage, pN stage, percentage of neutrophils, neutrophil-lymphocyte ratio, and serum albumin. We found that the predictive accuracy of the combined inflammatory/nutritional model, which incorporates both inflammatory indicators and nutrition indicators (iAUC: 0.676, 95% CI: 0.650-0.719, C-index: 0.698),was superior to that of the inflammation model (iAUC: 0.662, 95% CI: 0.673-0.706;C-index: 0.675), nutritional model (iAUC: 0.666, 95% CI: 0.642-0.698, C-index: 0.672), and TNM staging control model (iAUC: 0.676, 95% CI: 0.650-0.719, C-index: 0.658). Furthermore, the combined inflammatory/nutritional model had better fitting performance (AIC: 10 762) than the inflammatory model (AIC: 10 834), nutritional model (AIC: 10 810), and TNM staging control model (AIC: 10 974). Preoperative percentage of neutrophils, NLR, and BMI have predictive value for the prognosis of gastric cancer patients. The inflammatory / nutritional model can be used to predict the survival and prognosis of gastric cancer patients on an individualized basis.
探讨术前炎症和营养状况检测对术后生存的预后价值,并建立预测胃癌患者生存的预后模型。回顾性分析2005年1月至2014年12月在天津医科大学肿瘤医院接受根治性胃切除术的1123例胃癌患者的临床病理资料。排除有其他恶性肿瘤病史、胃切除术史、接受过术前治疗、在初次住院期间或术后第一个月死亡以及临床和病理信息缺失的患者。采用Cox单因素和多因素分析确定与这些胃癌患者生存相关的独立临床病理因素。Cox单因素分析用于确定与根治性胃切除术后胃癌患者生存相关的术前炎症和营养指标。此外,采用多因素生存分析的Cox比例回归模型(基于最大似然估计的向前逐步回归法)。将影响生存的独立临床病理因素纳入以下三个新的预后模型:(1)炎症模型:通过临床和单因素分析确定的术前显著炎症指标;(2)营养模型:通过临床和单因素分析确定的术前显著营养指标;(3)炎症/营养联合模型:通过临床和单因素分析确定的术前显著炎症和营养指标。将仅包含肿瘤TNM分期中的pT和pN分期的模型用作对照模型。采用受试者操作特征曲线下的综合面积(iAUC)和C指数评估模型的辨别力。通过赤池信息准则分析评估模型拟合。校准曲线用于评估3年或5年总生存(OS)时预测概率与实际概率之间的一致性。研究队列包括1123例胃癌患者。平均年龄为58.9±11.6岁,男性783例。单因素分析显示,年龄、手术方式、淋巴结清扫范围、肿瘤位置、最大肿瘤大小、检查淋巴结数目、pT分期、pN分期和神经侵犯与胃癌根治术后5年OS相关(均<0.050)。多因素分析进一步确定年龄(HR:1.18,95%CI:1.03-1.36,P=0.019)、最大肿瘤大小(HR:1.19,95%CI:1.03-1.38,P=0.022)、检查淋巴结数目(HR:0.79,95%CI:0.68-0.92,P=0.003)、pT分期(HR:1.40,95%CI:1.26-1.55,P<0.001)和pN分期(HR:1.28,95%CI:1.21-1.35,P<0.001)为胃癌患者OS的独立预后因素。此外,根据单因素生存分析,术前中性粒细胞计数、中性粒细胞百分比、中性粒细胞/淋巴细胞比值、血小板/中性粒细胞比值等炎症标志物以及血清白蛋白和体重指数等术前营养指标是胃癌的潜在预后因素(均<0.05)。基于上述结果,构建了三个预后预测模型。三个模型中包含的变量如下。(1)炎症模型:年龄、最大肿瘤大小、检查淋巴结数目、pT分期、pN分期、中性粒细胞百分比和中性粒细胞-淋巴细胞比值;(2)营养模型:年龄、最大肿瘤大小、检查淋巴结数目、pT分期、pN分期和血清白蛋白;(3)炎症/营养联合模型:年龄、最大肿瘤大小、检查淋巴结数目、pT分期、pN分期、中性粒细胞百分比、中性粒细胞-淋巴细胞比值和血清白蛋白。我们发现,将炎症指标和营养指标结合的炎症/营养联合模型的预测准确性(iAUC:0.676,95%CI:0.650-0.719,C指数:0.698)优于炎症模型(iAUC:0.662,95%CI:0.673-0.706;C指数:0.675)、营养模型(iAUC:0.666,95%CI:0.642-0.698,C指数: