Gd-EOB-DTPA based magnetic resonance imaging for predicting liver response to portal vein embolization

Szklaruk, Janio; Luersen, Gustavo Felipe; Ma, Jingfei; Wei, Wei; Underwood, Michelle

doi:10.4329/wjr.v9.i4.199

Cited by 1 publication

(1 citation statement)

References 21 publications

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“…MRI makes it possible to evaluate different tissue properties, including fat content, restriction of water diffusion, or increased T 2 -relaxation times, all of which support lesion detection. Furthermore, in combination with a liver-specific contrast agent such as gadoxetic acid (Gd-EOB-DTPA), monitoring the perfusion dynamics and the uptake of the agent allows functional assessment of the liver (Imbriaco et al, 2017 ; Szklaruk et al, 2017 ; Zhou et al, 2017 ), thereby improving the detection of liver carcinoma and classification of microvascular invasion in hepatocellular carcinoma. Thus, MRI is a versatile modality for creating detailed, anatomically accurate models for computationally aided liver surgery (Oshiro and Ohkohchi, 2017 ; Rutkowski et al, 2017 ).…”

Section: Computational-aided Surgery For Liver Resectionmentioning

confidence: 99%

Computational Modeling in Liver Surgery

et al. 2017

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The need for extended liver resection is increasing due to the growing incidence of liver tumors in aging societies. Individualized surgical planning is the key for identifying the optimal resection strategy and to minimize the risk of postoperative liver failure and tumor recurrence. Current computational tools provide virtual planning of liver resection by taking into account the spatial relationship between the tumor and the hepatic vascular trees, as well as the size of the future liver remnant. However, size and function of the liver are not necessarily equivalent. Hence, determining the future liver volume might misestimate the future liver function, especially in cases of hepatic comorbidities such as hepatic steatosis. A systems medicine approach could be applied, including biological, medical, and surgical aspects, by integrating all available anatomical and functional information of the individual patient. Such an approach holds promise for better prediction of postoperative liver function and hence improved risk assessment. This review provides an overview of mathematical models related to the liver and its function and explores their potential relevance for computational liver surgery. We first summarize key facts of hepatic anatomy, physiology, and pathology relevant for hepatic surgery, followed by a description of the computational tools currently used in liver surgical planning. Then we present selected state-of-the-art computational liver models potentially useful to support liver surgery. Finally, we discuss the main challenges that will need to be addressed when developing advanced computational planning tools in the context of liver surgery.

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