Predictive Value of Combined Computed Tomography Volumetry and Magnetic Resonance Elastography for Major Complications After Liver Resection

Overview

Journal Abdom Radiol (NY)

Publisher Springer

Specialties Gastroenterology
Radiology

Date 2021 Mar 8

PMID 33683428

Citations 4

Authors

Kazu Shibutani

Masahiro Okada

Jitsuro Tsukada

Kenji Ibukuro

Hayato Abe

Naoki Matsumoto

Yutaka Midorikawa

Mitsuhiko Moriyama

Tadatoshi Takayama

Affiliations

Soon will be listed here.

Abstract

Purpose: To retrospectively compare the predictive value of computed tomography volumetry (CTV), magnetic resonance elastography (MRE) of the liver, and their combination for major complications after liver resection.

Methods: We enrolled 108 consecutive patients who underwent anatomical liver resection for liver tumors and preoperative contrast-enhanced CT and MRE. The future liver remnant (FLR) ratio was calculated by CTV, while the liver stiffness measurement (LSM) was obtained by MRE. FLR ratio alone, LSM alone, and combined FLR ratio and LSM were evaluated to predict major complications (Clavien-Dindo grade ≥ IIIa). Univariate and multivariate analyses of hepatic biochemical parameters and imaging data were performed to identify predictors of major complications. Receiver operating characteristic analyses of FLR ratio, LSM, and their combination were performed, and the sensitivity and specificity were calculated.

Results: Twenty-two (20.4%) of the 108 patients experienced major complications. According to multiple regression analysis, the FLR ratio (odds ratio [OR] 0.96, 95% confidence interval [CI] 0.91-0.99, p = 0.040) and LSM (OR 1.72, 95% CI 1.01-2.94, p = 0.047) were independent predictors of major complications. The combined FLR ratio and LSM were predictive of major complications, with an area under the curve (AUC) of 0.818, sensitivity of 68.2%, and specificity of 84.9%. The AUC and specificity for combined FLR ratio and LSM were larger than those for FLR ratio (AUC: 0.711, specificity: 80.2%) and LSM (AUC: 0.793, specificity: 80.2%).

Conclusion: Combined CTV and MRE analysis can improve the AUC and specificity for predicting major complications after anatomical liver resection.

Citing Articles

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Shibutani K, Okada M, Tsukada J, Hyodo T, Ibukuro K, Abe H BJR Open. 2021; 3(1):20210019.

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References

Benzoni E, Molaro R, Cedolini C, Favero A, Cojutti A, Lorenzin D . Liver resection for HCC: analysis of causes and risk factors linked to postoperative complications. Hepatogastroenterology. 2007; 54(73):186-9. View

Gozzetti G, Mazziotti A, Cavallari A, Bellusci R, Bolondi L, Grigioni W . Clinical experience with hepatic resections for hepatocellular carcinoma in patients with cirrhosis. Surg Gynecol Obstet. 1988; 166(6):503-10. View

Garcea G, Maddern G . Liver failure after major hepatic resection. J Hepatobiliary Pancreat Surg. 2008; 16(2):145-55. DOI: 10.1007/s00534-008-0017-y. View

Hammond J, Guha I, Beckingham I, Lobo D . Prediction, prevention and management of postresection liver failure. Br J Surg. 2011; 98(9):1188-200. DOI: 10.1002/bjs.7630. View

Yoshioka R, Saiura A, Koga R, Seki M, Kishi Y, Yamamoto J . Predictive factors for bile leakage after hepatectomy: analysis of 505 consecutive patients. World J Surg. 2011; 35(8):1898-903. DOI: 10.1007/s00268-011-1114-7. View

Senousy B, Draganov P . Evaluation and management of patients with refractory ascites. World J Gastroenterol. 2008; 15(1):67-80. PMC: 2653293. DOI: 10.3748/wjg.15.67. View

Kim D, Choi J, Choi M, Park M, Lee Y, Eun Rha S . Prediction of Posthepatectomy Liver Failure: MRI With Hepatocyte-Specific Contrast Agent Versus Indocyanine Green Clearance Test. AJR Am J Roentgenol. 2018; 211(3):580-587. DOI: 10.2214/AJR.17.19206. View

