Keywords liver stiffness - clinically significant portal hypertension - liver cirrhosis - dynamic
contrast-enhanced ultrasound
Introduction
Portal hypertension (PH) is a nearly inevitable consequence of liver cirrhosis since
between 80% and 90% of asymptomatic patients already have an elevated portal pressure
gradient
[1 ]. The increase in portal pressure can result in many other clinical complications
that
affect prognosis and the natural history of the disease and include variceal bleeding,
ascites, spontaneous bacterial peritonitis, hepatorenal syndrome, and hepatic encephalopathy
[2 ]. As a consequence, the precise grading of PH becomes essential for the treatment
and
follow-up of patients with cirrhosis.
Until now, the measurement of the hepatic venous pressure gradient (HVPG) has been
recognized as the gold standard for classifying PH [3 ]. However, routine use of HVPG in the clinical setting is limited by its invasiveness
and the needs for skilled expertise.
The recently developed contrast-enhanced ultrasound (CEUS) perfusion imaging has markedly
expanded the possibilities for detailed hepatic hemodynamics [4 ]. It has been demonstrated that CEUS-based analysis of the transit time between the
hepatic vein (HV) and hepatic artery (HA) or portal vein (PV), can be useful for predicting
the grade of PH [5 ]
[6 ]
[7 ]
[8 ].
A correlation has been reported between regional hepatic perfusion evaluated through
the
analysis of microbubble kinetics after CEUS and PH in patients with cirrhosis [9 ].
Since fibrosis is the main determinant of tissue stiffness and hepatic resistance
to
portal blood flow, liver stiffness (LS) measurement has been tested in recent years
as a novel
way of obtaining noninvasive evaluation of portal pressure.
Several lines of evidence have demonstrated that transient elastography (TE) is useful
for
assessing the severity of PH [10 ]
[11 ]
[12 ]. Moreover, noninvasive Baveno and expanded Baveno criteria based on platelet count
and
LS assessment have shown high applicability in identifying patients without high-risk
gastroesophageal varices who had no need of endoscopic surveillance [3 ].
A recent study demonstrated that the combination of LS and perfusion parameters obtained
with dynamic contrast-enhanced magnetic resonance imaging provides excellent accuracy
for
diagnosing PH since both hepatic fibrosis and altered hepatic blood flow are involved
in the
pathogenesis of this syndrome [13 ].
Hence, the present study will prospectively evaluate the diagnostic performance of
perfusion parameters measured by D-CEUS and LS assessment by point shear wave elastography
(pSWE) for the prediction of PH in patients with liver cirrhosis. The secondary aim
was to
identify the optimal cut-off of selected parameters for the diagnosis of CSPH and
severe
portal hypertension (SPH).
Patients and Methods
Patients
Between January 2017 and February 2019, all consecutive patients with liver cirrhosis
who were scheduled for HVPG measurements in our Department of Internal Medicine and
Gastroenterology were enrolled in this prospective study.
According to international guidelines, the indication for HVPG measurement was suspected
advanced liver disease based on imaging and biochemical data [3 ].
Patients with liver cirrhosis were enrolled. Additional inclusion criteria were age
>18 years and consent to HVPG measurement. Patients were excluded if they had a history
of decompensated liver disease (Child Pugh C or Child Pugh B with actual or previous
detection of variceal bleeding, ascites, or overt hepatic encephalopathy), malignant
liver
tumor, portal vein thrombosis, cerebrovascular disease, sepsis, transjugular intrahepatic
portosystemic shunt, liver transplantation, and treatment with vasoactive drugs within
2
weeks before enrollment.
Study protocol
During the enrollment period, 63 patients with liver cirrhosis underwent HVPG
measurement. Among them, 15 participants did not enter the study because of ongoing
treatment with vasoactive drugs (5 patients), portal vein thrombosis (2 patients),
ascites
(6 patients), and a previous episode of gastrointestinal bleeding (2 patients). Two
patients
were not included in the final evaluation due to inadequate visualization of right
liver
lobe.
The enrolled patients underwent Doppler ultrasound, LS measurement, and D-CEUS on
the
same day of HVPG measurement.
HVPG measurement was performed as described elsewhere [3 ]. According to the Baveno consensus workshop, the patients were classified using
a
threshold of 10 mmHg for CSPH and 12 mmHg for SPH [3 ]
Liver cirrhosis severity was assessed by Child-Pugh [14 ] and MELD scores [15 ]. The size of gastroesophageal varices was classified into two groups: F0/F1: absent
or small varices, F2/F3: moderately or marked enlarged varices.
