Patent Publication Number: US-2018052213-A1

Title: Computerized optical analysis methods of mr (magnetic resonance) images for quantifying or determining liver lesions

Description:
FIELD OF THE INVENTION 
     This invention relates to the field of hepatic diagnosis and more precisely to computerized optical analysis methods of conventional, preferably non-contrast-enhanced, MR (magnetic resonance) images for quantifying or determining liver lesions, especially due or related to liver impairment, liver steatosis, non-alcoholic fatty liver disease (NAFLD), or non-alcoholic steatohepatitis (NASH). 
     BACKGROUND OF THE INVENTION 
     FLD (Fatty Liver Disease) describes a wide range of potentially reversible conditions involving the liver, wherein large vacuoles of triglyceride fat accumulate in hepatocytes via the process of steatosis (i.e. the abnormal retention of lipids within a cell). FLD is commonly associated with alcohol or metabolic syndromes (diabetes, hypertension, dyslipidemia, abetalipoproteinemia, glycogen storage diseases, Weber-Christian disease, Wolman disease, acute fatty liver of pregnancy, lipodystrophy). However, it can also be due to nutritional causes (malnutrition, total parenteral nutrition, severe weight loss, refeeding syndrome, jejuno-ileal bypass, gastric bypass, jejunal diverticulosis with bacterial overgrowth), as well as various drugs and toxins (amiodarone, methotrexate, diltiazem, highly active antiretroviral therapy, glucocorticoids, tamoxifen, environmental hepatotoxins) and other diseases such as inflammatory bowel disease or HIV. 
     Whether it is AFLD (Alcoholic Fatty Liver Disease) or NAFLD (Non-Alcoholic Fatty Liver Disease), FLD encompasses a morphological spectrum consisting from the mildest type “liver steatosis” (fatty liver), called NAFL, to the potentially more serious type “steatohepatitis”, called NASH, which is associated with liver-damaging inflammation and, sometimes, the formation of fibrous tissue. In fact, steatohepatitis has the inherent propensity to progress towards the development of fibrosis then cirrhosis which can produce progressive, irreversible liver scarring or towards hepatocellular carcinoma (liver cancer). 
     Because these diseases can be potentially reversed if diagnosed early enough, or at least their consequences limited, it is crucial to be able to provide the medical field with tools that permitting such an early, rapid and precise diagnosis. 
     For a long time, the diagnosis of liver steatosis has usually been accomplished by measuring markers such as g-glutamyl-transpeptidase (GGT) and alanine aminotransferase (ALT) while at the same time performing a liver biopsy in order to confirm FLD and determine the grading and staging of the disease. Although biopsies can provide important information regarding the degree of liver damage, in particular the severity of necro-inflammatory activity, fibrosis and steatosis, the procedure also presents several limitations, such as sampling error, invasiveness, cost, pain for patients which in turn brings forth a certain reluctance to undergo such a procedure; and finally complications may arise from such procedure, which in some cases can even lead to mortality. Ultrasonography is also used to diagnose liver steatosis. However, this method is subjective as it is based on echo intensity (echogenicity) and special patterns of echoes (texture). As a result, it is not sensitive enough and often inaccurate in patients with advanced fibrosis. Finally, it is generally admitted that around half of all FLD cases are detected by usual blood tests and around half by ultrasonography, resulting in around one quarter of missed diagnosis when both are used. 
     In recent years, the use of non-invasive biomarkers has gained importance in the field of hepatic diagnosis. Indeed, several tests using non-invasive biomarkers have already been developed and proposed for the diagnosis of fibrosis (see for example WO 2005/116901). The test relates to a method of diagnosing the presence and/or severity of a hepatic pathology and/or of monitoring the effectiveness of a curative treatment against a hepatic pathology in an individual, by establishing at least one non-invasive diagnostic score, in particular a diagnostic score for portal and septal fibrosis and/or an estimate for amount of fibrosis (the area of fibrosis) and/or an estimate for the architecture of fibrosis (fractal dimension). In addition, Poynard et al. (WO 2006/082522) has demonstrated that a single or a panel of biomarkers can be used as an alternative to liver biopsy for the diagnosis of steatosis, whether induced by alcohol, viral hepatitis or NAFLD, the most common causes of steatosis. In particular, this document provides for a new panel of biomarkers known as a SteatoTest (ST) with predictive values for the diagnosis of steatosis due to alcohol, NAFLD and hepatitis C and B. Serum GGT and ALT were considered as the standard biochemical markers. However, according to the French National Agency for Health (HAS), updated in December 2008, and current international opinion, the performance of this kind of test may be insufficient, especially due to the reference based on a subjective grading of liver steatosis with a poor inter-observer reproducibility. In addition, this grading is a semi-quantitative variable which implies an imprecise and limited reflect of the original pattern. 
     The Applicant has now observed that computerized optical analysis methods of conventional non-contrast-enhanced MR (magnetic resonance) images of the liver enable the detection of steatohepatitis by NASH-MRI and significant fibrosis by Fibro-MRI in patients suffering from NAFLD. These methods circumvent the above-mentioned limitations and are more precise and reliable than the methods cited in the prior art, as shown by their high diagnostic performance. The methods of the invention considerably reduce the need of biopsies, as they catch more information than in the prior art about the lesions evaluated, they ensure reproducibility and performance, while attenuating causes of false results (generally, sources of false results are antagonized in a score including several markers provided that some precautions are included). 
     BRIEF DESCRIPTION OF THE INVENTION 
     The present invention provides computerized optical analysis methods of conventional non-contrast-enhanced MR (magnetic resonance) images of the liver, said methods enable the detection of steatohepatitis by NASH-MRI and significant fibrosis by Fibro-MRI in patients suffering from NAFLD. By using the methods of the present invention it is possible to forecast the rate of disease progression, to support therapeutic decision-making, and to monitor potential therapeutic effects. 
     In particular, a first aspect of the invention refers to an non-invasive method for quantifying or determining the lesions of the liver of a subject suspected of suffering from a non-alcoholic fatty liver disease addressing as diagnostic target fibrosis, steatohepatitis and/or NASH (Non-alcoholic steatohepatitis), preferably for screening for subjects at risk of developing or having fibrosis, steatohepatitis and/or NASH, comprising:
         a. Performing or obtaining, preferably using 1.5-Tesla whole-body systems, a T2-weighted single shot fast spin echo magnetic resonance imaging (SSFSE-T2 MRI) and/or a Dynamic contrast-enhanced magnetic resonance imaging (DYNAMIC MRI) and/or fast spin-echo short inversion time inversion-recovery (STIR) magnetic resonance imaging (FAST-STIR MRI), on whole or part of the liver of the subject,   b. measuring at least one biomarker selected from the group consisting of: harmonic mean (E3) or Person&#39;s asymmetry coefficient (E22) from the SSFSE-T2 MRI or harmonic mean (E3), mode (E6), column&#39;s mean of multi-oriented co-ocurrence matrix (E31), maximum of main curvatures (E75) or order contrast (E57) from the DYNAMIC MRI or weighted mean curvature (E73) from the FAST-STIR MRI; and   c. using said measurement/s in a mathematical function useful as a predictive equation to obtain the probability of having any of the diagnostic targets.       

