基于列线图分析的肝细胞癌微血管浸润预测及其M2分类,Frontiers in Oncology

微血管浸润（MVI）作为关键的病理因素，尤其是其M2级，极大地影响着肝癌患者的预后。准确的术前预测 MVI 及其 M2 分类可以帮助临床医生做出最佳治疗决策。因此，我们旨在建立有效的列线图来预测 MVI 及其 M2 等级。

回顾性收集2015年1月至2020年9月接受肝癌根治性切除术的111例患者。我们利用逻辑回归和最小绝对收缩和选择算子 (LASSO) 回归来确定 MVI 及其 M2 分类的独立预测因素。计算综合鉴别改进（IDI）和净重分类改进（NRI），以从LASSO和逻辑回归的结果中选择潜在的预测因素。然后通过结合这些因素开发用于预测 MVI 及其 M2 等级的列线图。曲线下面积（AUC）、校准曲线和决策曲线分析（DCA）分别用于评估列线图的有效性、准确性和临床效用。

结合LASSO回归、逻辑回归、IDI和NRI分析结果，我们发现临床肿瘤-淋巴结-转移（TNM）分期、肿瘤大小、Edmondson-Steiner分类、甲胎蛋白（AFP）、肿瘤包膜、肿瘤边缘和肿瘤数量是 MVI 的独立危险因素。在 MVI 阳性患者中，只有临床 TNM 分期、肿瘤包膜、肿瘤边缘和肿瘤数量与 M2 分级高度相关。通过合并上述变量建立的列线图在预测MVI（AUC MVI = 0.926）及其M2分类（AUC M2 = 0.803）方面具有良好的性能。校准曲线证实了预测和实际观察结果非常吻合。DCA 证明了我们的列线图的显着临床效用。

本研究的列线图可以对 MVI 及其 M2 分类进行个性化预测，这可能有助于我们选择合适的治疗计划。

"点击查看英文标题和摘要"

Background and Aims

As a key pathological factor, microvascular invasion (MVI), especially its M2 grade, greatly affects the prognosis of liver cancer patients. Accurate preoperative prediction of MVI and its M2 classification can help clinicians to make the best treatment decision. Therefore, we aimed to establish effective nomograms to predict MVI and its M2 grade.

A total of 111 patients who underwent radical resection of hepatocellular carcinoma (HCC) from January 2015 to September 2020 were retrospectively collected. We utilized logistic regression and least absolute shrinkage and selection operator (LASSO) regression to identify the independent predictive factors of MVI and its M2 classification. Integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were calculated to select the potential predictive factors from the results of LASSO and logistic regression. Nomograms for predicting MVI and its M2 grade were then developed by incorporating these factors. Area under the curve (AUC), calibration curve, and decision curve analysis (DCA) were respectively used to evaluate the efficacy, accuracy, and clinical utility of the nomograms.

Combined with the results of LASSO regression, logistic regression, and IDI and NRI analyses, we founded that clinical tumor-node-metastasis (TNM) stage, tumor size, Edmondson–Steiner classification, α-fetoprotein (AFP), tumor capsule, tumor margin, and tumor number were independent risk factors for MVI. Among the MVI-positive patients, only clinical TNM stage, tumor capsule, tumor margin, and tumor number were highly correlated with M2 grade. The nomograms established by incorporating the above variables had a good performance in predicting MVI (AUCMVI = 0.926) and its M2 classification (AUCM2 = 0.803). The calibration curve confirmed that predictions and actual observations were in good agreement. Significant clinical utility of our nomograms was demonstrated by DCA.

The nomograms of this study make it possible to do individualized predictions of MVI and its M2 classification, which may help us select an appropriate treatment plan.