Castera du pitie 12 janvier 2015 selection (1)

Laurent CASTERA
Service d’Hépatologie,
Hôpital Beaujon, Université Paris VII
Alternatives à la PBH :
mesure de l’élasticité
hépatique
DU Hépatites Virales Cytokines et Antiviraux
Pitie, Paris, 12 Janvier 2015

Méthodes non invasives disponibles
2 approches différentes mais complémentaires
Biomarqueurs
Approche « biologique »
Castera & Pinzani. Lancet 2010; 375: 419-20
Approche « physique »
Elasticité hépatique

Mesure de l’élasticité hépatique
ARFI SSIFibroScan

◆  Principe & limites
◆  Performances diagnostiques
◆  Comparaison avec les biomarqueurs
◆  Suivi de la progression de la fibrose
◆  Nouvelles techniques
Plan

Elastométrie (FibroScan)
= 100
x
Biopsie foie
2.5 cm
Volume exploré
4 cm
1 cm ∅

%
-5
0
5
Depth(mm)
Time (ms)
0 20 40 60
10
20
30
40
50
60
E = 3.0 kPa
F0
Sandrin et al. UMB 2003; 12: 1705-13
VS = 1.0 m/s
E = 27.0 kPa
F4
VS = 3.0 m/s
Principe
“Plus le foie est dur, plus l’onde se propage vite”

Mesure de l’élasticité hépatique
75 kPa3
15 655.5
Normale
Roulot et al. J Hepatol 2008; 48: 606-13

Variability of transient elastography
Nascimbeni et al. Clin Gastroenterol Hepatol 2014; In press
531 paired liver stiffness measurements < 1 year from 452 patientser stiffness measurement (LSM1) and variability between paired LSMs. (A) Corre-
thmic absolute variability [log (LSM2-LSM1)] between paired LSMs (r ¼ 0.542; P <
LSM1 and logarithmic relative variability {log [(LSM2-LSM1)/LSM1*100]} between
Variation > 30% 34%
Variation > 50% 12%
Variation > 20% 50%
2 different operators
IQR / M
LSM > 7 kPa
BMI
ALT levels

How to interpret FibroScan results
manufacturer’s recommendations
Success rate > 60%
10 validated measures
IQR < 30% median
Castera, Forns & Alberti. J Hepatol 2008; 48: 835-47

Applicability of transient elastography
Unreliable 15.8%
IQR/LSM > 30%
9.2%
SR < 60%
8.1%
VS < 10
3.1%
Failure 3.1%
Valid shot = 0
Castéra et al. Hepatology 2010; 51: 828-35
FibroScan
not applicable
in 20%
of cases
N=13669 examinations
Obesity Operator
experience

Unreliable
IQR/LSM > 30%
SR < 60%
VS < 10
Failure
Valid shot = 0
XL Probe:
Does it really overcome the limitations of M probe ?
XL vs. M probe:
1% vs. 16%
N= 276 patients with BMI > 28 kg/m2
Myers et al. Hepatology 2012; 55:199-208.
XL vs. M probe:
27% vs. 50%

we conﬁrmed
M using the
obese patients
or advantage
use in obese
patients than
For example,
patients with
he M probe.
with extreme
59%, respec-
were obtained
mpared with
patients with
g the M and XL
stiffness between
ss values (P <
mean difference
dashed lines the
Fig. 6. Relationship between liver stiffness measured using the M
and XL probes and the stage of liver ﬁbrosis in patients with (A) viral
XL Probe: the cut-off issue
N= 65 NAFLD patients Myers et al. Hepatology 2012; 55:199-208.
7.8 vs. 6.4 kPa
M vs. XL

