Keywords
liver volumetry - FLR (future liver remnant) - Myrian
Introduction
With improvements in perioperative care, liver resections are increasingly performed
for primary or metastatic liver cancer, with mortality below 5%.[1]
[2] Surgeons aim for a total resection of focal liver lesions, but they need to avoid
an extensive loss of healthy liver parenchyma, since this can lead to postoperative
hepatic failure.[3] To perform partial liver resections safely, the determination of the entire preoperative
liver volume and the remaining postoperative liver volume is important. After resection,
the remnant liver must be able to preserve or recover an adequate synthetic ability
to compensate for lost hepatic parenchyma.[4] Thus, preoperative assessment of liver volumetry has become fundamental in selection
of the patient for liver resection. With the advent of multislice computed tomography
(CT), many studies have demonstrated a close correlation between intraoperative liver
volume or weight measurements and virtually measured liver volumes.[5]
[6]
[7]
[8] Liver volume detection can be done using manual, semiautomated, and automated tracing
method. Manual tracing of liver boundary on CT images though routinely used for liver
volume calculation, is time consuming and prone to intra- and interobserver variations[9] as compared with semiautomated liver segmentation technique.[10]
Materials and Methods
A single-center retrospective study of prospectively maintained data of patients who
underwent anatomical liver resections between August 2012 and December 2018 was performed.
Data on future liver remnant (FLR) estimation using a semiautomated software was available
for 200 out of 388 patients who underwent formal hepatic resections in the given time
period. The study was approved by the institutional review board. Patients who underwent
nonanatomical liver resection and partial hepatectomy for hemangiomas were excluded
from this study. During surgery for extracapsular excisions for hemangioma, the tumor
shrinks on table during surgery and the volume of resected specimen would be lower
than the estimated volume on CT scan. Hence, these patients were excluded from the
study.
All patients who underwent liver resection had a triphasic CT evaluation for surgical
planning. Most of the patients planned for anatomic resections were operated after
estimation of resection and remnant volumes on CT. The estimated FLR was calculated
using the Myrian XP-Liver (Intrasense) software by a single radiologist with more
than 5 years of experience in hepatobiliary reporting, after discussing the plane
of resection with the operating surgeon. The parameters evaluated by the software
to estimate FLR included total liver volume, volume of normal liver to be resected,
and tumor volume. 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 measured
immediately after liver resection in the operating room (OR). Surgical specimen weight
was used as the gold standard and weight-to-volume ratio of 1:1 was used as a standard
assumption to quantify volume of the resected specimen. The volume of resected specimen
was compared with the estimated resection volume calculated using the Myrian software.
The statistical analysis was performed with SPSS software version 24 (IBM Corp.).
Scan Parameters and Volumetry
Triphasic CT was performed with a multidetector CT scanner (Siemens SOMATOM Emotion
16, Siemens Healthcare GmbH). Positive oral contrast-medium was not administered.
After an initial noncontrast scan, triphasic CT was obtained following an intravenous
administration of a iodine contrast medium at a concentration of 300 mg/L (Ultravist
300, Bayer Schering Pharma AG) through an antecubital vein of the arm, using an automatic
syringe injector (Stellant, MedRad) at a flow rate of 3 to 4 mLs. The amount of contrast
media used was according to the patient’s weight, injecting 1.5 mL/kg. In all cases
a triphasic examination was performed using a bolus tracking technique; an arterial
phase scan of the upper abdomen, performed 10 seconds after the reach of the aortic
enhancement threshold (100 Hounsfield unit [HU]) at the level of celiac artery; a
portal phase scan of the upper abdomen, performed 35 seconds after the administration
of contrast media; and a venous phase scan of the upper abdomen, performed 60 seconds
after the administration of contrast media. The following parameters were used: slice
thickness 1.5 mm; increment 1.5 mm; tube voltage 110 kV; collimation 16 × 0.6; pitch
1.3; rotation time 0.6 second.
Myrian software was used for semiautomated volumetry which allows quick automatic
liver segmentation with volumetry using tissue density difference (HUs). The software
allows automatic calculation of the liver vascular territories using density difference
in arterial, portal, and venous phase, thus ensuring precise volumetric measurement
of the liver parenchyma. Three-dimensional (3D) volumetric display with different
transparency and color levels are provided for better anatomical understanding ([Fig. 1]). Axial images of the arterial, portal, and venous phases were used for CT volumetry.
