Semi-automated computed tomography Volumetry can predict hemihepatectomy specimens' volumes in patients with hepatic malignancy

Abstract

Background: One of the major causes of perioperative mortality of patients undergoing major hepatic resections is post-hepatectomy liver failure (PHLF). For preoperative appraisal of the risk of PHLF it is important to accurately predict resectate volume and future liver remnant volume (FLRV). The objective of our study is to prospectively evaluate the accuracy of hemihepatectomy resectate volumes that are determined by computed tomography volumetry (CTV) when compared with intraoperatively measured volumes and weights as gold standard in patients undergoing hemihepatectomy.

Methods: Twenty four patients (13 women, 11 men) scheduled for hemihepatectomy due to histologically proven primary or secondary hepatic malignancies were included in our study. CTV was performed using a semi-automated module (S, hereinafter) (syngo.CT Liver Analysis VA30, Siemens Healthcare, Germany). Conversion factors between CT volumes on the one side and intraoperative volumes and weights on the other side were calculated using the method of least squares. Absolute and relative disagreements between CT volumes and intraoperative volumes were determined.

Results: A conversion factor of c = 0.906 most precisely predicted intraoperative volumes of exsanguinated hemihepatectomy specimens from CT volumes in all patients with mean absolute and relative disagreements between CT volumes and intraoperative volumes of 57 ml and 6.3%. The use of operation-specific conversion factors yielded even better results.

Conclusions: CTV performed with S accurately predicts intraoperative volumes of hemihepatectomy specimens when applying conversion factors which compensate for exsanguination. This allows to precisely estimate the FLRV and thus minimize the risk of PHLF in patients undergoing major hepatic resections.

Keywords: Computed tomography volumetry; Hemihepatectomy; Hepatic malignancy.

Fig. 1

Segmentation process in a patient planned for left hemihepatectomy with resection of the MHV due to intrahepatic CC a) Plain axial CT image at the level of the maximum tumor extension shows the hypoattenuated tumor in the atrophic left liver lobe (black arrows) with upstream cholestasis (white arrowheads). b) and c) Detection of the liver outline. d) – f) Detection of the intrahepatic PV (in pink) and HV (in blue). Note occlusion of the left PV and of branches of the MHV and left HV due to tumor invasion. g) – h) Definition of the transection plane (in red)

Fig. 2

Segmentation process in a patient planned for right hemihepatectomy without resection of the MHV due to metastases of GCT a) Plain axial CT image shows a hypoattenuated metastasis in liver segment 8 (black arrows). b) and c) Detection of the liver outline. Further metastases are depicted at the level of the proximal MHV in c) (black arrows). d) – f) Detection of the intrahepatic PV (in pink) and HV (in blue). g) – h) Definition of the transection plane (in red)

Fig. 3

Segmentation process in a patient planned for extended right hemihepatectomy due to metastases of TC a) Plain axial CT image shows small peripherally hyperattenuated metastases (black arrows). b) and c) Detection of the liver outline. Another large metastasis is depicted at the level of the proximal MHV in c) (blue arrows). d) – f) Detection of the intrahepatic PV (in blue) and HV (in pink). g) – h) Definition of the transection plane (in red)

Fig. 4

Results of the regression analyses of intraoperative volumes and CT volumes The linear regression analysis with the method of least-squares was performed for a) all specimens, and for b) – d) each specific type of operation performed