scholarly journals Comparison of gadoxetic acid-enhanced dynamic imaging and diffusion-weighted imaging for the preoperative evaluation of colorectal liver metastases

2011 ◽  
Vol 34 (2) ◽  
pp. 345-353 ◽  
Author(s):  
Woo-Suk Chung ◽  
Myeong-Jin Kim ◽  
Yong Eun Chung ◽  
Yeo-Eun Kim ◽  
Mi-Suk Park ◽  
...  
2020 ◽  
Vol 125 ◽  
pp. 108895
Author(s):  
Mehmet Ali Gültekin ◽  
Hacı Mehmet Türk ◽  
Mehmet Beşiroğlu ◽  
Hüseyin Toprak ◽  
Ismail Yurtsever ◽  
...  

Cancers ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 151 ◽  
Author(s):  
Matteo Renzulli ◽  
Alfredo Clemente ◽  
Anna Maria Ierardi ◽  
Irene Pettinari ◽  
Francesco Tovoli ◽  
...  

Computed tomography (CT), magnetic resonance imaging (MRI), and 18-fluorideoxyglucose positron emission tomography (18FDG-PET) are historically the most accurate imaging techniques for diagnosing liver metastases. Recently, the combination of diffusion-weighted imaging and hepatospecific contrast media, such as gadoxetic acid in MRI, have been demonstrated to have the highest diagnostic accuracy, sensitivity, and specificity for detecting liver metastases. Various recent meta-analyses have confirmed the diagnostic superiority of this combination (diffusion-weighted imaging and gadoxetic acid-enhanced MRI), especially in terms of per lesion sensitivity, as compared with CT and 18FDG-PET, even for smaller lesions (≤1 cm). However, none of the oncological guidelines have suggested the use of MRI as a first-line technique for liver metastasis detection during the staging process of oncological patients. This review analyzes the history of the principal imaging techniques for the diagnosis of liver metastases, in particular of colorectal liver metastases, focusing on the most accurate method (diffusion-weighted imaging combined with gadoxetic acid-enhanced MRI), possible reasons for the lack of its diffusion in the guidelines, and possible future scenarios.


2019 ◽  
Vol 30 (1) ◽  
pp. 186-194 ◽  
Author(s):  
Jordi Rimola ◽  
Alejandro Forner ◽  
Víctor Sapena ◽  
Neus Llarch ◽  
Anna Darnell ◽  
...  

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