Clinical Trial Details
— Status: Completed
Administrative data
| NCT number |
NCT04623528 |
| Other study ID # |
S64242 |
| Secondary ID |
|
| Status |
Completed |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
January 2014 |
| Est. completion date |
October 2020 |
Study information
| Verified date |
November 2020 |
| Source |
Universitaire Ziekenhuizen Leuven |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
- To determine normal T1 and T2 values of the liver, and to assess the impact of age and
gender
- To determine the relation between markers of right heart decompensation and T1/T2 values
of the liver in patients with pulmonary hypertension, patients with dilated
cardiomyopathy, and patients with constrictive pericarditis (or constrictive physiology)
- To determine inter/intra-observer reproducibility for liver T1/T2 assessment
- To test/develop multi-feature texture analysis for T1/T2 analysis of the liver and
implement machine learning to derive indicative features (MR-derived measures only vs
combined with other clinical readouts)
Description:
Although liver biopsy is the current standard for histological characterization of the liver
parenchyma, this invasive procedure has a significant risk of - potentially lethal -bleeding.
Moreover, as liver disease may focally or heterogeneously affect the liver, histological
findings may be false negative or not be representative. Non-invasive imaging modalities
(e.g. ultrasound (US), computed tomography (CT) and magnetic resonance imaging (MRI), on the
other hand, provide valuable information with regard to global and regional tissue
characterization, and may be of help in guiding/targeting liver biopsy. Moreover, in recent
years new developments have opened the door towards a better, more comprehensive appreciation
of the status of the liver. One of these is elastography assessing the mechanical,
viscoelastic properties of the liver, thus providing valuable information with regard to the
liver stiffness (or elasticity), and be used for accurate staging of liver fibrosis (1,2).
Another approach is tissue characterization using the relaxation behaviour of liver tissue in
a high-field magnetic environment ('relaxometry'). As relaxation times are tissue specific,
focal or diffuse pathology may alter (i.e. shorten or prolong) relaxation times. These
alterations can be used to depict and to quantify disease severity. In the field of cardiac
imaging relaxometry has caused a paradigm shift, but for liver imaging this has received
relatively limited interest so far. Best known is T2* relaxometry allowing to diagnose and
follow-up patients with haemochromatosis. Recently, a few papers have described the use of T1
and T1-rho relaxometry to diagnosis liver congestion in Fontan patients, and to depict liver
fibrosis, respectively (3,4).
As part of a comprehensive cardiac MRI exam, we started performing routinely T1 and T2
relaxometry of the heart in 2014. The technique was validated against a standardized,
commercially-available phantom in an inter-national study (5). Practically, T1 and T2 mapping
includes measurements in both cardiac short-axis and horizontal long-axis before and after
the administration of intravenous commercially available gadolinium chelates. As in cardiac
short-axis direction the liver is partially encompassed, we will use these images to measure
T1 and T2 relaxation times of the liver. As all data are stored on PACS, it is our aim to
re-use these T1 and T2 mapping studies to determine the T1 and T2 relaxation of the liver in
normal conditions.
Firstly, we will derive from our cardiac MRI database a representative group of studies
labelled as normal (i.e. normal cardiac MRI findings). In the medical patient file the serum
biomarkers for liver disease will be checked (see below), as well as findings at
echocardiography (exclusion of right heart failure, exclusion of severe tricuspid
insufficiency), abdominal ultrasound / computed tomography (exclusion of liver disease, i.e.
hemochromatosis, steatosis, hepatic congestion, and liver cirrhosis). Only MRI studies will
be included if liver disease is excluded. In a next step, a representative region of interest
will be manually drawn (> 100 pixels) in a region not including the liver vessels. These
analyses will be performed on the pre- and post-contrast T1 map, and on the T2 map. The goal
is to determine normal values in at least 100 subjects, allowing to assess the impact of
aging and gender.
Secondly, we will measure liver T1 and T2 values in patients with different forms of right
heart failure, and assess the relation between liver T1 and T2 values and findings at
echocardiography, serum biomarkers, and right heart catheterization indicative of right heart
failure. The latter information will be retrieved from the medical patient file. As in right
heart failure, the filling pressure increases in the caval and hepatic veins, hereby causing
hepatic congestion. It is the hypothesis that in these circumstances T1 and T2 liver values
are increased. Also, in latter stages when liver fibrosis initiates and evolves towards
cardiac cirrhotic liver, we hypothesize increased T1 values. The aim is to evaluate whether
mean T1 and/or T2 of the liver parenchyma obtained at MRI can be used as imaging biomarker of
right heart decompensation. As concomitant liver disease (see above) may hamper correct
interpretation of our findings, pre-existing liver disease needs to be excluded. For this, a
similar approach as in the normal population will be used. The target patient population is
three-fold. First, patients with pulmonary hypertension with/without evidence of right heart
failure, who underwent a right heart catheterization with invasive pressure measurements (if
not available, pulmonary artery pressure estimations at transthoracic echocardiography will
be used), and signs of right heart failure at transthoracic echocardiography can be used.
Second, patients with dilated cardiomyopathy, defined as LV ejection fraction < 35% with and
without concomitant right ventricular (RV) dysfunction (i.e. RV EF < 35%). Third, patients
with constrictive pericarditis or inflammatory pericarditis with constrictive physiology
(i.e. increased respiratory-related ventricular coupling/interdependence). This group will be
compared to a group of patients with inflammatory pericarditis without constrictive
physiology (i.e., preserved ventricular coupling).
Thirdly, to assess the inter- and intra-observed reproducibility of liver T1 / T2 values,
measurements will be performed by two readers independently performing the measurements. One
reader will repeat the measurements respecting one week interval between the analyses. This
group involves 10 randomly selected studies in the normal group and 10 studies in the patient
group. Analysis include assessment of the intraclass correlation coefficient (ICC) and
coefficient of variation (CoV).
Finally, as step towards a more automated approach, we will evaluate/develop texture
analysis. This mathematical approach looks at patterns between pixels not visible by the
human eye and results in image 'features' that go beyond the typical mean (or median) and
deviations. It has shown to be a robust technique in many applications in the field of
medical imaging, and most likely will be useful for liver imaging as well. The large group of
derived image features needs to be further analysed using machine learning approaches
(with/without other clinical readouts). Machine learning of features coupled to a diagnosis
('target') has the potential to augment traditional risk scores with novel imaging
biomarkers. Regularization approaches based on e.g. support vector machines, random forests
or convolutional neural networks (CNNs) will be implemented.
