Zhong Yun, Chen Lingfeng, Ding Fadian, Ou Wenshi, Zhang Xiang, Weng Shangeng
Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Fujian Abdominal Surgery Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Front Oncol. 2024 Sep 16;14:1401095. doi: 10.3389/fonc.2024.1401095. eCollection 2024.
The early recurrence of hepatocellular carcinoma (HCC) correlates with decreased overall survival. Microvascular invasion (MVI) stands out as a prominent hazard influencing post-resection survival status and metastasis in patients with HBV-related HCC. The study focused on developing a web-based nomogram for preoperative prediction of MVI in HBV-HCC.
173 HBV-HCC patients from 2017 to 2022 with complete preoperative clinical data and Gadopentetate dimeglumine-enhanced magnetic resonance images were randomly divided into two groups for the purpose of model training and validation, using a ratio of 7:3. MRI signatures were extracted by pyradiomics and the deep neural network, 3D ResNet. Clinical factors, blood-cell-inflammation markers, and MRI signatures selected by LASSO were incorporated into the predictive nomogram. The evaluation of the predictive accuracy involved assessing the area under the receiver operating characteristic (ROC) curve (AUC), the concordance index (C-index), along with analyses of calibration and decision curves.
Inflammation marker, neutrophil-to-lymphocyte ratio (NLR), was positively correlated with independent MRI radiomics risk factors for MVI. The performance of prediction model combined serum AFP, AST, NLR, 15 radiomics features and 7 deep features was better than clinical and radiomics models. The combined model achieved C-index values of 0.926 and 0.917, with AUCs of 0.911 and 0.907, respectively.
NLR showed a positive correlation with MRI radiomics and deep learning features. The nomogram, incorporating NLR and MRI features, accurately predicted individualized MVI risk preoperatively.
肝细胞癌（HCC）的早期复发与总生存期降低相关。微血管侵犯（MVI）是影响乙肝相关HCC患者切除术后生存状态和转移的一个突出危险因素。本研究旨在开发一种基于网络的列线图，用于术前预测乙肝相关HCC中的MVI。
将2017年至2022年的173例具有完整术前临床资料和钆喷酸葡胺增强磁共振图像的乙肝相关HCC患者，按照7:3的比例随机分为两组，用于模型训练和验证。通过影像组学和深度神经网络3D ResNet提取MRI特征。将临床因素、血细胞炎症标志物以及通过LASSO选择的MRI特征纳入预测列线图。预测准确性的评估包括评估受试者操作特征（ROC）曲线下面积（AUC）、一致性指数（C指数），以及校准分析和决策曲线分析。
炎症标志物中性粒细胞与淋巴细胞比值（NLR）与MVI的独立MRI影像组学危险因素呈正相关。结合血清甲胎蛋白（AFP）、谷草转氨酶（AST）、NLR、15个影像组学特征和7个深度特征的预测模型性能优于临床模型和影像组学模型。联合模型的C指数值分别为0.926和0.917，AUC分别为0.911和0.907。
NLR与MRI影像组学及深度学习特征呈正相关。纳入NLR和MRI特征的列线图可准确术前预测个体化MVI风险。
