Department of Ultrasound, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Gastroenterological Center, Yokohama City University Medical Center, Yokohama, Kanagawa, Japan.
Biosci Trends. 2024 Jul 9;18(3):277-288. doi: 10.5582/bst.2024.01112. Epub 2024 Jun 12.
To establish clinical prediction models of vessels encapsulating tumor clusters (VETC) pattern using preoperative contrast-enhanced ultrasound (CEUS) and gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid magnetic resonance imaging (EOB-MRI) in patients with hepatocellular carcinoma (HCC). A total of 111 resected HCC lesions from 101 patients were included. Preoperative imaging features of CEUS and EOB-MRI, postoperative recurrence, and survival information were collected from medical records. The best subset regression and multivariable Cox regression were used to select variables to establish the prediction model. The VETC-positive group had a statistically lower survival rate than the VETC-negative group. The selected variables were peritumoral enhancement in the arterial phase (AP), hepatobiliary phase (HBP) on EOB-MRI, intratumoral branching enhancement in the AP of CEUS, intratumoral hypoenhancement in the portal phase of CEUS, incomplete capsule, and tumor size. A nomogram was developed. High and low nomogram scores with a cutoff value of 168 points showed different recurrence-free survival rates and overall survival rates. The area under the curve (AUC) and accuracy were 0.804 and 0.820, respectively, indicating good discrimination. Decision curve analysis showed a good clinical net benefit (threshold probability > 5%), while the Hosmer-Lemeshow test yielded excellent calibration (P = 0.6759). The AUC of the nomogram model combining EOB-MRI and CEUS was higher than that of the models with EOB-MRI factors only (0.767) and CEUS factors only (0.7). The nomogram verified by bootstrapping showed AUC and calibration curves similar to those of the nomogram model. The Prediction model based on CEUS and EOB-MRI is effective for preoperative noninvasive diagnosis of VETC.
建立基于术前超声造影(CEUS)和钆塞酸二钠增强磁共振成像(EOB-MRI)的肝细胞癌(HCC)患者血管包裹肿瘤簇(VETC)模式的临床预测模型。共纳入 101 例患者的 111 个 HCC 切除病灶。从病历中收集了 CEUS 和 EOB-MRI 的术前成像特征、术后复发和生存信息。采用最佳子集回归和多变量 Cox 回归选择变量建立预测模型。VETC 阳性组的生存率明显低于 VETC 阴性组。选择的变量包括动脉期(AP)肿瘤周围增强、EOB-MRI 肝胆期(HBP)、CEUS AP 肿瘤内分支增强、CEUS 门静脉期肿瘤内低增强、不完整包膜和肿瘤大小。建立了一个列线图。高和低列线图评分截断值为 168 分,显示出不同的无复发生存率和总生存率。曲线下面积(AUC)和准确性分别为 0.804 和 0.820,表明具有良好的区分能力。决策曲线分析显示具有良好的临床净获益(阈值概率>5%),而 Hosmer-Lemeshow 检验显示具有良好的校准度(P=0.6759)。联合 EOB-MRI 和 CEUS 的列线图模型的 AUC 高于仅 EOB-MRI 因素(0.767)和仅 CEUS 因素(0.7)的模型。通过自举法验证的列线图显示 AUC 和校准曲线与列线图模型相似。基于 CEUS 和 EOB-MRI 的预测模型可有效用于术前无创诊断 VETC。