Boursier et al. Hepatology 2013; 57: 1182-91
N=1165 patients with CLD; 70% HCV
0< IQR/M 0.30
. LSE with IQR/M
uracy than LSE with
with 0.10< IQR/M
LSE with IQR/M
not reach statistical
as a diagnostic cut-
tients for significant
dians !7.1 kPa, but
kPa: 81.5% versus
By using 12.5 kPa as
ell-classified patients
medians <12.5 kPa,
2.5 kPa: 94.3% ver-
3
). LSE thus demon-
e value for cirrhosis
value for significant
ient positive predic-
ient negative predic-
Finally, the rate of
classification derived
t significantly differ-
0/1: 64.5%, FFS2/3:
79).
LSE with IQR/M >0.30 had lower accuracy for signif-
icant fibrosis than LSE with IQR/M 0.30 (67.6%
versus 84.3%, P < 10À3
). In patients with LSE me-
dian !12.5 kPa, LSE with IQR/M >0.30 had lower
accuracy for cirrhosis than LSE with IQR/M 0.30
(45.1% versus 64.0%, P ¼ 0.011).
Fig. 1. Rate of well-classified patients by the LSE classification
derived from Castera et al.12
cutoffs, as a function of the three
classes of the classification and IQR/M.
P=NS
How does applicability translates
into accuracy?

Boursier et al. Hepatology 2013; 57: 1182-91
N=1165 patients with CLD; 70% HCV
How does applicability translates
into accuracy?
Table 5. New Reliability Criteria for LSE and Ensuing I
and Poorly Reliab
<7.1
LSE Diagnosis *: FFS0/1
IQR/M 0.10
0.10< and 0.30
>0.30
Because it is quick and easy in most cases, LSE should include 10 valid meas
*LSE diagnosis after categorization of LSE median into estimated Metavir fibro
significant fibrosis and 12.5 kPa for cirrhosis (12).
HEPATOLOGY, Vol. 57, No. 3, 2013
ble 5. New Reliability Criteria for LSE and Ensuing Interpretation as Very R
and Poorly Reliable (dark gray) LSE
LSE Median
<7.1 7.1 and <12.5
LSE Diagnosis *: FFS0/1 FFS2/3
0.10 Very reliable LSE
0.10< and 0.30 Reliable LSE
>0.30 Poorly relia
s quick and easy in most cases, LSE should include 10 valid measurements whatever the LSE s
osis after categorization of LSE median into estimated Metavir fibrosis stages (FFS) according to
osis and 12.5 kPa for cirrhosis (12).
he subgroup with IQR/M >0.30 and LSE median <7.1.
GY, Vol. 57, No. 3, 2013
liability Criteria for LSE and Ensuing Interpretation as Very Reliable (whi
LSE Median
<7.1 7.1 and <12.5 !12
FFS0/1 FFS2/3 FFS
Very reliable LSE
and 0.30 Reliable LSE
Poorly reliable LSE
n most cases, LSE should include 10 valid measurements whatever the LSE success rate.
ation of LSE median into estimated Metavir fibrosis stages (FFS) according to the diagnostic
for cirrhosis (12).
QR/M >0.30 and LSE median <7.1.
3, 2013
a for LSE and Ensuing Interpretation as Very Reliable (white), Reliable
LSE Median
<7.1 7.1 and <12.5 !12.5
FFS0/1 FFS2/3 FFS4
Very reliable LSE
Reliable LSE
Poorly reliable LSE
should include 10 valid measurements whatever the LSE success rate.
an into estimated Metavir fibrosis stages (FFS) according to the diagnostic cutoffs of Caster
LSE median <7.1.
BOURSIE
for LSE and Ensuing Interpretation as Very Reliable (white), Reliable (g
LSE Median
<7.1 7.1 and <12.5 !12.5
FFS0/1 FFS2/3 FFS4
Very reliable LSE
Reliable LSE
Poorly reliable LSE
hould include 10 valid measurements whatever the LSE success rate.
into estimated Metavir fibrosis stages (FFS) according to the diagnostic cutoffs of Castera et
SE median <7.1.
BOURSIER E
Ensuing Interpretation as Very Reliable (white), Reliable (gray),
rly Reliable (dark gray) LSE
LSE Median
7.1 and <12.5 !12.5
Patient Rate (%)FFS2/3 FFS4
Very reliable LSE 16.6
Reliable LSE 74.3 †
Poorly reliable LSE 9.1
0 valid measurements whatever the LSE success rate.
Metavir fibrosis stages (FFS) according to the diagnostic cutoffs of Castera et al.: 7.1 kPa for
.
BOURSIER ET AL. 1187

