Literature DB >> 30927948

Differentiation of liver abscess from liver metastasis using dual-energy spectral CT quantitative parameters.

Nan Wang1, Ye Ju1, Jingjun Wu1, Ailian Liu2, Anliang Chen1, Jinghong Liu1, Yijun Liu1, Jianying Li3.   

Abstract

OBJECTIVE: To explore the value of single source dual-energy spectral CT quantitative parameters in differential diagnosis of liver abscess and liver metastatic tumor.
METHODS: Fifty-one patients with 73 liver lesions (28 liver abscesses and 45 liver metastases) underwent plain and contrast-enhanced spectral CT scans. The fat and blood concentrations and CT values of 40-140 keV monochromatic images were measured to calculate effective atomic number (Eff-Z) and a slope (K): [CT(40 keV)-CT(140 keV)/100] for the cystic component on the plain scan images. The iodine concentration of the lesion wall on the enhanced three-phase images were measured and normalized to that of aorta for normalized iodine concentration (NIC). All of the single quantitative parameters were compared between liver abscesses and liver metastases by the independent samples t-test or Mann-Whitney U test. Statistical difference parameters between liver abscesses and liver metastases were analyzed by receiver-operating characteristic (ROC) curves. The diagnostic capability was determined by calculating the area under the curve (AUC). Using binary logistics regression analysis combining the best diagnostic performance parameter in the plain and enhanced phase images. The predictive model of liver abscess was established, and the receiver operating characteristic (ROC) analysis was performed.
RESULTS: There were significant differences in CT value, the slope (K), Eff-Z, blood and fat concentrations in the plain phase and NIC in the contrast-enhanced venous and delay phases between liver abscesses and liver metastases (all P < 0.05). The CT value at 40 keV in the plain phase provided 0.761 in area under the curve (AUC) in ROC with sensitivity of 71.4% and, specificity of 75.6% in differentiating liver abscess and tumor. Combining with NIC in delay phase, the respectively values improved to 0.963, and 89.3% and 93.3%.
CONCLUSION: The quantitative parameters in single source dual-energy CT provide high diagnostic accuracy in differentiating liver abscesses from liver metastases.
Copyright © 2019. Published by Elsevier B.V.

Entities:  

Keywords:  Differential diagnosis; Liver abscess; Liver metastasis

Mesh:

Substances:

Year:  2019        PMID: 30927948     DOI: 10.1016/j.ejrad.2019.02.024

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  5 in total

1.  Value of CT-Based Radiomics in Predicating the Efficacy of Anti-HER2 Therapy for Patients With Liver Metastases From Breast Cancer.

Authors:  Miao He; Yu Hu; Dongdong Wang; Meili Sun; Huijie Li; Peng Yan; Yingxu Meng; Ran Zhang; Li Li; Dexin Yu; Xiuwen Wang
Journal:  Front Oncol       Date:  2022-04-07       Impact factor: 5.738

2.  Radiomics Study for Differentiating Focal Hepatic Lesions Based on Unenhanced CT Images.

Authors:  Xitong Zhao; Pan Liang; Liuliang Yong; Yan Jia; Jianbo Gao
Journal:  Front Oncol       Date:  2022-04-27       Impact factor: 5.738

3.  Establishment of a novel system for the preoperative prediction of adherent perinephric fat (APF) occurrence based on a multi-mode and multi-parameter analysis of dual-energy CT.

Authors:  Guan Li; Jie Dong; Wei Huang; Zhengyu Zhang; Di Wang; Mingyu Zou; Qinmei Xu; Guangming Lu; Zhiqiang Cao
Journal:  Transl Androl Urol       Date:  2019-10

4.  3D variable flip angle T1 mapping for differentiating benign and malignant liver lesions at 3T: comparison with diffusion weighted imaging.

Authors:  Fei Wang; Qing Yang; Yupei Zhang; Jun Liu; Mengxiao Liu; Juan Zhu
Journal:  BMC Med Imaging       Date:  2022-08-18       Impact factor: 2.795

5.  Liver metastasis mimicking an abscess.

Authors:  Kaoutar Imrani; Tlaite Oubaddi; Hounayda Jerguigue; Rachida Latib; Youssef Omor
Journal:  BJR Case Rep       Date:  2021-04-12
  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.