The protocol was approved by our Institutional Review Board (approval number 13006).
Written informed consent was obtained from all study participants.
After enrollment, patients were followed up every six months with laboratory exams,
ultrasound, and clinical evaluation according to the standard surveillance program
of our
department. Any decompensation events were recorded.
Doppler ultrasound
Doppler ultrasound, LS, and D-CEUS studies were performed with an iU22 ultrasound
system (Philips) equipped with a wideband C5–2 MHz convex probe by two trained operators
(with 15 and 10 years of liver US experience) who were blinded to the clinical and
hemodynamic data of the patients.
The examination included a color Doppler examination according to a standardized
protocol in order to obtain portal vein blood velocity (PVV), hepatic venous (HV)
waveforms and damping index (DI), hepatic artery (HA) resistivity index (RI), and
splenic
artery (SA) RI as described elsewhere [16 ]
[17 ].
Point-shear wave elastography
Point-shear wave elastography
The LS evaluation was performed with the ElastPQ technique. Measurements were obtained
from the right hepatic lobe through intercostal spaces with the patient in supine
position
with suspended normal breathing and the right arm abducted. The operator selected
the most
appropriate area in the right liver lobe (usually 5th or 6th segment) free of large
vessels
and at least 2 cm below the liver capsule by moving the region of interest (ROI) perpendicular
to the center of the transducer. The median value of 10 successful LS measurements
was
obtained from each patient. The results were expressed in kilopascals (kPa). Measurements
were
considered reliable if the interquartile range was less than 30% of the median values.
Dynamic contrast-enhanced ultrasound (D-CEUS)
After Doppler ultrasound and LS evaluations, the operator obtained an intercostal
scan
of the right liver containing the right PV. Thereafter, a 2.4 mL solution of a
second-generation ultrasound contrast agent (SonoVue, Bracco) was injected as an intravenous
bolus followed by a flush of 10 ml normal saline. A dedicated, contrast-specific,
low
mechanical index technique (MI=0.08) was used in order to study the whole vascular
phase.
The overall gain was set to obtain a complete anechoic image of the liver parenchyma
for the
basal phase and the depth was regulated on the bottom of the image.
Signal enhancement of the liver parenchyma (LP) and portal vein (PV) was evaluated
in
real time and a three-minute clip was registered on a hard disk.
Finally, digitized quantification of contrast uptake was performed on the recorded
video
clip using the quantitative analysis software package QLAB, version 7.0 (Philips Healthcare)
as described elsewhere [4 ].
Five perfusion parameters were extracted from time-intensity curves: peak intensity
(PI
(in arbitrary units: AU)), time to PI (TP (in seconds)), area under the
time-intensity curve (AUC (in AU)), slope coefficient of wash in (Pw (in AU per
second)), mean transit time (MTT (in seconds)). Finally delta PI (Δ PI) was determined by subtracting the LP-PI from the PV-PI.
Statistical analysis
Categorical variables were expressed as mean±standard deviation (SD) values and
continuous data as percentages. The unpaired t-test or analysis of variance was applied
for
comparisons of normally distributed variables, while the Kruskal-Wallis’ test or Wilcoxon’s
rank-sum (Mann-Whitney’s) test was used for non-normally distributed parameters.
Correlations between noninvasive parameters and invasive hemodynamic data were made
by
Pearson’s test.
Linear regression analyses were performed according to the least-squares method. To
assess the role of selected variables in predicting CSPH, receiver operating characteristic
(ROC) curves with the area under the ROC curve (AUROC) were calculated, and DeLong’s
test
was used for pairwise comparison of the AUROCs. Optimal cut-off values were selected
on the
basis of sensitivity, specificity, positive predictive value (PPV), and negative predictive
value (NPV) using the Youden index.
General linear modeling with stepwise selection of one variable from each category
(Doppler ultrasound, D-CEUS and LS) was employed to select the most predictive parameters
for the assessment of CSPH. We calculated the estimated HVPG value based on the
corresponding regression equation, and we further tested the agreement between the
estimated
and the measured HVPG value according to Cohen’s K coefficient analysis.
Finally, parameters associated with clinical decompensation during follow-up were
tested
according to the Cox regression model.