the Mann–Whitney U
significant at P 0
formed using SPSS, v
many).
Results
The proof-of-concep
with chronic hepati
increase in liver stiffn
intake (0 min), 15 a
stiffness normalized 3
These data served to
60 min for the subseq
In the larger cohor
searched for significa
meal using an intrain
measurements that co
tion. For every sing
measurements at fast
Mean of 8 patients (± SEM)
–60 –30 0 30 60 90 120 150 180
4
6
8
10
Time (min)
Liverstiffness(kPa)
P = 0.01
Break-
fast
Fig. 1. Proof-of-concept pilot trial. Patients (n = 8) were evaluated
for liver stiffness over a total time period of 240 min. Between time
points À 30 and 0 min, patients ingested a standardized meal. Data
are presented as mean Æ SEM. Significance as calculated by the
Wilcoxon test. SEM, standard error of mean.
Food intake increases liver stiffness
Influence of food intake Pr
and
Stiff
Mea
(kPa
ful f
B be
same
discr
esop
the
when
Disc
Table 3. Baseline and Postmeal Liver Stiffness Values in the
125 Patients Included in the Study Stratified by Liver
Fibrosis Stage
F0-1
(n ¼ 50)
Median IQR
F2-3 (n ¼ 35)
Median IQR
F4
(n ¼ 40)
Median IQR JT test P Value
S0 (kPa) 5.0 1.4 10.7 3.4 21.2 25.7 <0.001
S15 (kPa) 5.9 1.7 12.2 4.3 24.5 27.3 <0.001
S30 (kPa) 6.2 1.8 14.2 5.1 24.9 27.3 <0.001
S45 (kPa) 5.7 1.4 12.1 5.0 24.9 28.4 <0.001
S60 (kPa) 5.5 1.3 11 4.2 22.7 27.7 <0.001
Smin (kPa) 5.0 1.4 10.7 3.4 21.2 25.9 <0.001
Smax (kPa) 6.7 1.9 13.2 5.0 25.4 28.7 <0.001
Sdelta (kPa) 1.9 0.9 2.7 0.8 4.7 2.8 <0.001
Sdelta (%) 33.6 21.1 25.3 8.6 16.6 7.5 <0.001
Abbreviations: F0-F4, METAVIR stage of fibrosis; IQR, interquartile range; JT,
Jonckheere-Terpstra test; S0-S60, stiffness values at different time points duringArena et al. Hepatology 2013; 58: 65-72
Mederacke et al. Liver Int 2009; 29: 1500-6
TE should be performed
in fasting patients

Confounders of liver stiffness
summary for clinical practice
Tapper, Castera, Afdhal. Clin Gastroenterol Hepatol 2015; In press

