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通过光谱CT得出的肝脏和脾脏细胞外体积分数预测慢性肝病严重程度

Predicting Chronic Liver Disease Severity by Liver and Splenic Extracellular Volume Fraction Derived from spectral-CT.

作者信息

Yang Yiming, Chen Zhiyuan, Zhou Dongjing, Guo Mengya, Qi Yan, Yu Mengqi, Liu Yupin

机构信息

Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.

Department of Imaging Research Center, GE HealthCare, Guangzhou, China.

出版信息

Curr Med Imaging. 2025;21:e15734056396041. doi: 10.2174/0115734056396041250728133515.

Abstract

INTRODUCTION

To evaluate the effectiveness of spectral-CT in assessing the severity of liver diseases in patients with chronic liver disease (CLD).

METHODS

A total of 148 CLD patients who underwent spectral-CT were retrospectively recruited, including 40 non-advanced CLD (non-ACLD), 74 compensated ACLD (cACLD), and 34 decompensated ACLD (dACLD). Iodine concentrations in the liver and spleen were assessed on iodine (water) images during the equilibrium phase, which allowed for the calculation of liver and splenic extracellular volume fractions (ECV). We determined the total liver volume, liver segmental volume ratio, and splenic volume from portal phase images. Moreover, established non-invasive tests were also collected. Areas under receiver operating characteristic curve (AUCs) were employed to evaluate the diagnostic performance of CT parameters and non-invasive tests in predicting CLD severity. Additionally, we analyzed the correlations between CT parameters and non-invasive tests.

RESULTS

The spleen volume demonstrated the highest AUC (0.815, P<0.001) for distinguishing between non-ACLD and cALCD. Child-Pugh score exhibited the highest AUC (0.948, P<0.001) for distinguishing cALCD and dACLD. Splenic ECV exhibited the highest AUC (0.853, P<0.001) for distinguishing non-ALCD and ACLD. In contrast, the liver ECV showed strong correlations with the Fibrosis-4 Index (r=0.653, p<0.001) and the Aminotransferase-to-Platelet Ratio Index (r=0.607, p<0.001), while spleen ECV correlated more strongly with the Child-Pugh score (r=0.719, p<0.001) and the Albumin-Bilirubin Index (r=0.742, p<0.001).

DISCUSSION

Liver and splenic ECV can effectively reflect the dynamic progression of CLD and correlate well with non-invasive tests in these patients.

CONCLUSION

Spectral-CT liver and splenic ECV could serve as non-invasive imaging biomarkers for severity stratification.

摘要

引言

评估光谱CT在评估慢性肝病(CLD)患者肝脏疾病严重程度方面的有效性。

方法

回顾性纳入148例行光谱CT检查的CLD患者,包括40例非晚期CLD(非ACLD)、74例代偿期ACLD(cACLD)和34例失代偿期ACLD(dACLD)。在平衡期的碘(水)图像上评估肝脏和脾脏的碘浓度,据此计算肝脏和脾脏的细胞外体积分数(ECV)。我们从门静脉期图像确定肝脏总体积、肝段体积比和脾脏体积。此外,还收集了已有的非侵入性检查。采用受试者操作特征曲线下面积(AUC)评估CT参数和非侵入性检查在预测CLD严重程度方面的诊断性能。此外,我们分析了CT参数与非侵入性检查之间的相关性。

结果

脾脏体积在区分非ACLD和cALCD方面的AUC最高(0.815,P<0.001)。Child-Pugh评分在区分cALCD和dACLD方面的AUC最高(0.948,P<0.001)。脾脏ECV在区分非ALCD和ACLD方面的AUC最高(0.853,P<0.001)。相比之下,肝脏ECV与纤维化-4指数(r=0.653,p<0.001)和天冬氨酸转氨酶与血小板比值指数(r=0.607,p<0.001)密切相关,而脾脏ECV与Child-Pugh评分(r=0.719,p<0.001)和白蛋白-胆红素指数(r=0.742,p<0.001)的相关性更强。

讨论

肝脏和脾脏ECV可以有效反映CLD的动态进展,并与这些患者的非侵入性检查密切相关。

结论

光谱CT肝脏和脾脏ECV可作为用于严重程度分层的非侵入性影像学生物标志物。

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