     Preferably, the diagnostic target of the non-invasive method of the first aspect of the invention is steatohepatitis and the method comprises:
         a. performing a T2-weighted single shot fast spin echo magnetic resonance imaging (SSFSE-T2 MRI) and/or a Dynamic contrast-enhanced magnetic resonance imaging (DYNAMIC MRI) and/or fast spin-echo short inversion time inversion-recovery (STIR) magnetic resonance imaging (FAST-STIR MRI), on whole or part of the liver of the subject,   b. measuring at least one biomarker selected from the group consisting of: harmonic mean (E3) from the SSFSE-T2 MRI or order contrast (E57) from the DYMANIC MRI or weighted mean curvature (E73) from the FAST-STIR MRI; and   c. using said measurement/s in a mathematical function useful as a predictive equation to obtain the probability of having steatohepatitis.       

     More preferably, the diagnostic target of the non-invasive method of the first aspect of the invention is steatohepatitis and the method comprises:
         a. performing a T2-weighted single shot fast spin echo magnetic resonance imaging (SSFSE-T2 MRI) on whole or part of the liver of the subject,   b. measuring the harmonic mean (E3) from the SSFSE-T2 MRI; and   c. using said measurement in a mathematical function useful as a predictive equation to obtain the probability of having steatohepatitis.       

     More preferably, the diagnostic target of the non-invasive method of the first aspect of the invention is steatohepatitis and the method comprises:
         a. performing a Dynamic contrast-enhanced magnetic resonance imaging (DYNAMIC MRI) on whole or part of the liver of the subject,   b. measuring the order contrast (E57) from the DYNAMIC MRI; and   c. using said measurement in a mathematical function useful as a predictive equation to obtain the probability of having steatohepatitis.       