FibroScan: meta-analyses
Chon et al. PLoS ONE 2012
liver parenchyma between fibrotic bands in patients with CHB
than in those with CHC. [47] These two observations might have
an optim
a design
Table 4. Characteristics of previous reported meta-analyses versus curre
Number of
included
studies
Number of
included subjects
for analysis AUROC
$ F2 $ F3
Talwalkar15
9 2,083 0.870 N/A
Stebbing16
22 4,760 0.84 0.89
Fredrich-rust et al17
50 8,206 0.84 0.89
Tsochatzis et al18
40 7,723 N/A N/A
Chon et al 18 2,772 0.859 0.887
AUROC, area under the receiver operating characteristic curve; kPa, kilopascal.
doi:10.1371/journal.pone.0044930.t004
parenchyma between fibrotic bands in patients with CHB
in those with CHC. [47] These two observations might have
an optimal referen
a designated liver
ble 4. Characteristics of previous reported meta-analyses versus current study.
Number of
included
studies
Number of
included subjects
for analysis AUROC
S
S
$ F2 $ F3 F4 $
walkar15
9 2,083 0.870 N/A 0.957 70
bbing16
22 4,760 0.84 0.89 0.94 70
drich-rust et al17
50 8,206 0.84 0.89 0.94 N
chatzis et al18
40 7,723 N/A N/A N/A 79
on et al 18 2,772 0.859 0.887 0.929 7
OC, area under the receiver operating characteristic curve; kPa, kilopascal.
10.1371/journal.pone.0044930.t004
Talwalkar et al. CGH 2007 Friedrich-Rust et al. Gastroenterology 2008
Tsochatzis et al. J Hepatol 2011Stebbing et al. APT 2010

14.6
Transient elastography for cirrhosis
(n=1007 patients with various CLD, 165 with cirrhosis)
3 75
correctly classified 92 %
Ganne-Carrié et al. Hepatology 2006; 44: 1511-7
F = 4
74%
4.5%
misclassified
17%
3.5 %
misclassified
F < 4
96%
83%

75 KPa3
FibroScan : which cut-offs ?
de Ledinghen et al. JAIDS 2006
12.5
HCV
11.0
HBV
Marcellin et al. Liver Int 2009 Castera et al. Gastroenterology 2005
F4:
17.1
PBC/PSC
Corpechot et al. Hepatology 2006
8% 25%
11.8
HIV-HCV
24% 19%

Cirrhosis: Post-test probabilities
(pre-test:14%)
N=1307 patients with viral hepatitis; 14% with cirrhosis
Posttestprobabilitiesofcirrhosis
72%
50% 40% 10%
0-3%
7-27%
< 7kPa 7-17kPa >17kPa
Degos et al. J Hepatol 2010; 53: 1013-21

Castera et al. Gastroenterology 2005; 128: 343-50.
Comparaison des approches
fibrose significative
P=NS
P=NS

N= 1307 patients; F4: 25%
.

P<0.0001
cirrhose

N= 436 patients; F4: 14%.

cirrhose
Zarski et al. J Hepatol 2012; 56: 55-62
ZARSKI

superior to the best blood tests or Fibroscan™ alone in the ‘‘per-
protocol’’ analysis (382 patients). However, when we considered
the population of 436 patients (‘‘intention to diagnose popula-
tion’’) the combination of Fibroscan™ plus a blood test markedly
classiﬁed. This percentage increases to 75% for a length of 25 mm
[3]. Also, a 25 mm biopsy is considered the optimal length for
accurate liver evaluation. Considering this, in our study a sam-
pling error for liver biopsy remains since only 50% of patients
Table 3. Performance of blood tests and Fibroscan™ for the diagnosis of cirrhosis (F4).
n = 436* n = 382‡
AUROC 95% CI p Sidak AUROC 95% CI p Sidak
FIBROMETER®
0.89 [0.86;0.93] 0.90 [0.86;0.93]
FIBROTEST®
0.86 [0.83;0.90] 0.325 0.87 [0.82;0.91] 0.321
APRI 0.86 [0.81;0.91] 0.141 0.87 [0.82;0.91] 0.410
ELFG 0.88 [0.83;0.92] 0.883 0.87 [0.83;0.92] 0.860
HEPASCORE®
0.89 [0.86;0.93] 1.000 0.89 [0.85;0.92] 0.998
FIB4 0.83 [0.76;0.89] 0.018 0.84 [0.77;0.90] 0.069
FIBROSCAN™
(interpretable results)
- - - 0.93 [0.89;0.96] 0.559
⁄
CHC patients having all blood tests; à
CHC patients with all the tests and interpretable Fibroscan™.
JOURNAL OF HEPATOLOGY

Summary: significant fibrosis
Transient elastographySerum markers
=

Summary: cirrhosis
Transient elastographySerum markers
<

Qu’en est-il de la combinaison
des méthodes?

