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双能计算机断层扫描定量参数在鉴别肿瘤性与单纯性门静脉血栓形成中的价值。

Value of dual-energy computed tomography quantitative parameters in differentiating neoplastic from bland portal vein thrombosis.

作者信息

Peng Ying-Jie, Dai Ting, Li Dan, Liu Peng, He Ya-Qiong

机构信息

Department of Radiology, The First Affiliated Hospital of Hunan Normal University Hunan Provincial People's Hospital, Changsha, China.

出版信息

Quant Imaging Med Surg. 2025 Jun 6;15(6):5101-5113. doi: 10.21037/qims-2024-2516. Epub 2025 May 15.

Abstract

BACKGROUND

Portal vein thrombosis (PVT) is a common clinical pathological state involving distinct pathophysiological processes. Accurate discrimination of PVT nature is of utmost importance for guiding treatment strategies, but histopathology has limitations and imaging lacks quantitative indices. This study aimed to evaluate the feasibility and diagnostic value of quantitative parameters from dual-energy computed tomography (DECT), an advanced technique that allows for such quantitative evaluation, in distinguishing neoplastic from bland PVT.

METHODS

Computed tomography (CT) images of 173 patients with PVT (bland group, n=74; neoplastic group, n=99) were retrospectively analyzed. Portal venous phase iodine-based decomposition images were reconstructed to contrast iodine indices among groups, including thrombus iodine concentration (IC), normalized iodine concentration in the aorta (NIC-A), normalized iodine concentration in the portal vein (NIC-V), electron density (Rho), effective atomic number (Z), dual-energy index (DEI), and spectral slope (K). Diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Univariate and multivariate analyses selected DECT parameters and created a nomogram for prediction.

RESULTS

(I) The values of IC, NIC-A, NIC-V, Rho, Z, DEI, and K were significantly higher in the neoplastic group compared to the bland group (P<0.001). (II) The AUC using IC, NIC-A, and NIC-V to differentiate between neoplastic and bland PVT were 0.963, 0.970, and 0.969, respectively; using Rho, Z, DEI, and K, they were 0.732, 0.952, 0.949, and 0.933, respectively. All the quantitative parameters achieved high sensitivity and high specificity in distinguishing neoplastic from bland PVT. (III) A nomogram was developed to predict neoplastic PVT probability; its AUC, sensitivity, and specificity reached remarkable levels, with values of 0.994, 98.59%, and 97.96% in the training cohort and 0.940, 100%, and 92.00% in the test cohort, respectively.

CONCLUSIONS

The DECT quantitative parameters demonstrate significant potential as non-invasive markers for distinguishing between neoplastic and bland PVT.

摘要

背景

门静脉血栓形成(PVT)是一种涉及不同病理生理过程的常见临床病理状态。准确鉴别PVT的性质对于指导治疗策略至关重要,但组织病理学存在局限性,而影像学缺乏定量指标。本研究旨在评估双能计算机断层扫描(DECT)的定量参数在鉴别肿瘤性PVT与单纯性PVT方面的可行性和诊断价值,DECT是一种能够进行此类定量评估的先进技术。

方法

回顾性分析173例PVT患者(单纯性组,n = 74;肿瘤性组,n = 99)的计算机断层扫描(CT)图像。重建门静脉期碘基分解图像以对比各组间的碘指标,包括血栓碘浓度(IC)、主动脉归一化碘浓度(NIC-A)、门静脉归一化碘浓度(NIC-V)、电子密度(Rho)、有效原子序数(Z)、双能指数(DEI)和光谱斜率(K)。使用受试者操作特征曲线(AUC)下面积、敏感性、特异性和准确性评估诊断性能。单因素和多因素分析选择DECT参数并创建预测列线图。

结果

(I)肿瘤性组的IC、NIC-A、NIC-V、Rho、Z、DEI和K值显著高于单纯性组(P < 0.001)。(II)使用IC、NIC-A和NIC-V鉴别肿瘤性PVT与单纯性PVT的AUC分别为0.963、0.970和0.969;使用Rho、Z、DEI和K时,分别为0.732、0.952、0.949和0.933。所有定量参数在鉴别肿瘤性PVT与单纯性PVT方面均具有高敏感性和高特异性。(III)开发了一个预测肿瘤性PVT概率的列线图;其AUC、敏感性和特异性达到显著水平,在训练队列中分别为0.994、98.59%和97.96%,在测试队列中分别为0.940、100%和92.00%。

结论

DECT定量参数作为鉴别肿瘤性PVT与单纯性PVT的非侵入性标志物具有显著潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4352/12209630/249a72adb85a/qims-15-06-5101-f1.jpg

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