     More preferably, the diagnostic target of the non-invasive method of the first aspect of the invention is steatohepatitis and the method comprises:
         a. performing a fast spin-echo short inversion time inversion-recovery (STIR) magnetic resonance imaging (FAST-STIR MRI) on whole or part of the liver of the subject,   b. measuring the weighted mean curvature (E73) from the FAST-STIR MRI; and   c. using said measurement in a mathematical function useful as a predictive equation to obtain the probability of having steatohepatitis.       

     Still more preferably, the diagnostic target of the non-invasive method of the first aspect of the invention is steatohepatitis and the method comprises:
         a. performing a T2-weighted single shot fast spin echo magnetic resonance imaging (SSFSE-T2 MRI) and a Dynamic contrast-enhanced magnetic resonance imaging (DYNAMIC MRI) and a fast spin-echo short inversion time inversion-recovery (STIR MRI) magnetic resonance imaging (FAST-STIR MRI), on whole or part of the liver of the subject,   b. measuring the biomarkers selected from the group consisting of: harmonic mean (E3) from the SSFSE-T2 MRI, order contrast (E57) from the DYMANIC MRI and weighted mean curvature (E73) from the FAST-STIR MRI; and   c. using said measurements in a mathematical function useful as a predictive equation to obtain the probability of having steatohepatitis, wherein said mathematical function useful as a predictive equation is preferably as follows:       

       NASH-MRI=1/(1 +e   (1.654−0.079*E3   _   T2−0.127*E57   _   DYN*E73   _   FAST) ) 
     Preferably, the diagnostic target of the non-invasive method of the first aspect of the invention is fibrosis and the method comprises:
         a. performing a T2-weighted single shot fast spin echo magnetic resonance imaging (SSFSE-T2 MRI) and/or a Dynamic contrast-enhanced magnetic resonance imaging (DYNAMIC MRI), on whole or part of the liver of the subject,   b. measuring at least one biomarker selected from the group consisting of: Person&#39;s asymmetry coefficient (E22) from the SSFSE-T2 MRI or harmonic mean (E3), mode (E6), column&#39;s mean of multi-oriented co-occurrence matrix (E31) or maximum of main curvatures (E75) from the DYMANIC MRI; and   c. using said measurement/s in a mathematical function useful as a predictive equation to obtain the probability of having any of the diagnostic targets.       

     More preferably, the diagnostic target of the non-invasive method of the first aspect of the invention is fibrosis and the method comprises:
         a. performing a T2-weighted single shot fast spin echo magnetic resonance imaging (SSFSE-T2 MRI) on whole or part of the liver of the subject,   b. measuring the Person&#39;s asymmetry coefficient (E22) from the SSFSE-T2 MRI; and   c. using said measurement in a mathematical function useful as a predictive equation to obtain the probability of having fibrosis.       

     More preferably, the diagnostic target of the non-invasive method of the first aspect of the invention is fibrosis and the method comprises:
         a. performing a Dynamic contrast-enhanced magnetic resonance imaging (DYNAMIC MRI) on whole or part of the liver of the subject,   b. measuring the following biomarkers selected from the group consisting of: harmonic mean (E3), mode (E6), column&#39;s mean of multi-oriented co-occurrence matrix (E31) and maximum of main curvatures (E75) from the DYMANIC MRI; and   c. using said measurements in a mathematical function useful as a predictive equation to obtain the probability of having fibrosis.       

     More preferably, the diagnostic target of the non-invasive method of the first aspect of the invention is fibrosis and the method comprises:
         a. performing a Dynamic contrast-enhanced magnetic resonance imaging (DYNAMIC MRI) on whole or part of the liver of the subject,   b. measuring the harmonic mean (E3) from the DYNAMIC MRI; and   c. using said measurement in a mathematical function useful as a predictive equation to obtain the probability of having fibrosis.       

     More preferably, the diagnostic target of the non-invasive method of the first aspect of the invention is fibrosis and the method comprises:
         a. performing a Dynamic contrast-enhanced magnetic resonance imaging (DYNAMIC MRI) on whole or part of the liver of the subject,   b. measuring the mode (E6) from the DYNAMIC MRI; and   c. using said measurement in a mathematical function useful as a predictive equation to obtain the probability of having fibrosis.       