Castera et al. Gastroenterology 2005; 128: 343-50.
ElastométrieMarqueurs sériques
+
Bien
classés F≥2:
75%
La combinaison augmente
les performances diagnostiques

Poynard et al. Plos One 2008
Concordance in world without gold standard:
a new way to increase diagnostic accuracy

Boursier et al. Am J Gastroenterol 2011; 106: 1255-63
N= 729 patients with CHC
La combinaison augmente
les performances diagnostiques

•  Good reproducibility
• High applicability (95%)
• Low cost & wide
availability (non patented)
•  Advantages
FibroScan
• Genuine property of the liver
• High performance for cirrhosis
• User-friendly
•  Advantages
Biomarkers
•  Disadvantages
• Non specific of the liver
• Performance for cirrhosis
• Cost & availability (patented)
• Low applicability (80%)
• False positive (inflammation)
• Requires a dedicated device-
Biomarkers vs. FibroScan
summary
Castera L . Gastroenterology 2012; 142: 1293-302

Classification of chronic liver disease based on histological, clinical, hemodynamic, and biological parameters. In the noncirrhotic stage
GARCIA-TSAO ET AL. HEPATOLOGY, April 2010
Classification of chronic liver disease based on histological, clinical, hemodynamic, and biological parameters. In the noncirrhotic stag
Classification of chronic liver disease based on histological, clinical, hemodynamic, and biological parameters. In the noncirrhotic stage
Classification of chronic liver disease based on histological, clinical, hemodynamic, and biological parameters. In the noncirrhotic stag
Garcia-Tsao et al. Hepatology 2010; 51: 1445-9
Now There Are Many (Stages) Where Before There
Was One: In Search of a Pathophysiological
Classification of Cirrhosis
Guadalupe Garcia-Tsao,1 Scott Friedman,2 John Iredale,3 and Massimo Pinzani4
F
or more than a century and a half, the description
of a liver as “cirrhotic” was sufficient to connote
both a pathological and clinical status, and to as-
sign the prognosis of a patient with liver disease. How-
ever, as our interventions to treat advanced liver disease
have progressed (e.g., antiviral therapies), the inadequacy
of a simple one-stage description for advanced fibrotic
liver disease has become increasingly evident. Until re-
cently, refining the diagnosis of cirrhosis into more than
one stage hardly seemed necessary when there were no
interventions available to arrest its progression. Now,
however, understanding the range of potential outcomes
based on the severity of cirrhosis is essential in order to
predict outcomes and individualize therapy. This position
paper, rather than providing clinical guidelines, attempts
to catalyze a reformulation of the concept of cirrhosis
from a static to a dynamic one, creating a template for
further refinement of this concept in the future.
We already make the clinical distinction between com-
pensated and decompensated cirrhosis, and are incremen-
tally linking these clinical entities to quantitative variables
such as portal pressure measurements and emerging non-
invasive diagnostics. Moreover, mounting evidence sug-
gests that cirrhosis encompasses a pathological spectrum
which is neither static nor relentlessly progressive, but
rather dynamic and bidirectional, at least in some pa-
tients. Thus, there is a pressing need to redefine cirrhosis
in a manner that better recognizes its underlying relation-
changes, and more faithfully reflects its progression, re-
versibility and prognosis, ultimately linking these param-
eters to clinically relevant outcomes and therapeutic
strategies. The Child-Pugh and Model for End-Stage
Liver Disease (MELD) scores are currently deployed to
define prognosis by modeling hepatic dysfunction, but do
not provide direct evidence of the stage or dynamic state
of cirrhosis. The need for more refined cirrhosis staging is
especially germane given the increasing use of effective
antiviral treatments in patients with hepatitis B virus
(HBV) and hepatitis C virus (HCV) cirrhosis and the
emergence of effective antifibrotic agents, wherein we
must define favorable or unfavorable endpoints that cor-
relate with a discrete clinical outcome in patients with
cirrhosis.
The normal liver has only a small amount of fibrous
tissue in relation to its size. As a result of continued liver
injury, however, there is progressive accumulation of ex-
tracellular matrix, or scar. Although different chronic liver
diseases are characterized by distinct patterns of fibrosis
deposition,1 the development of cirrhosis represents a
common outcome leading to similar clinical conse-
quences that impose an increasing burden in clinical prac-
tice.
Anatomical-Pathological Context
notably activated hepatic stellate ce
broblasts, as well as key cytokines su
growth factor and transforming grow
roles of bone marrow–derived cells a
epithelial-mesenchymal transition a
tion, but it is unlikely that these sour
provide a major contribution to hep
trix in chronic human liver disease
proteases that degrade scar and the p
them are better understood. Moreo
understanding of distinctive pathoge
sis at different stages and from differ
that fibrosis may be customized acco
and underlying cause.
Cirrhosis in experimental model
may be reversible.24 Following withd
stimulus, a dense micronodular cirrh
modeling to a more attenuated, m
However, some septa will persist, like
laid down early in the injury and ar
“mature” (i.e., cross-linked).
Moreover, in experimental mode
may be the site of neoangiogenesis.
already present in chronic inflamm
concurrent with the fibrogenic proce
a role in the pathogenesis of portal
effectiveness of therapeutic angioge
only improving fibrosis, but also in
sure, is suggested by data from anima
been established in humans.27 Altho
HEPATOLOGY, Vol. 51, No. 4, 2010
lassification of chronic liver disease based on histological, clinical, hemodynamic, and biological parameters. In the noncirrhotic stage
RCIA-TSAO ET AL. HEPATOLOGY, April 2010
75 kPa3
5.5 15 65

