Construction of a nomogram combining CEUS and MRI imaging for preoperative diagnosis of microvascular invasion in hepatocellular carcinoma

IF 1.8 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING European Journal of Radiology Open Pub Date : 2024-07-08 DOI:10.1016/j.ejro.2024.100587
Feiqian Wang , Kazushi Numata , Akihiro Funaoka , Takafumi Kumamoto , Kazuhisa Takeda , Makoto Chuma , Akito Nozaki , Litao Ruan , Shin Maeda
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Abstract

Purpose

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).

Methods

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.

Results

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 P > 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.

Conclusions

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.

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结合 CEUS 和 MRI 成像构建用于肝细胞癌微血管侵犯术前诊断的提名图
目的利用类 Sonazoid 对比增强超声波(S-CEUS)和钆-乙氧苄基-二乙烯三胺五乙酸磁共振成像(EOB-MRI),探索肝细胞癌(HCC)微血管侵犯(MVI)的无创术前诊断策略。S-CEUS 和 EOB-MRI 检查均在肝切除术后一个月内进行。研究指标包括:肿瘤大小;S-CEUS三期血管情况;EOB-MRI的边缘、信号强度和瘤周楔形;S-CEUS或EOB-MRI的肿瘤均匀性、肿瘤囊的存在和完整性;S-CEUS的分支强化情况;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以及多变量逻辑回归分析来优化模型的特征选择。结果 在我们纳入的 16 个变量中,EOB-MRI HBP 中的楔形和边缘、EOB-MRI/S-CEUS AP 或 HBP/PVP 图像中的囊完整性、S-CEUS AP 中的分支增强被确定为 MVI 的独立风险因素,并被纳入提名图的构建中。该提名图的诊断效率极高,全数据训练集的曲线下面积为 0.8434,重复 500 次的引导验证集的曲线下面积为 0.7925。在评估提名图时,训练集的 Hosmer-Lemeshow 检验显示模型拟合良好,P > 0.05。结论 本研究建立的 MVI Nomogram 可为 MVI 的术前诊断提供优化策略,从而改善 MVI 相关 HCC 的治疗和预后。
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来源期刊
European Journal of Radiology Open
European Journal of Radiology Open Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
4.10
自引率
5.00%
发文量
55
审稿时长
51 days
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