The level of statistical significance was set at P < 0.05.
Statistical analysis was performed using Stata Software version 14.0.
Results
The study included 46 patients (31 men, 15 women; mean age±SD: 57±11 years). Demographic
and clinical data of the study population are provided in [Table 1 ].
Table 1 Demographic and clinical data of the study population.
Characteristics
All patients
(n = 46)
HVPG<10 mmHg (n = 22)
HVPG≥10 mmHg (n = 24)
p-value
Significant p-values are in bold. N: number of patients; SD:
standard deviation, AST: aspartate aminotransferase; ALT: alanine aminotransferase;
INR: international normalized ratio; PLT: platelet count; MELD: model for end stage
liver disease; HVPG: hepatic venous pressure gradient
Age, years
Mean (SD)
57.3 (10.7)
59.1 (10.4)
55.6 (10.9)
0.69
Sex, n (%)
Male/female
31 (67.4)/15 (32.6)
15 (68.2)/7 (31.8)
16 (66.7)/8 (33.3)
0.91
Etiology, n (%)
Viral
22 (47.8)
13 (59.1)
9 (37.5)
Alcoholic
9 (19.6)
3 (13.6)
6 (25.0)
0.64
Metabolic
11 (23.9)
4 (18.2)
7 (29.2)
Autoimmune
4 (8.7)
2 (9.1)
2 (8.3)
Laboratory values
Mean (SD)
Bilirubin (mg/dl)
1.63 (1.3)
1.20 (0.9)
1.40 (1.5)
0.68
Albumin (g/dL)
3.46 (0.5)
3.47 (0.5)
3.44 (0.4)
0.77
Creatinine (mg/dl)
0.87 (0.2)
0.89 (0.2)
0.86 (0.2)
0.54
AST (U/l)
40.1 (15.9)
35.7 (11.1)
44.0 (18.7)
0.07
ALT (U/l)
23.5 (15.2)
19.3 (11.3)
27.4 (17.5)
0.07
INR
1.23 (0.2)
1.23 (0.2)
1.23 (0.2)
0.91
PLT (10^9/l)
96.7 (32.9)
102.8 (33.5)
91.1 (32.1)
0.23
MELD, mean (SD)
9.8 (3.3)
9.6 (3.1)
9.9 (3.4)
0.77
Child Pugh class, n (%)
(A/B/C)
37/9/0
18/4/0
19/5/0
0.56
Varices, n (%)
F0-F1/F2-F3
31 (67.4)/15 (32.6)
19 (86.4)/3 (13.6)
12 (50.0)/ 12 (50.0)
0.03
HVPG
Mean (SD)
11.3 (6.1)
5.7 (1.9)
16.3 (3.6)
<0.0001
Linear regression analysis showed that there was an excellent significant correlation
between LS and HVPG ([Table 2 ]). Additionally, portal pressure was significantly correlated with all flow parameters
from PV ([Table 2 ]).
Table 2 Correlation between imaging parameters and HVPG measurements.
R (correlation coefficient)
p-value
Significant p-values are in bold. PI: peak intensity; AU:
arbitrary units; AUC: area under the curve; Pw : slope coefficient of wash
in; sec: seconds; TP : time to peak; MTT: mean transit time; Δ PI:
difference between PI of portal vein and PI of liver parenchyma; PVV: portal vein
velocity; RI: resistivity index; HA: hepatic artery; HV DI: hepatic vein damping
index; SA: splenic artery; cm: centimeters
Perfusion parameters
Liver parenchyma
PI (AU)
–0.797
<0.0001
AUC (AU)
–0.249
0.10
Pw
(AU/sec)
–0.363
0.01
TP
(sec)
0.035
0.82
MTT (sec)
0.398
0.006
Portal vein
PI (AU)
–0.762
<0.0001
AUC (AU)
–0.381
0.009
Pw
(AU/sec)
–0.585
<0.0001
TP
(sec)
0.544
0.0001
MTT (sec)
0.588
<0.0001
Δ PI
0.572
<0.0001
Liver stiffness
0.809
<0.0001
Doppler parameters
PVV (cm/sec)
–0.174
0.24
RI HA
0.319
0.03
HV DI
0.549
<0.0001
RI SA
0.461
0.001
Among flow parameters derived from LP, the PI, Pw , and MTT were significantly
correlated to HVPG: the correlation was substantial for PI and weak for Pw and MTT
([Table 2 ]). Finally ΔPI was positively related to HVPG ([Table 2 ]).
