Zhang Jianfeng, Zeng Fanxin, Jiang Shijie, Tang Hui, Zhang Jian
Department of Liver Transplantation, The Third Affiliated Hospital of Sun Yat-sen University, 600 Tianhe Road, Guangzhou 510630, China.
Department of Liver Transplantation, The Third Affiliated Hospital of Sun Yat-sen University, 600 Tianhe Road, Guangzhou 510630, China.
HPB (Oxford). 2023 Jan;25(1):45-53. doi: 10.1016/j.hpb.2022.08.007. Epub 2022 Aug 20.
Microvascular invasion (MVI) is an adverse factor for the prognosis of patients with hepatocellular carcinoma (HCC). We aimed to construct a preoperative prediction model for MVI, thereby providing a reference for clinicians in formulating treatment options for HCC.
A total of 360 patients with non-metastatic HCC were retrospectively enrolled. We used logistic regression analysis to screen out independent risk factors for MVI and further constructed a predictive model for MVI. The performance of the model was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).
Logistic regression analysis revealed that fibrinogen (>4 g/L) (OR: 6.529), alpha-fetoprotein (≥ 400 ng/mL) (OR: 2.676), cirrhosis (OR: 2.25), tumor size (OR: 1.239), and poor tumor border (OR: 3.126) were independent risk factors of MVI. The prediction model of MVI had C-index of 0.746 and 0.772 in the training and validation cohorts, respectively. The calibration curves showed good agreement between actual and predicted MVI risk. Finally, DCA reveals that this model has good clinical utility.
The nomogram-based model we established can predict the preoperative MVI well and provides reference for surgeons to make clinical treatment decisions.
微血管侵犯（MVI）是肝细胞癌（HCC）患者预后的不利因素。我们旨在构建一个术前MVI预测模型，从而为临床医生制定HCC治疗方案提供参考。
回顾性纳入360例非转移性HCC患者。我们采用逻辑回归分析筛选出MVI的独立危险因素，并进一步构建MVI预测模型。通过受试者工作特征（ROC）曲线、校准曲线和决策曲线分析（DCA）评估模型的性能。
逻辑回归分析显示，纤维蛋白原（>4 g/L）（OR：6.529）、甲胎蛋白（≥400 ng/mL）（OR：2.676）、肝硬化（OR：2.25）、肿瘤大小（OR：1.239）和肿瘤边界不清（OR：3.126）是MVI的独立危险因素。MVI预测模型在训练队列和验证队列中的C指数分别为0.746和0.772。校准曲线显示实际和预测的MVI风险之间具有良好的一致性。最后，DCA显示该模型具有良好的临床实用性。
我们建立的基于列线图的模型能够很好地预测术前MVI，并为外科医生做出临床治疗决策提供参考。
