NON-INVASIVE DEVICE FOR DETECTING LIVER DAMAGE

Description

TECHNICAL FIELD

[0001] The invention relates to a non-invasive device for detecting liver lesions using ultrasound and shear waves. This device can be used in humans and animals and is intended, for example, for detecting liver lesions such as alcoholic steatohepatitis (ASH) or non-alcoholic steatohepatitis (NASH). The invention allows for the calculation of a score reflecting the degree of liver damage. EARLIER ART

[0002] Chronic liver diseases typically lead to liver damage such as fibrosis. Fibrosis is a fibrous scarring process in liver tissue resulting from inflammation. Initially asymptomatic fibrosis can progress to cirrhosis. Liver tissue elasticity is a marker of liver fibrosis. Transient elastography, as described, for example, in patent application number FR 2843290, is known to be used to measure and quantify liver tissue elasticity.

[0003] This document presents an implementation of a prior art device. This device consists of a probe equipped with a vibration generator that produces a low-frequency elastic wave in a tissue, for example, by vibration, and analyzes the propagation of this low-frequency elastic wave using high-frequency ultrasonic waves emitted and received by an ultrasonic transducer. The measurements obtained via this device allow the elasticity of liver tissue to be quantified. This device also allows the ultrasonic attenuation of tissues to be quantified, as described, for example, in patent application number FR 2949965. The quantification of ultrasonic attenuation in the liver is correlated with the amount of steatosis.

[0004] In contrast, in humans, certain diseases, such as NASH, are not necessarily linked solely to the amount of fibrosis or steatosis, and can, for example, involve both steatosis (fatty liver) and inflammation, with or without fibrosis. Therefore, the stage of NASH or its progression to NASH cannot be diagnosed using a single parameter.

[0005] Other examples of prior art are provided by WO 2015 / 014763 A2; MIETTE V. ET AL. : “P1C-2 Liver Stiffness Measurement Using Transient Elastography in Patients with Non-Alcoholic Fatty Liver (NAFLD) and Non-Alcoholic Steatohepatitis (NASH)”, IEEE ULTRASONICS SYMPOSIUM, October 1, 2007, pages 1333-1336; WO 2013 / 025798 A1; SASSO M. ET AL. : “Controlled Attenuation Parameter (CAP): A Novel VCTE™ Guided Ultrasonic Attenuation Measurement for the Evaluation of Hepatic Steatosis: Preliminary Study and Validation in a Cohort of Patients with Chronic Liver Disease from Various Causes”, ULTRASOUND IN MEDICINE AND BIOLOGY, NEW YORK, NY, US, vol. 36, no. 11, November 1, 2010, pages 1825-1835. DESCRIPTION OF THE INVENTION

[0006] The present invention aims to resolve at least one of the aforementioned drawbacks of the prior art. To this end, the invention provides a device according to claim 1.

[0007] Furthermore, the present description relates to a device for calculating a score for humans or animals, said score being a quantitative or semi-quantitative assessment of a liver lesion of the steatohepatitis type of alcoholic or non-alcoholic origin, said calculation device being constructed and arranged to calculate a score using at least the following physical and possibly biological parameters: A parameter correlated with inflammation and / or fibrosis, A parameter correlated with steatosis.

[0008] This method offers the advantage of enabling the early detection of certain types of liver damage, such as NASH, which can be correlated with inflammation, fibrosis, and steatosis. In contrast, NAFLD (Non-Alcoholic Fatty Liver Disease) is correlated only with steatosis. Therefore, the score allows for differentiation between patients with NAFLD and those with NASH.

[0009] The score can be a quantitative or semi-quantitative assessment (e.g., binary indicator) of steatohepatitis-type liver damage of alcoholic or non-alcoholic origin.

[0010] The computing device can be embedded in: an ultrasound scanner, or a device constructed and arranged to measure at least liver stiffness.

[0011] The device can be constructed and arranged to deliver the score concurrently with the measured physical parameters. In other words, the ultrasound scanner or the device constructed and arranged to measure at least liver stiffness measures physical parameters, and the device according to the invention calculates the score taking into account at least the measured physical parameters.

[0012] The computing device can be built and arranged to communicate with: a remote ultrasound scanner, or a remote device constructed and arranged to measure at least liver stiffness.

[0013] According to the invention, the parameter correlated with fibrosis is liver elasticity.

[0014] According to the invention, the parameter correlated with steatosis can be a measure of the attenuation of ultrasound waves, for example the parameter called CAP as described in the article Sasso, M., et al. (2010). "Controlled attenuation parameter (CAP): a novel VCTE guided ultrasonic attenuation measurement for the evaluation of hepatic steatosis: preliminary study and validation in a cohort of patients with chronic liver disease from various causes." Ultrasound Med Biol 36(11): 1825-1835.

[0015] Alternatively, according to the invention, the parameter correlated with steatosis can be a measure of liver tissue viscosity.

