肝癌血清标志物预测模型的建立及诊断价值评估

Evaluation and diagnostic model of serum tumer markers for hepatocellular carcinoma

目的 通过多因素回归分析研究创伤小、低成本、可量化、准确度好的血清学指标,建立模型以准确诊断肝细胞癌(hepatocellular carcinoma,HCC),以期早期识别原发性肝癌,提高患者治愈率和延长生存期。方法 选取山东第一医科大学附属中心医院2017—2020年间收治的188例慢性HBV感染患者进行回顾性分析,其中150例患者入选HBV感染相关非肝癌组;38例患者纳入HBV感染相关肝癌组。应用SPSS 25.0软件和Stata SE 15.0软件进行统计学分析,比较两组患者的一般资料,入院后检测的血常规、生化及病毒学指标。通过单因素筛选及多因素logistic回归建立回归模型,绘制各模型的受试者工作特征(receiver operating characteristic,ROC)曲线并与经典肿瘤学指标对比模型的曲线下面积(area under curve,AUC),计算其灵敏度、特异性,选出诊断HCC的最佳模型。结果 研究建立了联合血清学指标血清唾液酸(serum sialic acid,SA)、门冬氨酸氨基转移酶(aspartate aminotransferase,AST)、谷氨酰转肽酶(glutamyl transferase,GGT)、甲胎蛋白(alpha-fetoprotein,AFP)、HBsAg的多因素回归模型,AUC面积最大,为0.943,显著高于AFP单项的0.816,差异有统计学意义(P=0.004),且与其余曲线无交叉。灵敏度83.9%,特异性92%。结论 对于HBV感染患者,综合血清学指标SA、GGT、AST、HBsAg和AFP建立的回归模型,对于诊断HCC具有较好的诊断效率。

Objective:To find serum markers which are easy to elevate,less invasive and at low cost in order to identify HCC at an early stage and take active and effective treatment by establishing a diagnostic model with multivariate regression analysis which helps to improve the patient′s cure rate and survival rate. Methods:In this retrospective study,188 patients with chronic HBV infection who were treated in Jinan Central Hospital Affiliated to Shandong First Medical University between 2017and 2020 were enrolled. There are 150 patients with HBV infection in non-liver cancer group;38 patients were included in the HBV infection related liver cancer group. Statistical analysis was performed using SPSS 25. 0 and Stata se 15. 0 software to compare the general data,conventional,biochemical and virological parameters detected after admission. The best model was selected by plotting receiver operating characteristic curve(ROC)and comparing the area under the curve(AUC)to calculate the sensitivity and specificity through univariate screening and multi-factor logical regression analysis. Results:In this study,we developed a logistic regression model of the combined serological indicators:SA,AST,GGT,AFP and HBs Ag. The AUC was the largest at 0. 943,which was significantly higher than the AUC = 0. 816(P = 0. 004)of the single item of AFP with no crossover with other curves. Sensitivity was 83. 9%,and specificity was 92%. Conclusion:For patients with HBV infection,the regression model established by comprehensive serological parameters:SA,GGT,AST,HBs Ag and AFP has a good diagnostic efficiency for diagnosing HCC.