An excellent correlation was found between portal pressure and HV DI ([Table 2 ]) and a moderate correlation between portal pressure and SA RI and HA RI ([Table 2 ]).
Diagnostic accuracy for diagnosing clinically significant portal hypertension (HVPG
≥10
mmHg)
Among perfusion parameters, the PI, AUC, and Pw of both the LP and PV were
significantly decreased, while ΔPI, PV-TP , PV-MTT, and LP-MTT were increased in
CSPH ([Table 3 ]).
Table 3 Imaging parameters in patients without and with clinically significant portal
hypertension (HVPG < 10 mmHg vs. ≥ 10 mmHg) and without and
with severe portal hypertension (HVPG < 12 mmHg vs. ≥ 12
mmHg). Data are presented as mean±SD.
CSPH
SPH
Parameters
HVPG<10
HVPG≥10
p-value
HVPG<12
HVPG≥12
p-value
Significant p-values are in bold. CSPH: clinically significant
portal hypertension; SPH: severe portal hypertension; LP: liver parenchyma; PV:
portal vein; PI: peak intensity; AUC: area under the curve; Pw: slope coefficient
of wash in; TP : time to peak; MTT: mean transit time; LS: liver
stiffness; PVV: portal vein velocity; RI: resistivity index; HA: hepatic artery;
HV DI: hepatic vein damping index; SA: splenic artery.
Unit of measurement
. PI, AUC, Δ PI: arbitrary
units; TP , MTT: seconds; Pw
: arbitrary units per second; LS: kPa; PVV: centimeters
per second
D-CEUS
PI-LP
31.50±5.89
17.18±3.10
<0.0001
30.10±6.77
16.81±3.06
<0.0001
AUC-LP
1232.9±402.2
866.7±412.7
0.003
1154.2±434.7
908.1±425.8
0.06
Pw -LP
0.41±0.15
0.23±0.14
0.002
0.37±0.15
0.25±0.15
0.009
TP -LP
46.63±20.94
50.17±13.61
0.49
48.70±20.50
48.18±13.27
0.91
MTT-LP
27.31±12.24
39.12±14.51
0.005
29.36±12.97
38.37±15.20
0.03
PI-PV
36.19±5.74
25.43±2.98
<0.0001
35.19±6.11
25.09±2.90
<0.0001
AUC-PV
1490.5±710.7
926.9±489.2
0.002
1374.4±738.3
984.6±497.1
0.04
Pw –PV
0.49±0.17
0.29±0.11
<0.0001
0.47±0.17
0.29±0.12
0.0002
TP -PV
44.72±10.31
55.63±9.72
0.0006
44.77±9.69
57.13±9.41
0.0001
MTT-PV
30.37±9.28
50.72±24.66
0.0007
29.35±12.97
38.37±15.21
0.03
Δ PI
4.71±1.99
8.38±2.22
<0.0001
5.10±2.17
8.44±2.35
<0.0001
LS
22.18±2.82
30.76±5.08
<0.0001
22.85±3.31
31.19±5.24
<0.0001
Doppler
PVV
25.11±4.75
25.13±5.94
0.99
26.12±5.75
23.93±5.75
0.16
RI-HA
0.66±0.05
0.69±0.06
0.14
0.65±0.04
0.70±0.06
0.04
HV DI
0.44±0.13
0.64±0.16
0.0001
0.46±0.13
0.64±0.16
<0.0001
RI-SA
0.61±0.04
0.65±0.03
0.005
0.61±0.04
0.64±0.04
0.009
Clinical examples of the corresponding contrast uptake time-intensity curves from
the LP
and PV in patients with and without CSPH are shown in [Fig. 1 ].
Fig. 1 D-CEUS study. Regions of interest (ROI) were drawn on the portal vein (red circle)
and liver parenchyma (yellow circle) to measure the corresponding time-intensity curves.
a ) 51-year-old male patient with HVPG=16 mmHg. b 55-year-old female patient with HVPG=3 mmHg. Patient with severe
portal hypertension had lower PI both on LP (11.9 vs. 26.9 AU) and PV (21 vs. 29.8
AU)
and higher Delta PI (9.1 vs. 2.9 AU). PI: peak intensity; LP: liver parenchyma; PV:
portal vein; Delta PI: difference between PI-PV and PI-LP.
