Wang Feiqian, Numata Kazushi, Funaoka Akihiro, Kumamoto Takafumi, Takeda Kazuhisa, Chuma Makoto, Nozaki Akito, Ruan Litao, Maeda Shin
Ultrasound Department, The First Affiliated Hospital of Xi'an Jiaotong University, No. 277 West Yanta Road, Xi'an, Shaanxi 710061, PR China.
Gastroenterological Center, Yokohama City University Medical Center, 4-57 Urafune-cho, Minami-ku, Yokohama, Kanagawa 232-0024, Japan.
Eur J Radiol Open. 2024 Jul 8;13:100587. doi: 10.1016/j.ejro.2024.100587. eCollection 2024 Dec.
To use Sonazoid contrast-enhanced ultrasound (S-CEUS) and Gadolinium-Ethoxybenzyl-Diethylenetriamine Penta-Acetic Acid magnetic-resonance imaging (EOB-MRI), exploring a non-invasive preoperative diagnostic strategy for microvascular invasion (MVI) of hepatocellular carcinoma (HCC).
111 newly developed HCC cases were retrospectively collected. Both S-CEUS and EOB-MRI examinations were performed within one month of hepatectomy. The following indicators were investigated: size; vascularity in three phases of S-CEUS; margin, signal intensity, and peritumoral wedge shape in EOB-MRI; tumoral homogeneity, presence and integrity of the tumoral capsule in S-CEUS or EOB-MRI; presence of branching enhancement in S-CEUS; baseline clinical and serological data. The least absolute shrinkage and selection operator regression and multivariate logistic regression analysis were applied to optimize feature selection for the model. A nomogram for MVI was developed and verified by bootstrap resampling.
Of the 16 variables we included, wedge and margin in HBP of EOB-MRI, capsule integrity in AP or HBP/PVP images of EOB-MRI/S-CEUS, and branching enhancement in AP of S-CEUS were identified as independent risk factors for MVI and incorporated into construction of the nomogram. The nomogram achieved an excellent diagnostic efficiency with an area under the curve of 0.8434 for full data training set and 0.7925 for bootstrapping validation set for 500 repetitions. In evaluating the nomogram, Hosmer-Lemeshow test for training set exhibited a good model fit with > 0.05. Decision curve analysis of nomogram model yielded excellent clinical net benefit with a wide range (5-80 % and 85-94 %) of risk threshold.
The MVI Nomogram established in this study may provide a strategy for optimizing the preoperative diagnosis of MVI, which in turn may improve the treatment and prognosis of MVI-related HCC.
使用声诺维增强超声(S-CEUS)和钆塞酸二钠磁共振成像(EOB-MRI),探索一种用于肝细胞癌(HCC)微血管侵犯(MVI)的非侵入性术前诊断策略。
回顾性收集111例新诊断的HCC病例。S-CEUS和EOB-MRI检查均在肝切除术前1个月内进行。研究以下指标:大小;S-CEUS三期的血管情况;EOB-MRI的边缘、信号强度和瘤周楔形;S-CEUS或EOB-MRI中的肿瘤均匀性、肿瘤包膜的存在和完整性;S-CEUS中的分支增强;基线临床和血清学数据。应用最小绝对收缩和选择算子回归及多因素逻辑回归分析对模型进行特征选择优化。制定了MVI列线图,并通过自助重采样进行验证。
在我们纳入的16个变量中,EOB-MRI肝胆期的楔形和边缘、EOB-MRI/S-CEUS动脉期或肝胆期/门静脉期图像中的包膜完整性以及S-CEUS动脉期的分支增强被确定为MVI的独立危险因素,并纳入列线图构建。列线图具有良好的诊断效率,完整数据训练集的曲线下面积为0.8434,500次重复自助验证集的曲线下面积为0.7925。在评估列线图时,训练集的Hosmer-Lemeshow检验显示模型拟合良好,P>0.05。列线图模型的决策曲线分析在较宽范围(5-80%和85-94%)的风险阈值下产生了良好的临床净效益。
本研究建立的MVI列线图可为优化MVI的术前诊断提供策略,进而改善与MVI相关的HCC的治疗和预后。