The liver outline, hepatic artery (HA), portal vein (PV), and hepatic veins (HV),
and their branches were drawn automatically by the software. After setting seed points
into the hepatocaval confluence and the main stem of the PV, the system automatically
segments the HV and PV. Those vessels which were not automatically identified by the
software were drawn manually. Finally, the transection plane was defined. The volumes
of the intrahepatic vessels in the liver area marked for resection were included in
the CT volume. After volumetric reconstruction of the normal liver parenchyma, tumor, and hepatic
vasculature, virtual hepatectomy is performed in accordance with the intended surgical
resection plane as decided by the operating hepatobiliary surgeon. The software automatically
calculates FLR using the formula
Fig. 1 A 72-year-old male with suspected malignant lesion in the right lobe of the liver
was planned for right hepatectomy. (A) Cross-sectional computed tomography (CT) image shows color-coded structures—volume
of liver to be resected in light green, future liver remnant in peach color (junction
marks the transection plane), hepatic vein in green, and tumor in purple color. (B) Corresponding three-dimensional (3D) image shows transection plane with volume of
resecting segment and future liver remnant. The total volume to be resected was obtained
by adding tumor volume and cut liver volume and was compared with the volume of the
specimen.
FLR = (Total Liver Volume–Resected Volume) / (Total Liver volume–Tumor Volume)
It is expressed in percentage.
Results
Data on FLR estimation using semiautomated software was available for 200 out of 388
patients who underwent formal hepatic resections in the given time period ([Tables 1]
[2]). The median resected volume of surgical specimen was 650 mL (with interquartile
range [IQR] 364–950), while median estimated volume using the Myrian software was
617 mL (with IQR 362–979). The mean postoperative resected volume was 801.8 g and
the mean estimated volume was 817.57 mL. In the evaluation of operative resection
volume, Spearman’s correlation test showed significant correlation between the estimated
specimen volume recorded using the Myrian software with the actual value recorded
in OR (p-value < 0.0001) with correlation coefficient (r) value of 0.956 ([Fig. 2]). The difference between estimated and actual specimen volume had median value of
38 mL (with IQR 9.25–105).
Table 1
Demographic and surgical details
|
Age distribution
|
15–79 y
|
|
Male:Female
|
141:59
|
|
Mean age for male
|
53.5 y
|
|
Mean age for female
|
49.8 y
|
|
Right hepatectomy
|
93
|
|
Right extended hepatectomy
|
12
|
|
Right posterior sectionectomy
|
7
|
|
Right anterior sectionectomy
|
1
|
|
Left hepatectomy
|
38
|
|
Left lateral hepatectomy
|
31
|
|
Left extended hepatectomy
|
8
|
|
Central hepatectomy
|
10
|
Table 2
Distribution of cases as per histopathology
|
Hepatocellular carcinoma
|
87 (43.5%)
|
|
Metastases
|
70 (35%)
|
|
Cholangiocarcinoma
|
27 (13.5%)
|
|
Hepatic adenoma
|
3
|
|
Carcinoma gallbladder
|
2
|
|
Angiomyolipoma
|
1
|
|
Embryonal sarcoma
|
1
|
|
Hepatoblastoma
|
1
|
|
Benign biliary cystadenoma
|
1
|
|
Pyogenic abscess
|
2
|
|
Hydatid cyst
|
2
|
|
Nontubercular granulomatous infection
|
1
|
|
Tuberculosis
|
1
|
|
Immunoglobulin G4-related pseudotumor
|
1
|
Fig. 2 Scatter plot (A) and corresponding table (B) showing correlation between estimated resection volume using semiautomated method
and actual resection volume. The correlation coefficient (r) is 0.956. Red dots represent the Myrian volume and blue dots the actual volume.
Volume along the X-axis represents specimen volume, while the Y-axis represents estimated volume by the Myrian software.
Discussion
Of various organs, the liver is one of the most difficult to segment virtually due
to its varying shape. Ultrasound, CT, or magnetic resonance imaging (MRI) has been
used to estimate hepatic volume preoperatively prior to liver resection and transplantation.
