A Validation Study of Liver Volumetry Estimation by a Semiautomated Software in Patients Undergoing Hepatic Resections

Abstract

Purpose The purpose of this study was to validate the use of a semiautomated software for liver volumetry preoperatively by comparing it with the volume of resected specimen in patients undergoing hepatic resections.

Materials and Methods This is a single-center retrospective study of patients who underwent estimation of future liver remnant (FLR) using Myrian XP-Liver which is a semiautomated software for hepatectomy. The estimated resection volume, which is the sum of volume of normal liver to be resected and tumor volume, was compared with actual specimen weight to calculate the accuracy of the software. The statistical analysis was performed with SPSS software version 24.

Results Data on FLR estimation using the semiautomated software was available for 200 out of 388 patients who underwent formal hepatic resections. The median resected volume of surgical specimen was 650 mL (interquartile range [IQR] 364–950), while the median estimated volume using the Myrian software was 617 mL (IQR 362–979). There was significant correlation between estimated resection volume calculated using the semiautomated method and actual specimen weight (p-value < 0.0001) with the Spearman’s correlation value of 0.956.

Conclusion The estimated volume of liver to be resected as calculated by the semiautomated software was accurate and correlated significantly with the volume of resected specimen, and hence, the estimation of FLR volume may likely correlate with the true postoperative residual liver volume. In addition, the software-based liver segmentation, FLR estimation, and color-coded three-dimensional images provide a clear road map to the surgeon to facilitate safe resection.

Keywords

liver volumetry - FLR (future liver remnant) - Myrian

Publication History

Article published online:
24 December 2020

• References

• 1 Poon RT, Fan ST, Lo CM. et al. Improving perioperative outcome expands the role of hepatectomy in management of benign and malignant hepatobiliary diseases: analysis of 1222 consecutive patients from a prospective database. Ann Surg 2004; 240 (04) 698-708, discussion 708–710
• 2 Kamiyama T, Nakanishi K, Yokoo H. et al. Perioperative management of hepatic resection toward zero mortality and morbidity: analysis of 793 consecutive cases in a single institution. J Am Coll Surg 2010; 211 (04) 443-449
• 3 Karlo C, Reiner CS, Stolzmann P. et al. CT- and MRI-based volumetry of resected liver specimen: comparison to intraoperative volume and weight measurements and calculation of conversion factors. Eur J Radiol 2010; 75 (01) e107-e111
• 4 Michalopoulos GK, DeFrances MC. Liver regeneration. Science 1997; 276 (5309) 60-66
• 5 Heymsfield SB, Fulenwider T, Nordlinger B, Barlow R, Sones P, Kutner M. Accurate measurement of liver, kidney, and spleen volume and mass by computerized axial tomography. Ann Intern Med 1979; 90 (02) 185-187
• 6 Schiano TD, Bodian C, Schwartz ME, Glajchen N, Min AD. Accuracy and significance of computed tomographic scan assessment of hepatic volume in patients undergoing liver transplantation. Transplantation 2000; 69 (04) 545-550
• 7 Lemke AJ, Brinkmann MJ, Schott T. et al. Living donor right liver lobes: preoperative CT volumetric measurement for calculation of intraoperative weight and volume. Radiology 2006; 240 (03) 736-742
• 8 Yamanaka J, Saito S, Fujimoto J. Impact of preoperative planning using virtual segmental volumetry on liver resection for hepatocellular carcinoma. World J Surg 2007; 31 (06) 1249-1255
• 9 Nakayama Y, Li Q, Katsuragawa S. et al. Automated hepatic volumetry for living related liver transplantation at multisection CT. Radiology 2006; 240 (03) 743-748
• 10 Bae KT, Giger ML, Chen CT, Kahn Jr CE. Automatic segmentation of liver structure in CT images. Med Phys 1993; 20 (01) 71-78
• 11 Niehues SM, Unger JK, Malinowski M, Neymeyer J, Hamm B, Stockmann M. Liver volume measurement: reason of the difference between in vivo CT-volumetry and intraoperative ex vivo determination and how to cope it. Eur J Med Res 2010; 15 (08) 345-350
• 12 Lieffers JR, Mourtzakis M, Hall KD, McCargar LJ, Prado CM, Baracos VE. A viscerally driven cachexia syndrome in patients with advanced colorectal cancer: contributions of organ and tumor mass to whole-body energy demands. Am J Clin Nutr 2009; 89 (04) 1173-1179
• 13 Suzuki K, Epstein ML, Kohlbrenner R. et al. Quantitative radiology: automated CT liver volumetry compared with interactive volumetry and manual volumetry. AJR Am J Roentgenol 2011; 197 (04) W706-12
• 14 Radtke A, Sotiropoulos GC, Nadalin S. et al. Preoperative volume prediction in adult live donor liver transplantation: 3-D CT volumetry approach to prevent miscalculations. Eur J Med Res 2008; 13 (07) 319-326
• 15 Udupa JK, Leblanc VR, Zhuge Y. et al. A framework for evaluating image segmentation algorithms. Comput Med Imaging Graph 2006; 30 (02) 75-87
• 16 Chartrand G, Cresson T, Chav R, Gotra A, Tang A, De Guise J. Semi-automated liver CT segmentation using Laplacian meshes. 2014 I.E. International Symposium on Biomedical Imaging, Beijing; 2013
• 17 Bégin A, Martel G, Lapointe R. et al. Accuracy of preoperative automatic measurement of the liver volume by CT-scan combined to a 3D virtual surgical planning software (3DVSP). Surg Endosc 2014; 28 (12) 3408-3412
• 18 Simpson AL, Geller DA, Hemming AW. et al. Liver planning software accurately predicts postoperative liver volume and measures early regeneration. J Am Coll Surg 2014; 219 (02) 199-207