[0016] The calculation device can be constructed and arranged to calculate a score using at least one additional parameter correlated with metabolic syndrome.

[0017] The calculation device can be constructed and arranged to calculate a score using at least one additional anthropomorphic parameter.

[0018] The calculation device can be constructed and arranged to calculate a score using at least one additional biological parameter. The at least one biological parameter can, for example, be chosen from the following: transaminases (AST, ALT), GGT, ALP, serum iron, ferritin, transferrin saturation, adipokine (e.g., adiponectin, leptin, resistin), cytokine (e.g., TNFα, IL6, IL1-β), HDL cholesterol, blood glucose, insulin, bilirubin, α2-macroglobulin, haptoglobin, apolipoprotein A1, CK18, triglycerides, adiponectin, urea, genetic polymorphism (e.g., PNPLA3 polymorphism, TM6SF2), CRP and / or leptin.

[0019] The calculation device can be constructed and configured to communicate with a score display device. The score can be displayed as a numerical value, a binary indicator, a probability, or a risk. This embodiment offers the particular advantage of simplifying the interpretation of the score analysis, which reflects the state of a calculated liver function.

[0020] This description also includes a score that takes into account at least the following physical and possibly biological parameters: A parameter correlated with inflammation and / or fibrosis, and a parameter correlated with steatosis.

[0021] The score can take into account at least one parameter of inflammatory activity. This at least one parameter of inflammatory activity can be chosen from the following: transaminase levels, liver stiffness, or liver viscosity.

[0022] The score can take into account at least one anthropomorphic parameter such as weight, height, waist circumference, hip circumference, chest circumference or a demographic parameter such as age and sex.

[0023] The score can take into account at least one biological parameter.

[0024] At least one biological parameter may be chosen from the following parameters: transaminases (AST, ALT), GGT, ALP, serum iron, ferritin, transferrin saturation, adipokine (e.g., adiponectin, leptin, resistin), cytokine (e.g., TNFα, IL6, IL1-β), cholesterol, HDL cholesterol, blood glucose, insulinemia, bilirubin, α2macroglobulin, haptoglobin, apolipoprotein A1, CK18, triglycerides, adiponectin, urea, genetic polymorphism (e.g., PNPLA3 polymorphism, TM6SF2), CRP and / or leptin.

[0025] The biological parameter may be a metabolomic parameter.

[0026] The score is calculated using statistical modeling (also known as statistical learning) such as logistic regression, decision trees, Bayesian classifier, random forests, SVM, neural network, discriminant analysis, etc. BRIEF DESCRIPTION OF THE FIGURES

[0027] Other features and advantages of the invention will become clear from the description given below, which is by way of example and not limitation, with reference to: to the figure 1 illustrating, schematically, a first example of the realization of a device for calculating a score reflecting the state of a liver lesion, integrated into a device constructed and arranged to measure liver elasticity, at the figure 2 illustrating, schematically, a second example of the realization of a device for calculating a score reflecting the state of a liver lesion, constructed and arranged to communicate with a remote ultrasound scanner. DESCRIPTION OF THE INVENTION

[0028] There figure 1 represents a device 100 for calculating a score reflecting the state of a liver lesion integrated into a device 200 constructed and arranged to measure liver elasticity.

[0029] In this non-limiting embodiment, the device 200 comprises an elastography probe 201 equipped with an ultrasound transducer 202 constructed and arranged to emit and receive ultrasound waves. In this embodiment, the elastography probe 201 further comprises means for generating a shear wave in the liver tissue. These means may be an electrodynamic actuator 203 constructed and arranged to generate a low-frequency wave. The device 200 is therefore constructed and arranged to measure physical parameters, for example, parameters that are correlated with inflammation and / or fibrosis and parameters that are correlated with steatosis.

[0030] For example, one parameter related to fibrosis is liver elasticity. This elasticity measurement serves as a marker of the pathological state of liver tissue.

[0031] The parameter correlated with steatosis can be a measure of the attenuation of ultrasound waves in liver tissue. Hepatic steatosis is an accumulation of fat in the liver. Measuring the attenuation of ultrasound wave propagation therefore allows for the quantification of steatosis.

[0032] Device 100, which calculates a score reflecting the state of liver injury, is designed and configured to calculate a score using a parameter correlated with liver tissue inflammation and / or a parameter correlated with fibrosis. In the example described, these parameters are measured using elastography probe 201.

[0033] In the illustrated example, device 200 also includes a human-machine interface 204 built and arranged to input marker parameters of metabolic syndrome used for calculating the score.

[0034] Thus, an operator can enter marker parameters for metabolic syndrome via the human-machine interface 204. Metabolic syndrome is defined as a cluster of health problems sharing a common underlying metabolic disorder; it is a group of risk factors more or less linked by a common origin, metabolic targets, or mechanisms. This group of parameters may include: HDL cholesterol, triglycerides, blood glucose, blood pressure, and / or waist circumference.

