Purpose

The prediction of microvascular invasion (MVI) has increasingly been recognized to reflect prognosis involving local invasion and distant metastasis of hepatocellular carcinoma (HCC). The aim of this study was to assess a predictive model using preoperatively accessible clinical parameters and radiographic features developed and validated to predict MVI. This predictive model can distinguish clinical outcomes after liver transplantation (LT) for HCC patients.

Methods

In total, 455 HCC patients who underwent LT between January 1, 2015, and December 31, 2019, were retrospectively enrolled in two centers in China as a training cohort (ZFA center; n = 244) and a test cohort (SLA center; n = 211). Univariate and multivariate backward logistic regression analysis were used to select the significant clinical variables which were incorporated into the predictive nomogram associated with MVI. Receiver operating characteristic (ROC) curves based on clinical parameters were plotted to predict MVI in the training and test sets.

Results

Univariate and multivariate backward logistic regression analysis identified four independent preoperative risk factors for MVI: α-fetoprotein (AFP) level (p < 0.001), tumor size ((p < 0.001), peritumoral star node (p = 0.003), and tumor margin (p = 0.016). The predictive nomogram using these predictors achieved an area under curve (AUC) of 0.85 and 0.80 in the training and test sets. Furthermore, MVI could discriminate different clinical outcomes within the Milan criteria (MC) and beyond the MC.

Conclusions

The nomogram based on preoperatively clinical variables demonstrated good performance for predicting MVI. MVI may serve as a supplement to the MC.

中文翻译：

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一种术前模型，用于预测微血管侵犯并根据经验标准协助肝移植肝移植的预后分层

目的

微血管浸润（MVI）的预测越来越被认为可以反映肝细胞癌（HCC）局部浸润和远处转移的预后。本研究的目的是使用术前可访问的临床参数和放射学特征来评估预测模型，该模型已开发和验证以预测 MVI。该预测模型可以区分 HCC 患者肝移植 (LT) 后的临床结果。

方法

总共有 455 名在 2015 年 1 月 1 日至 2019 年 12 月 31 日期间接受 LT 的 HCC 患者作为培训队列（ZFA 中心；n = 244）和测试队列（SLA 中心；n = 211）。使用单变量和多变量向后逻辑回归分析选择重要的临床变量，这些变量被纳入与 MVI 相关的预测列线图中。绘制基于临床参数的受试者工作特征 (ROC) 曲线以预测训练和测试集中的 MVI。

结果

单变量和多变量后向逻辑回归分析确定了 MVI 的四个独立的术前危险因素：甲胎蛋白 (AFP) 水平 ( p  < 0.001)、肿瘤大小 (( p  < 0.001)、瘤周星形节点 ( p  = 0.003) 和肿瘤边缘( p  = 0.016). 使用这些预测因子的预测列线图在训练和测试集中实现了 0.85 和 0.80 的曲线下面积 (AUC)。此外，MVI 可以区分米兰标准 (MC) 内和 MC 之外的不同临床结果.

结论

基于术前临床变量的列线图在预测 MVI 方面表现良好。MVI 可作为 MC 的补充。