LS, HV DI, and RI of SA were all significantly higher in patients with CSPH ([Table 3 ]).
Diagnostic accuracy for diagnosing severe portal hypertension (HVPG ≥12 mmHg)
In patients with SPH, the results of time-intensity curve analysis were significantly
different in three of the five D-CEUS parameters extracted from the LP (PI, Pw ,
and MTT) and in all parameters from the PV ([Table 3 ]). In particular, we found a significant decrease in the PI, AUC, and Pw
and a significant increase in the ΔPI, MTT, and TP in the SPH group.
Among Doppler parameters, HV DI, SA RI, and HA RI were increased in patients with
SPH
([Table 3 ]).
Finally, a significant increase in LS was also observed in patients with SPH ([Table 3 ]).
Performance for diagnosis of CSPH and SPH
For the assessment of CSPH and SPH, the AUROC of LP-PI was greater than that of the
other indices. For the diagnosis of CSPH, the AUROC was 1.000, and the optimal cut-off
value
of LP-PI was 23.3 AU with a sensitivity and a specificity of 100% (Supplementary Table
1) . For the
diagnosis of SPH, the AUROC was 0.981, and the optimal cut-off value of LP-PI was
22.3 AU
with a sensitivity of 92% and a specificity of 100% (Table 4). Excellent results were
obtained also with the AUROCs of other perfusion parameters and in particular of PV-PI:
ΔPI,
PV-Pw for the assessment of CSPH (0.977, 0.888, and 0.848, respectively) (Supplementary Table 1) and PV-PI, ΔPI, PV- Pw, PV-Tp for the assessment of SPH
(0.956, 0.853, 0.811, and 0.818, respectively) (Supplementary Table
1) .
The AUROCs of LS for diagnosing CSPH and SPH were 0.923 and 0.894, respectively, with
the optimal cut-off values of 24.2 for CSPH (sensitivity 91.7%, specificity 81.8%)
(Supplementary Table 1) and 25.7 for SPH (sensitivity 80.9%,
specificity 84%) (Supplementary Table 1) .
In pairwise comparison of the AUROCs of Doppler parameters, HV DI allowed better
assessment of CSPH compared with SA RI (AUROCs 0.824 and 0.761, respectively). Similar
results were obtained for the evaluation of SPH (AUROCs 0.818, 0.733, 0.662, and 0.613
for
HV DI, SA RI, HA RI, and PVV, respectively) (Supplementary Table
1) .
Combination of noninvasive methods
In order to avoid the influence of perfusion features related to patient hemodynamics,
among different D-CEUS parameters with excellent correlation with PH, we decided to
use ΔPI
for multivariate analysis.
According to linear regression modeling, only LS was significantly related to the
presence of CSPH (p<0.0001) with a coefficient of 0.82 (95%CI: 0.64–1.05) (Supplementary Table 2) and optimal agreement between the estimated and
the measured HVPG value (Cohen’s K Coefficient 0.809; p<0.0001) ([Fig. 2 ]).
Fig. 2 Scatter dot plot demonstrating the correlation between estimated and measured HVPG
values. HVPG: hepatic venous pressure gradient.
Prediction of clinical outcome
During a mean follow-up time of 34 months, 11 patients experienced clinical
decompensation and 3 patients underwent orthotopic liver transplantation. The most
frequent decompensation event was ascites (6 patients), followed by gastrointestinal
bleeding (3 patients), and hepatic encephalopathy (2 patients).
As expected HVPG and MELD values were significantly higher in patients with clinical
decompensation during follow-up (HR [95% CI] 1.15 [1.04–1.28], p=0.009 for HVPG and
1.25
[1.08–1.45], p=0.003 for MELD) (Supplementary Table 3) .
None of the ultrasound parameters were related to the occurrence of decompensation
events except for two perfusion parameters extracted from the portal vein: Tp and
MTT (HR
[95% CI] 1.09 [1.01–1.16], p=0.01 and 1.06 [1.02–1.09], p=0.001, respectively) (Supplementary Table 3) .
Discussion
The complications of cirrhosis are mainly associated with the occurrence of CSPH,
in
particular in the early phases of the disease. In this context an accurate assessment
of PH
becomes relevant to allow optimization of individualized treatment.