[0035] This human-machine interface 204 is also built and arranged to input biological parameters used for scoring. These biological parameters can be: transaminases (ALT, AST), GGT, ALP, serum iron, cholesterol, HDL cholesterol, blood glucose, insulin, bilirubin, α2-macroglobulin, haptoglobin, apolipoprotein A1, CK18, triglycerides, adiponectin, and / or leptin.

[0036] This human-machine interface 204 is also built and arranged to input anthropomorphic and demographic parameters used for scoring. These demographic and anthropomorphic parameters include, for example, a person's age, sex, height, weight, waist circumference, hip circumference, or chest circumference.

[0037] Based on these different parameters, the computing device 100 calculates a score using logistic regression or any other scoring method, for example decision trees, Bayesian classifier, random forests, large margin separator decision trees (SVM), or neural network.

[0038] To this end, the computing device 100 can be formed by one or more microprocessors built and adapted to execute sequences of instructions allowing an implementation of the aforementioned logistic regression or any other scoring method.

[0039] In the illustrated example, the calculated score is represented as a binary indicator 205 equal to 1 and displayed on a screen 206 of the device 200. This binary indicator 205 can be used to recommend that a patient consult a specialist. For example, when the indicator equals 1, the patient is diagnosed as being at risk and requires further investigation or additional tests.

[0040] Conversely, when the indicator is equal to 0, the patient should not consult a specialist. This indicator can also be different; it can be implemented as a value.

[0041] In this non-limiting embodiment, the measurement of physical parameters, the input of other parameters, the calculation of the score and the display of the score are carried out on the device 200. Thus, this embodiment has the particular advantage of calculating the score in real time (in other words on the place where the measurements of the physical parameters are carried out), and then displaying the score, thus allowing for rapid analysis.

[0042] In different, non-limiting examples, device 200 may be formed by an ultrasound scanner, an MRI scanner, or an MRI scanner implementing magnetic resonance elastography (MRE).

[0043] In a non-exhaustive implementation illustrated on the figure 2The device for calculating a score reflecting the state of a liver lesion 100 is built and configured to communicate with a remote ultrasound scanner 300. In other words, the calculation device 100 is located remotely from the ultrasound scanner 300. Thus, measurements are taken on the ultrasound scanner 300 and then transmitted via a network connection 140, for example, an Ethernet, Bluetooth, or Wi-Fi connection, to the calculation device 100. It is also possible to transmit other parameters, for example, anthropomorphic or demographic data, to the calculation device 100 via a computer 400. Similarly, this computer 400 can communicate with the calculation device 100 via an Ethernet or Wi-Fi connection 150. The calculation device 100 can be implemented using one or more processors. Alternatively, the computer can be integrated into the ultrasound scanner 300.

[0044] In this non-limiting implementation, the score display can be performed on the 300 ultrasound scanner, on the 400 computer, or both.

Claims

1. A device (100) for calculating a score (205) for human or animal, said score (205) being a quantitative or semi-quantitative evaluation of a liver injury of alcoholic or non-alcoholic steatohepatitis type, the score being calculated by means of statistical modelling, the score being a function of the following physical and biological parameters: - a parameter correlated to fibrosis; - a parameter correlated to steatosis; and - a parameter correlated to inflammatory activity; wherein the parameter correlated to fibrosis is liver elasticity; wherein the parameter correlated to steatosis is an ultrasound wave attenuation measurement or a liver viscosity measurement; wherein the parameter correlated to inflammatory activity is one of: ALAT, ASAT and GGT.

2. The calculation device (100) according to claim 1, wherein the statistical modelling is logistic regression, a decision tree, a Bayesian classifier or a neural network.

3. The calculation device (100) according to one of the preceding claims, characterised in that it is embedded in: - an ultrasound scanner, or - a device constructed and arranged to measure at least liver elasticity.

4. The calculation device (100) according to one of claims 1 and 2, characterised in that it is constructed and arranged to communicate with: - an ultrasound scanner, or - a device constructed and arranged to measure at least liver elasticity.

5. The calculation device (100) according to any of the preceding claims, characterised in that it is constructed and arranged to calculate a score by means of at least one additional parameter correlated to the metabolic syndrome.

6. The calculation device (100) according to any of the preceding claims, characterised in that it is constructed and arranged to calculate a score by means of at least one additional parameter of anthropomorphic type.

7. The calculation device (100) according to any of the preceding claims, characterised in that it is constructed and arranged to calculate a score by means of at least one additional parameter of biological type.

8. The calculation device (100) according to any of the preceding claims, characterised in that it is constructed and arranged to communicate with a device (206) for displaying the score (205).

9. The calculation device (100) according to the preceding claim, characterised in that the score (205) is displayed as a numerical value, a binary indicator, a probability or a risk.