OV grade II / III
27
Ascites
49
HCC
54
Bleeding
63 kPa12 75
Foucher et al. Gut 2006; 55: 403-8.
Complications de la cirrhose
711 patients with liver diseases
F3F4 144

TE for predicting PTH, OV, LOV
meta-analysis
Shi et al. Liver Int 2013; 56: 62-71
‘positive’ measurement. Furthermore, a ‘negative’ mea-
surement was also informative, as significant portal
helpful tool for management patients with PHT in
chronic liver diseases (33).
(A) (B) (C)
Fig. 2. HSROC curve of the TE for evaluation of PHT. (A) TE detection for significant portal hypertension; (B) TE detection for oesophageal
varices; (C) TE detection for large oesophageal varices; The size of the dots for 1-specificity and sensitivity of the single studies in the ROC
space was derived from the respective sample size. HSROC for significant portal hypertension was 0.93, for oesophageal varices detection
was 0.84, and for large oesophageal varices detection was 0.78. HSROC, hierarchical summary receiver operating characteristic; PHT, portal
hypertension; TE, transient elastograpy.
TE for portal hypertension Shi et al.
(A) (B) (C)
(A) (B) (C)
18 studies; N= 3644 patients
PTH OV LOV
AUC: 0.93 AUC: 0.84 AUC: 0.78

Résumé
◆  L’élasticité hépatique est bien corrélée avec le
gradient portal et la présence (taille?) des VO.
◆  Les performances de l’élastométrie et des
biomarqueurs sont cependant insuffisantes pour
remplacer la fibroscopie pour la recherche de
VO.