The use of CT volumetry on cadavers was first performed by Heymsfield et al[5] in 1979 and the accuracy of this method was found to be within 5% of water displacement
volumetry. Since then, various studies have shown preoperatively measured liver volumes
using CT and MRI) correlated with intraoperative volume and weight measurement. Conversion
factors have been suggested to compensate for overestimation of preoperative volume
which could be due to blood perfusion.[3]
[11] Median liver density estimated by intraoperative weight and volume measurement averages
from 1.05 to 1.07 g/mL.[11]
[12]
Various automatic and semiautomatic segmentation techniques have been used to measure
preoperative volumes because they had the advantage of requiring substantially lesser
time compared with manual volumetry.[13] Manual and automated volumetry softwares have been used for estimation of liver
volumes and vascular volumes of liver donors prior to transplantation.[9]
[14] CT is more commonly used, as it is more accessible, provides higher spatial resolution,
and has short acquisition time.[9] Manual liver segmentation used for calculation of preoperative resection volumes
however relies highly on the user performing the segmentation.[15] It is done by the contouring of pixels along the boundary of the liver; or by in-painting
of the liver parenchyma on sequential CT slices with the use of paintbrush tools.
Once the liver has been identified on each slice, postprocessing software is used
to generate liver volume. There is no exclusion of the vessels enclosed by the parenchyma
in the manually painted liver. This exercise is repeated for calculation of lesion
volume. The user then has to separately paint the volume of the liver which would
be resected depending on the intended resection plane. This process has to be repeated
many times if there are multiple possible resection planes. Manual segmentation is
thus time consuming and may take up to 90 minutes per patient.[16] It is also prone to intra- and interobserver variability given its inherent subjectivity.15 As a result, for a high volume center, manual segmentation is not ideally suited
to guide patient selection and treatment planning.
Semiautomated and automated segmentation techniques require minimal initialization
from the user; the software provides most of the optimization. The semiautomated softwares
mainly employ intensity-based techniques, using density difference in arterial, portal,
and venous phase allowing a precise automatic volumetric measurement of the liver
parenchyma. Since the intrahepatic vessels are color coded, liver subsegmentation
can be performed according to vascular supply (i.e., PVs and HAs) or drainage (i.e.,
HVs) rather than the standard Couinaud classification system which does not take into
account the different anatomical liver variants seen in individual patients. This
is especially useful for performing parenchyma sparing anatomical resections in cirrhotic
patients by allowing the surgeon to remove the tumor with adequate margins without
removing excess of normal liver parenchyma.
A study done for evaluation of FLR by automated and semiautomated methods in hepatectomy
patients (n = 36) has shown the volumes using both techniques had a significant difference but
a high degree of correlation with actual intraoperative specimen volume.[17] Another study (n = 66) evaluated correlation between preoperative planned remnant liver volume and
postoperative actual remnant liver volume determined from early postoperative scan
using a semiautomated method and found significant correlation.[18]
In this study, validation of the semiautomated software was tried by comparing the
preoperative estimated resection volume with the volume of the resected surgical specimen.
Assuming that the density of the liver is 1 kg/m3, the median resected volume of surgical specimen was found to be 650 mL. The median
estimated volume calculated using the Myrian software was 617 mL and the difference
between estimated and actual volume had a median value of 38 mL. Spearman’s correlation
test showed significant correlation (p-value < 0.0001) between the estimated specimen weight recorded using the Myrian software
with that of the actual specimen weight with correlation coefficient (r) value of 0.956.
Liver segmentation by the Myrian software and the color-coded 3D images obtained are
more comprehensible for the operating surgeon, who can easily guide or modify the
resection plane. Multiple possible resection planes can be drawn and resection volumes
for each can be calculated easily if there are a surgical dilemma and provide a clear
road map for the surgeon to perform a safe liver resection.
Since the volume of the resected specimen closely correlates with the estimated volume
estimated by Myrian software, it may be assumed that the estimated FLR would also
be accurate and reliable. However, a study comparing the FLR estimated by the Myrian
software with surgical outcome is required to validate this assumption.
The limitation of this study is that, the volume of surgical specimen was calculated
by standard assumption of weight-to-volume ratio of liver to be 1:1 and the actual
density of specimen to calculate volume was not determined. Also, the influence of
cirrhosis and tumor histopathology on the density of resected specimen and volume
estimation by the Myrian software was not evaluated.
Conclusion
The semiautomated software-based preoperative liver volumetry was accurate and showed
significant correlation when compared with resected surgical specimen. The software-estimated
FLR volume may hence correlate with the true residual liver volume and be a valuable
tool to select potential surgical candidates for liver resection. The liver segmentation
and color-coded 3D images provides a clear road map to the surgeon to facilitate safe
resection.