Ozaki K, Matsui O, Kobayashi S, Sanada J, Koda W, Minami T . Selective atrophy of the middle hepatic venous drainage area in hepatitis C-related cirrhotic liver: morphometric study by using multidetector CT. Radiology. 2010; 257(3):705-14. DOI: 10.1148/radiol.10100468. View

Costa A, Tremblay St-Germain A, Abdolell M, Smoot R, Cleary S, Jhaveri K . Can contrast-enhanced MRI with gadoxetic acid predict liver failure and other complications after major hepatic resection?. Clin Radiol. 2017; 72(7):598-605. DOI: 10.1016/j.crad.2017.02.004. View

10.

Abe H, Midorikawa Y, Mitsuka Y, Aramaki O, Higaki T, Matsumoto N . Predicting postoperative outcomes of liver resection by magnetic resonance elastography. Surgery. 2017; 162(2):248-255. DOI: 10.1016/j.surg.2017.02.014. View

11.

Wang Q, Zhu H, Liu H, Zhang B . Performance of magnetic resonance elastography and diffusion-weighted imaging for the staging of hepatic fibrosis: A meta-analysis. Hepatology. 2012; 56(1):239-47. DOI: 10.1002/hep.25610. View

12.

Sato N, Kenjo A, Kimura T, Okada R, Ishigame T, Kofunato Y . Prediction of major complications after hepatectomy using liver stiffness values determined by magnetic resonance elastography. Br J Surg. 2018; 105(9):1192-1199. DOI: 10.1002/bjs.10831. View

13.

Wiesner R, Edwards E, Freeman R, Harper A, Kim R, Kamath P . Model for end-stage liver disease (MELD) and allocation of donor livers. Gastroenterology. 2003; 124(1):91-6. DOI: 10.1053/gast.2003.50016. View

14.

Reeder S, Pineda A, Wen Z, Shimakawa A, Yu H, Brittain J . Iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL): application with fast spin-echo imaging. Magn Reson Med. 2005; 54(3):636-44. DOI: 10.1002/mrm.20624. View

15.

Reeder S, Robson P, Yu H, Shimakawa A, Hines C, McKenzie C . Quantification of hepatic steatosis with MRI: the effects of accurate fat spectral modeling. J Magn Reson Imaging. 2009; 29(6):1332-9. PMC: 2689318. DOI: 10.1002/jmri.21751. View

16.

Yu H, McKenzie C, Shimakawa A, Vu A, Brau A, Beatty P . Multiecho reconstruction for simultaneous water-fat decomposition and T2* estimation. J Magn Reson Imaging. 2007; 26(4):1153-61. DOI: 10.1002/jmri.21090. View

17.

Kubota K, Makuuchi M, Kusaka K, Kobayashi T, Miki K, Hasegawa K . Measurement of liver volume and hepatic functional reserve as a guide to decision-making in resectional surgery for hepatic tumors. Hepatology. 1997; 26(5):1176-81. DOI: 10.1053/jhep.1997.v26.pm0009362359. View

18.

Venkatesh S, Yin M, Ehman R . Magnetic resonance elastography of liver: technique, analysis, and clinical applications. J Magn Reson Imaging. 2013; 37(3):544-55. PMC: 3579218. DOI: 10.1002/jmri.23731. View

19.

Wang Y, Ganger D, Levitsky J, Sternick L, McCarthy R, Chen Z . Assessment of chronic hepatitis and fibrosis: comparison of MR elastography and diffusion-weighted imaging. AJR Am J Roentgenol. 2011; 196(3):553-61. PMC: 3093963. DOI: 10.2214/AJR.10.4580. View

20.

Kim B, Lee J, Lee Y, Lee K, Suh K, Han J . MR elastography for noninvasive assessment of hepatic fibrosis: experience from a tertiary center in Asia. J Magn Reson Imaging. 2011; 34(5):1110-6. DOI: 10.1002/jmri.22723. View