Huang Yue, Chen Lingfeng, Ding Qingzhu, Zhang Han, Zhong Yun, Zhang Xiang, Weng Shangeng
Department of Hepatobiliary Pancreatic Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
Fujian Abdominal Surgery Research Institute, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
Front Oncol. 2024 Apr 16;14:1295575. doi: 10.3389/fonc.2024.1295575. eCollection 2024.
To construct and validate radiomics models for hepatocellular carcinoma (HCC) grade predictions based on contrast-enhanced CT (CECT).
Patients with pathologically confirmed HCC after surgery and underwent CECT at our institution between January 2016 and December 2020 were enrolled and randomly divided into training and validation datasets. With tumor segmentation and feature extraction, radiomic models were constructed using univariate analysis, followed by least absolute shrinkage and selection operator (LASSO) regression. In addition, combined models with clinical factors and radiomics scores (Radscore) were constructed using logistic regression. Finally, all models were evaluated using the receiver operating characteristic (ROC) curve with the area under the curve (AUC), calibration curve, and decision curve analysis (DCA).
In total 242 patients were enrolled in this study, of whom 170 and 72 formed the training and validation datasets, respectively. The arterial phase and portal venous phase (AP+VP) radiomics model were evaluated as the best for predicting HCC pathological grade among all the models built in our study (AUC = 0.981 in the training dataset; AUC = 0.842 in the validation dataset) and was used to build a nomogram. Furthermore, the calibration curve and DCA indicated that the AP+VP radiomics model had a satisfactory prediction efficiency.
Low- and high-grade HCC can be distinguished with good diagnostic performance using a CECT-based radiomics model.
构建并验证基于对比增强CT（CECT）的肝细胞癌（HCC）分级预测的放射组学模型。
纳入2016年1月至2020年12月期间在本机构接受手术且病理确诊为HCC并接受CECT检查的患者，并将其随机分为训练集和验证集。通过肿瘤分割和特征提取，采用单变量分析构建放射组学模型，随后进行最小绝对收缩和选择算子（LASSO）回归。此外，使用逻辑回归构建包含临床因素和放射组学评分（Radscore）的联合模型。最后，使用受试者工作特征（ROC）曲线、曲线下面积（AUC）、校准曲线和决策曲线分析（DCA）对所有模型进行评估。
本研究共纳入242例患者，其中170例和72例分别构成训练集和验证集。在我们构建的所有模型中，动脉期和门静脉期（AP + VP）放射组学模型被评估为预测HCC病理分级的最佳模型（训练集中AUC = 0.981；验证集中AUC = 0.842），并用于构建列线图。此外，校准曲线和DCA表明AP + VP放射组学模型具有令人满意的预测效率。
使用基于CECT的放射组学模型可以很好地区分低级别和高级别HCC，诊断性能良好。