     More preferably, the diagnostic target of the non-invasive method of the first aspect of the invention is fibrosis and the method comprises:
         a. performing a Dynamic contrast-enhanced magnetic resonance imaging (DYNAMIC MRI) on whole or part of the liver of the subject,   b. measuring the column&#39;s mean of multi-oriented co-occurrence matrix (E31) from the DYMANIC MRI; and   c. using said measurement in a mathematical function useful as a predictive equation to obtain the probability of having fibrosis.       

     More preferably, the diagnostic target of the non-invasive method of the first aspect of the invention is fibrosis and the method comprises:
         a. performing a Dynamic contrast-enhanced magnetic resonance imaging (DYNAMIC MRI) on whole or part of the liver of the subject,   b. measuring the maximum of main curvatures (E75) from the DYNAMIC MRI; and   c. using said measurement in a mathematical function useful as a predictive equation to obtain the probability of having fibrosis.       

     Still more preferably, the diagnostic target of the non-invasive method of the first aspect of the invention is fibrosis and the method comprises:
         a. performing a T2-weighted single shot fast spin echo magnetic resonance imaging (SSFSE-T2 MRI) and a Dynamic contrast-enhanced magnetic resonance imaging (DYNAMIC MRI), on whole or part of the liver of the subject,   b. measuring the following biomarkers selected from the group consisting of: Person&#39;s asymmetry coefficient (E22) from the SSFSE-T2 MRI and harmonic mean (E3), mode (E6), column&#39;s mean of multi-oriented co-occurrence matrix (E31) and maximum of main curvatures (E75) from the DYNAMIC MRI; and   c. using said measurements in a mathematical function useful as a predictive equation to obtain the probability of having any of the diagnostic targets, wherein preferably said mathematical function useful as a predictive equation is as follows:       

       Fibro−MRI=1/(1 +e   (−4.207−1.101*E3   _   DYN+1.105*E6   _   DYN+115.737″E22   _   T2−0.696*E31   _   DYN+0.825*E75   _   DYN) ).
 
     A preferred embodiment of the preferred embodiment of the invention implies the use of an additional step for confirming the presence of fibrosis, steatohepatitis and/or NASH by means of the examination of the liver by any means. It is further noted that the method of the first aspect of the invention can be used for classifying human subjects as healthy subjects or as subjects having fibrosis, steatohepatitis and/or NASH, and for monitoring the response to a therapy or for monitoring the progression of fibrosis, steatohepatitis and/or NASH. 
     In addition, it is also further noted that the method of the invention can additionally comprise a further method of treatment step for treating those patients suffering form or having probabilities of suffering from any of the diagnostic targets. Such treatment therapy, in particular for NASH, can be selected from, but is not limited to, any of the following: OCA, elefibranor, aramchol, simtuzumab, IMM-124E, cernicriviroc, emricasan, GS-4997+simtuzumab, Px-104, BMS-986036, SHP626, ZGN839, GR-MD-02, NDI-010976, GLP-01 semaglutide, NGM282, MSDC-0602, VK2809, MN-001, GS4998, RG-125, DUR-928, CER-209, solitromycin and PXS-4728A. 
     A further preferred embodiment of the first aspect of the invention refers to a computerized optical analysis method of the conventional MR (magnetic resonance) images of the liver obtained in step a), which comprises the means of analysis or measurement according to steps b) and/or c) of the first aspect of the invention in order to obtain the probability of suffering from the diagnostic target. 
     A second aspect of the invention refers to a computer program for implementing the computerized optical analysis method of the precedent paragraph. 
     A third aspect of the invention refers to the device comprising the computer program of the second aspect of the invention. 
     The present invention thus offers a clear solution to the problems cited above because it is focused on an in vitro method for identifying or screening human subjects at risk of suffering from fibrosis, steatohepatitis and/or NASH, departing from performing a T2-weighted single shot fast spin echo magnetic resonance imaging (SSFSE-T2 MRI) and/or a Dynamic contrast-enhanced magnetic resonance imaging (DYNAMIC MRI) and/or fast spin-echo short inversion time inversion-recovery (STIR) magnetic resonance imaging (FAST-STIR MRI), on whole or part of the liver of the subject. 
    
    
     
       BRIEF DESCRIPTION OF THE FIGURES 
         FIG. 1 . Comparative analysis of diagnostic accuracy between NASHMRi (AUROC: 0.86; 95% CI:0.76-0.96), CK-18 (AUROC:0.44; 95% CI:0.29-0.60); p&lt;0.0001. 
         FIG. 2 . Comparative analysis of diagnostic accuracy between Fibro-MRI (AUROC: 0.95; 95% CI:0.88-1.00) and Transient Elastography (AUROC:0.91; 95% CI:0.81-1.00); p=ns. 
         FIG. 3 . Comparative analysis of diagnostic accuracy among FibroMRi (AUROC: 0.89; 95% CI:0.79-0.98), Sydney Index (AUROC:0.64; 95% CI:0.47-0.82) and NAFLD Fibrosis Score (AUROC: 0.61; 95% CI: 0.43-0.79); p&lt;0.001. 
     
