Integrated Nomograms for Preoperative Prediction of Microvascular Invasion and Lymph Node Metastasis Risk in Hepatocellular Carcinoma Patients
Integrated Nomograms for Preoperative Prediction of Microvascular Invasion and Lymph Node Metastasis Risk in Hepatocellular Carcinoma Patients
复制标题
术前预测肝细胞癌患者微血管侵犯和淋巴结转移风险的综合列线图
DOI：
10.1245/s10434-019-08071-7
复制
发表时间：
2020-05-01
影响因子：
3.7
通讯作者：
Xiao, Zhiyu
中科院分区：
文献类型：
--
作者：
Yan, Yongcong;Zhou, Qianlei;Xiao, Zhiyu
BackgroundThe目前的工作的目的是开发和验证准确的术前列线图预测微血管浸润（MVI）和淋巴结转移（LNM）在hepatocellularcarcinoma.Patients和MethodsA共268例切除的肝细胞癌（HCC）被分为一个训练集（n= 180），在早期，和验证集（n= 88），此后。MVI和LNM的危险因素基于逻辑回归进行评估。使用最小绝对收缩和选择算子算法建立血液签名。通过结合风险因素和血液特征构建列线图。使用训练集评价性能，并使用验证集进行验证。决策曲线analysis.ResultsThe MVI的危险因素是肝炎B病毒（HBV）DNA载量，门静脉高压症，巴塞罗那肝脏诊所（BCLC）阶段，和三个计算机断层扫描（CT）成像功能，即肿瘤的数量，大小和封装，而只有BCLC阶段，Child-Pugh分类，肿瘤封装与LNM。与LNM列线图（C指数为0.765和0.717）相比，纳入风险因素和血液特征的列线图在预测训练集和验证集中的MVI方面取得了更好的性能（C指数为0.828和0.804）。校准曲线也显示出良好的拟合。决策曲线表明显着的临床usefuls.ConclusionsThe新的验证诺模图HCC患者在此提出的非侵入性术前工具，可以有效地预测MVI和LNM的个性化风险，这种预测能力可以帮助医生解释疾病的患者咨询。
BackgroundThe aim of the present work is to develop and validate accurate preoperative nomograms to predict microvascular invasion (MVI) and lymph node metastasis (LNM) in hepatocellular carcinoma.Patients and MethodsA total of 268 patients with resected hepatocellular carcinoma (HCC) were divided into a training set (n= 180), in an earlier period, and a validation set (n= 88), thereafter. Risk factors for MVI and LNM were assessed based on logistic regression. Blood signatures were established using the least absolute shrinkage and selection operator algorithm. Nomograms were constructed by combining risk factors and blood signatures. Performance was evaluated using the training set and validated using the validation set. The clinical values of the nomograms were measured by decision curve analysis.ResultsThe risk factors for MVI were hepatitis B virus (HBV) DNA loading, portal hypertension, Barcelona liver clinic (BCLC) stage, and three computerized tomography (CT) imaging features, namely tumor number, size, and encapsulation, while only BCLC stage, Child–Pugh classification, and tumor encapsulation were associated with LNM. The nomogram incorporating both risk factors and blood signatures achieved better performance in predicting MVI in the training and validation sets (C-indexes of 0.828 and 0.804) than the LNM nomogram (C-indexes of 0.765 and 0.717). Calibration curves also demonstrated a good fit. The decision curves indicate significant clinical usefulness.ConclusionsThe novel validated nomograms for HCC patients presented herein are noninvasive preoperative tools that can effectively predict the individualized risk of MVI and LNM, and this predictive power can aid doctors in explaining the illness for patient counseling.