Liver stiffness
Relationship with liver-related events
diagnosed if coincide
the tumor did not
performed. When t
examination was rep
Statistical analyses
Data are expresse
median (range), or n (
patients with and with
the chi-squared and
predictors of LRE
multivariate Cox pro
used. Hazard ratios
intervals (CIs) are in
characteristic (ROC)
were used to calcula
prediction of LRE d
sensitivity and specif
were expressed in per
HCC were calculate
value,0.05 on a t
significant. Statistica
Figure 2. Cumulative incidence rates of LREs based on
stratified LSM values (Kaplan-Meier plot). Patients with LSM
value .19 kPa were at a significantly greater risk of LREs development
Kim et al. PloSOne 2012; 7: e36676
N=128 HBV patientsF3-F4

Elasticité hépatique
survie sans complications
Robic et al. J Hepatol 2011; 55: 1017-24
N=100 patients CLD
84.1%, respectively
f any complication
85.4%, respectively,
p <0.001) (Fig. 2B).
risk of PHT related
ng PHT related com-
0.845 [0.767–0.923]
tic patients, HVPG
values being 0.725
ectively. (Fig. 3B).
e of signiﬁcant PHT
remaining free of
pectively (Log Rank
e patients with a
mplications. In the
a 10 mmHg thresh-
1.0
C: 0.815 (0.727-0.903)
the prediction of liver
0.0
0.2
a
Days
0.0
0.2
0.4
0.6
0.8
1.0
0 200 400 600 800
0 200 400 600 800
Survivalfreeof
anycomplications
Days
LS <21.1 kPa
LS ≥21.1 kPa
B
Fig. 2. Risk of liver related complications according to HVPG or liver stiffness.
(A) Probability of remaining free of liver related complications according to the
10 mmHg-threshold for HVPG. (B) Probability of remaining free of liver related
complications according to the 21.1 kPa-threshold for liver stiffness.

Elasticité hépatique & survie
Vergniol et al. Gastroenterology 2011;Figure 2. Overall survival probability according to liver stiffness, biomarkers, and liver biops
NICAL–LIVER,
NCREAS,AND
LIARYTRACT
Figure 2. Overall survival probability according to liver stiffness, biomarkers, and liver biopsy. (A) O
the diagnosis of severe ﬁbrosis or cirrhosis. (B) Overall survival according to different cut-offs of live
CLINICAL–LIVER,
PANCREAS,AND
BILIARYTRACT
N=1457 patients VHC

Elasticité hépatique & survie
Corpechot et al. Gastroenterology 2014;N=168 patients CSP

Friedrich-Rust et al. Radiology 2009 ; 252: 595-604
Challengers for measuring liver stiffness
ARFI (VirtualTouch®))
Nightingale et al. UMB 2002 ; 28: 227-35

