Can contrast-enhanced MRI with gadoxetic acid predict liver failure and other complications after major hepatic resection?

A. F. Costa, A. Tremblay St-Germain, M. Abdolell, R. L. Smoot, S. Cleary, K. S. Jhaveri

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Aim To determine whether a combination of clinical factors, the future liver remnant (FLR) ratio, and hepatic uptake of gadoxetic acid can be used to predict post-hepatectomy liver failure (PHLF) and other major complications (OMC). Materials and methods Sixty-five consecutive patients who underwent pre-hepatectomy gadoxetic acid-enhanced magnetic resonance imaging (MRI) between October 2010 and December 2013 were included. The relative liver enhancement (RLE) of gadoxetic acid was calculated from regions of interest on MRI, and FLR ratios were obtained from computed tomography (CT). PHLF and OMC were defined by the International Study Group of Liver Surgery criteria and Clavien–Dindo grade of ≥3, respectively. Multivariate logistic regression modelling was performed to identify predictors of PHLF and OMC, including RLE, FLR ratio, age, sex, chemotherapy history, intra-operative blood loss, and intra-operative transfusion. Results Nine patients experienced PHLF and another nine patients experienced OMC. RLE was comparable to the FLR ratio in predicting PHLF (areas under the receiver operating characteristic [AUROC] curves, 0.665 and 0.705), but performed poorly in predicting OMCs (AUROCs, 0.556 and 0.702). Combining all clinical and imaging parameters as predictors yielded the best performing predictive models (AUROCs, 0.875 and 0.742 for PHLF and OMC, respectively). Conclusion A model based on clinical parameters, the FLR ratio, and RLE of gadoxetic acid may improve pre-hepatectomy risk assessment.

Original languageEnglish (US)
Pages (from-to)598-605
Number of pages8
JournalClinical Radiology
Volume72
Issue number7
DOIs
StatePublished - Jul 2017

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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