Li Shumin, Zeng Qianwen, Liang Ruiming, Long Jianyan, Liu Yihao, Xiao Han, Sun Kaiyu
Department of Gastroenterology and Hepatology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Department of Liver Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Front Surg. 2022 Mar 4;9:833779. doi: 10.3389/fsurg.2022.833779. eCollection 2022.
BACKGROUND: Mounting studies reveal the relationship between inflammatory markers and post-therapy prognosis. Yet, the role of the systemic inflammatory indices in preoperative microvascular invasion (MVI) prediction for hepatocellular carcinoma (HCC) remains unclear. PATIENTS AND METHODS: In this study, data of 1,058 cases of patients with HCC treated in the First Affiliated Hospital of Sun Yat-sen University from February 2002 to May 2018 were collected. Inflammatory factors related to MVI diagnosis in patients with HCC were selected by least absolute shrinkage and selection operator (LASSO) regression analysis and were integrated into an "Inflammatory Score." A prognostic nomogram model was established by combining the inflammatory score and other independent factors determined by multivariate logistic regression analysis. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used to evaluate the predictive efficacy of the model. RESULTS: Sixteen inflammatory factors, including neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, etc., were selected by LASSO regression analysis to establish an inflammatory score. Multivariate logistic regression analysis showed that inflammatory score (OR = 2.14, 95% CI: 1.63-2.88, < 0.001), alpha fetoprotein (OR = 2.02, 95% CI: 1.46-2.82, < 0.001), and tumor size (OR = 2.37, 95% CI: 1.70-3.30, < 0.001) were independent factors for MVI. These three factors were then used to establish a nomogram for MVI prediction. The AUC for the training and validation group was 0.72 (95% CI: 0.68-0.76) and 0.72 (95% CI: 0.66-0.78), respectively. CONCLUSION: These findings indicated that the model drawn in this study has a high predictive value which is capable to assist the diagnosis of MVI in patients with HCC.
背景:越来越多的研究揭示了炎症标志物与治疗后预后之间的关系。然而,全身炎症指标在肝细胞癌(HCC)术前微血管侵犯(MVI)预测中的作用仍不明确。 患者与方法:本研究收集了2002年2月至2018年5月在中山大学附属第一医院接受治疗的1058例HCC患者的数据。通过最小绝对收缩和选择算子(LASSO)回归分析选择与HCC患者MVI诊断相关的炎症因子,并将其整合为“炎症评分”。通过将炎症评分与多因素逻辑回归分析确定的其他独立因素相结合,建立了预后列线图模型。采用受试者操作特征(ROC)曲线和曲线下面积(AUC)评估模型的预测效能。 结果:通过LASSO回归分析选择了16种炎症因子,包括中性粒细胞与淋巴细胞比值、血小板与淋巴细胞比值等,以建立炎症评分。多因素逻辑回归分析显示,炎症评分(OR = 2.14,95%CI:1.63 - 2.88,P < 0.001)、甲胎蛋白(OR = 2.02,95%CI:1.46 - 2.82,P < 0.001)和肿瘤大小(OR = 2.37,95%CI:1.70 - 3.30,P < 0.001)是MVI的独立因素。然后利用这三个因素建立了MVI预测列线图。训练组和验证组的AUC分别为0.72(95%CI:0.68 - 0.76)和0.72(95%CI:0.66 - 0.78)。 结论:这些研究结果表明,本研究构建的模型具有较高的预测价值,能够辅助HCC患者MVI的诊断。
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