studies

608A AASLD ABSTRACTS HEPATOLOGY, October, 2015
800
Role of Acoustic Radiation Force Impulse (ARFI) elastography
in non-invasive assessment of fibrosis in active
autoimmune liver disease: A simultaneous biopsy-controlled
study
David I. Sherman 1 , Waleed Fateen 1 , Minal J Sangwaiya 2 , Paul
Tadrous 3 , Philip J. Shorvon 2 ; 1 Gastroenterology, Central MIddlesex
Hospital, London North West Healthcare NHS Trust, London,
United Kingdom; 2 Radiology, Central Middlesex Hospital, London
North West Healthcare NHS Trust, London, United Kingdom;
3 Cellular Pathology, Northwick Park Hospital, London North West
Healthcare NHS Trust, London, United Kingdom
Introduction The predictive accuracy of ARFI elastography (virtual
touch quantification, VTq) for the non-invasive assessment
of liver fibrosis is well validated in viral hepatitis. Whilst there
is evidence that inflammation can increase liver stiffness, the
role of ARFI in interpreting active autoimmune liver disease is
unclear. We report the results of a preliminary biopsy-controlled
study in a large autoimmune cohort, in which liver stiffness
(LS) and histology were sampled simultaneously from the same
region of liver tissue. Patients and Methods Our local database
of 101 patients with autoimmune liver disease (63 AICH +/-
overlap, 38 PBC or PSC) was interrogated. LS estimation by
ARFI was performed using a standard validated protocol by a
single operator. Biopsies were performed from the same region
of liver using an 18G Biopince needle, immediately after LS
measurement. Clinical, biochemical, ultrasonic and histopathological
data were collated retrospectively. Predictive accuracy
variables were determined for fibrosis stage using both standard
and local calibrations 1 . Results Sixty one ARFI + liver
biopsies performed at the same session were identified out of a
total of 164 ARFI examinations and 114 liver biopsies. Patients
included group 1: 34 active AICH (diagnosis pre-therapy or
flare on therapy); group 2: 7 AICH biochemical remission; and
group 3: 17 cholestatic liver disease. Validation confirmed
satisfactory ARFI quality: SD/mean > 0.3 in 4(6.9%), failure
in 3(4.9%). Co-pathology was seen in 8(13%), mostly NAFLD.
AUROC analysis showed overall predictive performance for
all groups/group 1/group 3 were 54.8/41.7/56.3, respectively;
for exclusion of ≥F2 69.5/44.9/81.8; for detection of
F3/4 fibrosis 70.1/55.9/79.8. Using the chosen cut off point,
sensitivity and NPV for F3/4 detection were 100% for both all
groups and group 3. No difference was seen between performance
of standard and local calibrations. Conclusion These
simultaneous paired data demonstrate a reduced predictive
performance for fibrosis stage using ARFI in active autoimmune
liver disease, probably due to the inflammatory process. However,
reasonable performance was demonstrated for both ≥F2
exclusion and confirmation of F3/4 in PBC/PSC. The finding
that ARFI can reliably exclude advanced fibrosis, particularly
in cholestatic liver disease, is of potential practical importance.
Further biopsy-controlled studies are needed to confirm the role
of ARFI in this patient group. Reference D. Sherman et al. J
Hepatol 2014;60(1):Suppl. p S413.
Disclosures:
The following authors have nothing to disclose: David I. Sherman, Waleed
Fateen, Minal J Sangwaiya, Paul Tadrous, Philip J. Shorvon
801
A clustering based fully automated method for collagen
proportional area extraction in liver biopsy images
Nikos Giannakeas 2 , Zoi Tsianou 2 , Markos Tsipouras 2 , Alexandros
T. Tzallas 3 , Pinelopi Manousou 1 , Epameinondas Tsianos 2 ;
1 Institute for Liver and Digestive Health, London, Royal Free Hospital
and UCL, London, United Kingdom; 2 1st Division of Internal
Medicine and Division of Gastroenterology, Faculty of Medicine,
School of Health Sciences, University of Ioannina, Ioannina,
Greece; 3 School of Applied Technology, Department of Computer
Engineering, Technological Educational Institute of Epirus, Arta,
Greece
Background and Aim: Collagen Proportional Area (CPA)
extraction using digital image analysis (DIA) in liver biopsies
provides an effective way to estimate liver disease staging.
CPA represents accurately fibrosis expansion in liver tissue
compared to semiquantitative staging scores. However, CPA
has not reached everyday clinical practice. The lack of standardized
and robust methods for computerized DIA for CPA
assessment is a major limitation. We aim to create a fully
automated methodology for CPA computation in liver biopsy
images. Method: The proposed methodology is based in
three stages. 1)The tissue detection stage: a k–means clustering
approach is used to separate the tissue from the background.
2)The tissue characterization stage: machine learning
techniques are employed to characterize liver tissue, muscle,
vessels, blood clots, structural collagen, dye stain and artifacts
(scratches or contaminations of the substrate). Several shape
and color features are extracted from each tissue area. This set
of features is used for training and testing a classification algorithm,
for automated area classification. 3) The CPA calculation
stage: this stage also employs the k-means clustering, to separate
fibrosis areas from normal liver tissue. CPA is computed as
the number of fibrosis pixels divided by the number of pixels
of the whole liver tissue. Results: The proposed methodology
was evaluated using a dataset of 93 images of liver biopsies,
obtained from 93 hepatitis C patients. The dataset was kindly
provided by the Royal Free Hospital, London and our results
were compared with the standardized method. Liver biopsies
were formalin fixed, paraffin embedded and stained using
Picrosirius red. High accuracy results were obtained for all
stages of the proposed methodology (1 st stage: 98% accuracy
in tissue detection, 2 nd stage: 85% accuracy in tissue area
characterization, and 3 rd stage: less than 2% mean error). Conclusions:
According to the results, CPA is extracted accurately
in most of the cases (errors