The results of our study suggest that both LS and perfusion parameters had a high
accuracy
in the diagnosis of CSPH and SPH. We found three categories: D-CEUS-related perfusion
parameters, LS, and Doppler values that independently predicted PH.
These noninvasive parameters mirror the two main pathogenetic components of PH, namely
the
liver architectural derangement and the impairment of hepatic perfusion.
With respect to the first aspect, liver elastography has provided a major advantage
in the
assessment of patients with compensated chronic liver disease [18 ]. Numerous previous studies have correlated TE with HVPG [13 ]
[19 ]
[20 ] and, according to the Baveno VII consensus report, this method is sufficiently
accurate to evaluate PH with a cut-off value of 25 kPa to rule in and 15 kPa to rule
out CSPH
[3 ].
Preliminary results with SWE confirmed the significant correlation between LS and
HVPG
values [13 ]
[21 ]
[22 ]
[23 ].
In regard to pSWE and in particular ElastPQ, it has been demonstrated that the combination
of platelet count and LS values may be useful to select patients with chronic liver
disease
that can safely avoid screening endoscopy [24 ].
Our study provides relevant additional information regarding the noninvasive diagnosis
of
CSPH using this elastographic modality since the use of ElastPQ to determine the presence
of
CSPH has not been previously tested.
The finding of a correlation between HVPG and LS consolidates the close relationship
between the progression of liver fibrosis and that of portal pressure. However, in
parallel to
an increase in LS, liver cirrhosis can cause hemodynamic changes associated with both
raised
hepatic resistance and the development of portosystemic collaterals [2 ].
In this context the association of an alternative method capable of evaluating liver
perfusion could carefully reproduce the clinical background typical of PH.
According to previous results, only weak correlations have been shown between Doppler
parameters and the presence of PH [25 ] except for HV DI, an established sign of high portal pressure and liver dysfunction
[18 ].
We demonstrated that liver perfusion evaluation by D-CEUS may exceed the limitations
of
Doppler measurement. In our series several perfusion parameters derived from time-intensity
curves were closely correlated with HVPG values. Among them, PI-LP had the best diagnostic
performance and was negatively correlated with HVPG with an AUROC of 1.000 for predicting
the
presence of CSPH and 0.992 for assessing SPH. We hypothesized that this CEUS parameter
would
be a measure of the liver blood content in the different stages of disease. Not only
the
increase in fibrosis and architectural changes but also the development of hyperdynamic
circulation and arteriovenous shunts is associated with a reduction in portal perfusion
and
liver blood content that produces a decrease in signal intensity.
Our results confirm previously reported data obtained with MR which show that portal
perfusion to the liver is inversely related to portal pressure and hepatic resistance
[14 ].
The concomitant application of different ultrasound perfusion parameters could be
very
interesting since it not only reflects the entire pathogenetic process of PH, but
also makes
it possible to overcome the limitations of each single parameter.
In the multivariate logistic regression analysis, however, only LS showed good performance
for the diagnosis of CSPH.
On the other hand, perfusion parameters related to PV flow were associated with the
occurrence of decompensation events during follow-up together with standard parameters
such as
HVPG and MELD.
These findings suggest that D-CEUS might be a useful complement to a standard scoring
system for monitoring the clinical course of the disease.
Our study has some limitations. First, the small number of patients enrolled means
it is
not possible to draw certain conclusions regarding the best noninvasive method to
diagnose
CSPH and to predict decompensation events. Second, the study population was somewhat
heterogeneous even if we excluded patients with very severe disease. Moreover, variability
in
measurements is an issue with these techniques, and the reproducibility of the results
could
be influenced by the specific equipment and setting employed. In particular, the identified
cut-off may not be universally applicable. Future work should probably focus on the
development of practical and widely accepted systems for the classification of PH
based on
D-CEUS and LS findings.
To conclude, our preliminary results suggest that both LS and perfusion parameters
obtained by D-CEUS provide excellent accuracy for assessing the degree of PH. As a
reliable
and noninvasive procedure, these US-based techniques are a promising method for detecting
CSPH
and SPH in clinical practice. Further large studies are needed to prospectively validate
these
findings and also to determine whether LS and D-CEUS parameters can be used for monitoring
the
hemodynamic response to therapy.
Personalized medicine could benefit from this noninvasive approach, especially in
high-risk situations, such as primary prophylaxis of liver decompensation.
