Qu Chengming, Wang Qiang, Li Changfeng, Xie Qiao, Cai Ping, Yan Xiaochu, Sparrelid Ernesto, Zhang Leida, Ma Kuansheng, Brismar Torkel B
Institute of Hepatobiliary Surgery, Southwest Hospital, Army Medical University, Chongqing, China.
Division of Medical Imaging and Technology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm, Sweden.
Front Oncol. 2022 May 19;12:831795. doi: 10.3389/fonc.2022.831795. eCollection 2022.
The aim of this study is to establish and validate a radiomics-based model using preoperative Gd-EOB-DTPA-enhanced MRI to predict microvascular invasion (MVI) in patients with hepatocellular carcinoma ≤ 5 cm.
Clinicopathologic and MRI data of 178 patients with solitary hepatocellular carcinoma (HCC) (≤5 cm) were retrospectively collected from a single medical center between May 2017 and November 2020. Patients were randomly assigned into training and test subsets by a ratio of 7:3. Imaging features were extracted from the segmented tumor volume of interest with 1-cm expansion on arterial phase (AP) and hepatobiliary phase (HBP) images. Different models based on the significant clinical risk factors and/or selected imaging features were established and the predictive performance of the models was evaluated.
Three radiomics models, the AP_model, the HBP_model, and the AP+HBP_model, were constructed for MVI prediction. Among them, the AP+HBP_model outperformed the other two. When it was combined with a clinical model, consisting of tumor size and alpha-fetoprotein (AFP), the combined model (AP+HBP+Clin_model) showed an area under the curve of 0.90 and 0.70 in the training and test subsets, respectively. Its sensitivity and specificity were 0.91 and 0.76 in the training subset and 0.60 and 0.79 in the test subset, respectively. The calibration curve illustrated that the combined model possessed a good agreement between the predicted and the actual probabilities.
The radiomics-based model combining imaging features from the arterial and hepatobiliary phases of Gd-EOB-DTPA-enhanced MRI and clinical risk factors provides an effective and reliable tool for the preoperative prediction of MVI in patients with HCC ≤ 5 cm.
本研究旨在建立并验证一种基于放射组学的模型，该模型利用术前钆塞酸二钠增强磁共振成像（Gd-EOB-DTPA-enhanced MRI）预测直径≤5 cm的肝细胞癌患者的微血管侵犯（MVI）情况。
回顾性收集了2017年5月至2020年11月期间在某单一医疗中心就诊的178例孤立性肝细胞癌（HCC）（≤5 cm）患者的临床病理和MRI数据。患者按7:3的比例随机分为训练集和测试集。在动脉期（AP）和肝胆期（HBP）图像上，从感兴趣的肿瘤分割体积（向外扩展1 cm）中提取影像特征。基于显著的临床危险因素和/或选定的影像特征建立了不同模型，并对模型的预测性能进行评估。
构建了三个用于预测MVI的放射组学模型，即AP模型、HBP模型和AP+HBP模型。其中，AP+HBP模型的表现优于其他两个模型。当它与由肿瘤大小和甲胎蛋白（AFP）组成的临床模型相结合时，联合模型（AP+HBP+临床模型）在训练集和测试集中的曲线下面积分别为0.90和0.70。其在训练集中的敏感性和特异性分别为0.91和0.76，在测试集中分别为0.60和0.79。校准曲线表明联合模型在预测概率和实际概率之间具有良好的一致性。
基于放射组学的模型结合了Gd-EOB-DTPA增强MRI动脉期和肝胆期的影像特征以及临床危险因素，为术前预测直径≤5 cm的HCC患者的MVI提供了一种有效且可靠的工具。