Challenger for measuring liver stiffness
Supersonic shear Imaging (Aixplorer®)
Muller et al. UMB 2009; 35: 219-29
Bavu et al. UMB 2011;37: 1361-73
Contrary to FS, as vibration induced by the radiation
force creates a short transient excitation, the frequency
bandwidth of the generated shear wave is large, typically
ranging from 60 to 600 Hz (Fig. 3). Such wideband
‘‘shear wave spectroscopy’’ can give a refined analysis
of the complex mechanical behavior of tissue. As shown
in Figure 3, the shear wave dispersion law can be assessed
from displacement movies in the region-of-interest.
Thus, the global elasticity imaged by SSI makes use
of higher frequency content and is also influenced by the
dispersive properties of the liver tissues because it aver-
ages the full mechanical response of the liver tissues
over a large bandwidth. In parallel, SWS provides
a refined analysis in a larger box of these dispersive prop-
erties of tissues by estimating frequency dependence of
the shear wave speed.
Statistical methods
The diagnosis performance of FS and SSI are
compared by using receiver operating characteristic
(ROC) curves and box-and-whisker curves on the same
cohort. A patient was assessed as positive or negative ac-
cording to whether the noninvasive marker value was
greater than or less than to a given cutoff value, respec-
tively. Connected with any cutoff value is the probability
of a true positive (sensitivity) and the probability of a true
negative (specificity). The ROC curve is a plot of
sensitivity vs. (1-specificity) for all possible cutoff values.
The most commonly used index of accuracy is the area
1366 Ultrasound in Medicine and Biology Volume 37, Number 9, 2011
over a large bandwidth. In parallel, SWS provides
a refined analysis in a larger box of these dispersive prop-
erties of tissues by estimating frequency dependence of
the shear wave speed.
Statistical methods
The diagnosis performance of FS and SSI are
compared by using receiver operating characteristic
(ROC) curves and box-and-whisker curves on the same
cohort. A patient was assessed as positive or negative ac-
cording to whether the noninvasive marker value was
greater than or less than to a given cutoff value, respec-
tively. Connected with any cutoff value is the probability
of a true positive (sensitivity) and the probability of a true
negative (specificity). The ROC curve is a plot of
sensitivity vs. (1-specificity) for all possible cutoff values.
The most commonly used index of accuracy is the area
under the ROC curve (AUROC), with values close to
1.0 indicating high diagnosis accuracy. Optimal cutoff
values for liver stiffness were chosen to maximize the
sum of sensitivity and specificity and positive and nega-
tive predictive values were computed for these cutoff
values. By using these cutoff values, the agreement
between FS and SSI was evaluated. Statistical analyses
were performed with Matlab R2007a software (Math-
works, Natick, MA, USA) using the statistical analysis
toolbox and Medcalc software (Mariakerke, Belgium).
RESULTS
Liver stiffness mapping using SSI
The Young’s modulus corresponding to the stiffness
of the liver tissues are presented for 4 patients in Figure 4.
The elasticity mapping is superimposed with the corre-
sponding B-mode images on which the fat and muscle
region are well differentiated from the liver region and
the elasticity is mapped only in the liver region.
Figure 4a, b, c and d show the elasticity mapping for
patients who have been classified as predicted fibrosis
levels F1, F2, F3 and F4, respectively.
The median elasticity derived from these maps are
Fig. 4. Bidimensional liver elasticity maps assessed using the
supersonic shear imaging (SSI) technique superimposed to
the corresponding B-scan. The Young’s modulus representing
the liver stiffness is represented in color levels. (a): patient
59 - F1. E 5 4.78 6 0.83 kPa (b): patient 51 - F2. E 5 10.64 6
1.10 kPa (c): patient 39 - F3. E 5 14.52 6 2.20 kPa (d): patient
22 - F4. E 5 27.43 6 2.64 kPa.

Supersonic shear Imaging (Aixplorer®)
Comparison with TE and ARFI in CLD
Cassinoto et al. J Hepatol 2014; in press
N= 349 patients with CLD
ble 3: Areas under the receiver operating characteristic curve (with 95% confidence
erval) for the diagnostic accuracy of SSI, Fibroscan, ARFI, and serum fibrosis
logical markers for the diagnosis of histologic fibrosis stage.
n=349 ≥F1 ≥F2 ≥F3 F4
SSI 0.89
(0.84-0.92)
0.89
(0.84-0.92)
0.92
(0.89-0.95)
0.92
(0.89-0.95)
Fibroscan 0.84
(0.77-0.89)
0.83
(0.78-0.87)
0.86
(0.81-0.89)
0.90
(0.86-0.93)
ARFI 0.81
(0.73-0.87)
0.81
(0.75-0.85)
0.85
(0.80-0.89)
0.84
(0.79-0.88)
Fibrotest 0.79
(0.71-0.85)
0.74
(0.68-0.79)
0.78
(0.73-0.83)
0.81
(0.75-0.85)
FIB-4 0.77
(0.70-0.83)
0.75
(0.70-0.80)
0.77
(0.72-0.82)
0.82
(0.76-0.86)

Challengers for measuring liver stiffness
Advantages & disavantages
•  Can be implemented on a
regular US machine
• High applicability
• Performance close to TE
•  Advantages
ARFI
• Further validation needed
• Narrow range of values
• Quality criteria not defined
•  Can be implemented on a
regular US machine
• High range of value (2-150 kPa)
• Performance higher than TE ?
•  Advantages
SWE
• Further validation needed
• Quality criteria?
• Limited data on reproducibility
Berzigotti & Castera. J Hepatol 2013; 59: 180-2

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