    
    
     DESCRIPTION OF THE INVENTION 
     List of Abbreviations 
     
         
         NAFLD: Non-alcoholic fatty liver disease 
         MRI: Magnetic resonance images 
         FGF21: Fibroblast grown factor 21 
         CK-18: Cytokeratin 18 
         NASH: Non-alcoholic steatohepatitis 
         NAS Score: NAFLD Activity Score 
         SAF: Steatosis, Activity, Fibrosis score 
         HOMA-IR: Homeostatic Model Assessment of Insulin Resistance 
         ALT: Alanine aminotransferase 
         AST: Aspartate aminotransferase 
         ECLIA: Electrochemiluminescence immunoassay 
         BMI: Body mass index 
         WL: Window level 
         WW: Window width 
         FoV: Field of view 
         ROC: Receiver operating characteristics 
         AUROC: Area under receiver operating characteristic 
         AUC: Area under the curve 
         PPV: Positive predictive value 
         NPV: Negative predictive value 
         MRS: Magnetic resonance spectroscopy 
         MRE: Magnetic resonance elastography 
       
    
     Definitions 
     In the context of the present invention E3: Harmonic mean is understood as the Harmonic mean HH of a set of nn numbers {xx1,xx2, . . . xxnn} as a specific kind of average. In this case, {xx1,2, . . . xxnn} represents the whole set of pixel values of each image sample. 
     
       
         
           
             H 
             = 
             
               n 
               
                 
                   ∑ 
                   
                     i 
                     = 
                     1 
                   
                   n 
                 
                  
                 
                   1 
                   
                     x 
                     i 
                   
                 
               
             
           
         
       
     
     In the context of the present invention E6: Mode means the Mode of a set of nn numbers {xx1,2, . . . xxnn}, namely the mode is the value that occurs most often. 
     In the context of the present invention E22: Pearson Mode Skewness is the Pearson mode skewness, also called Pearson&#39;s first coefficient of skewness, is a way to figure out the skewness of a distribution. It tells how far the distribution departs from symmetry. 
     
       
         
           
             P 
             = 
             
               
                 mean 
                 - 
                 mode 
               
               σ 
             
           
         
       
     
     In the context of the present invention E31: Column&#39;s mean in multi-oriented co-occurrence matrix is understood as follows. In image processing, co-occurrence matrices are used to analyze the texture of an image. A multi-oriented matrix computes the magnitude and orientation of the local gradient vector at each pixel position, so each pixel carries its gray value, its gradient magnitude and its gradient orientation. 
     In the context of the present invention E57: 2-2 Order Contrast is understood as the texture attribute of a given sample, computed as a local grey level variation in the grey level co-occurrence matrix. It can be thought of as a linear dependency of grey levels of neighboring pixels. If the neighboring pixels are very similar in their grey level values then the contrast in the image is very low. In case of texture, the grey level variations show the variation of texture itself. High contrast values are expected for heavy textures and low for smooth, soft textures. Specific details of this parameter are given in Texture Characterization based on Grey-Level Co-occurrence Matrix. Huertas, R. y Gebejes, A. 2013. Conference of Informatics and Management Sciences. 375-378. 
     In the context of the present invention E73: Averaged mean curvature is a value representing the mean of the curvatures mean at each pixel within the image sample. Curvature is a feature used to describe image surface. 
     In the context of the present invention E75: Maximum of primary curvatures is the maximum value for all the primary curvatures (primary curvature from every pixel of the sample) is used as a surface attribute descriptor. 
     In the context of the present invention the MRI protocols shown below can be followed in order to obtain the necessary information to carried-out the methods of the present invention. However, it is noted that the protocols shown herein below are especially suitable for acquiring MRIs using General Electric (Milwaukee, Conn. USA) 1.5-Tesla whole-body systems (other protocols might be necessary for different systems): 
     
       
         
           
               
               
               
             
               
                   
               
               
                 Protocol SSFSE-T2 
                 Protocol FAST 
                 Protocol DYNAMIC 
               
               
                 MRI 
                 STIR MRI 
                 MRI 
               
               
                   
               
             
            
               
                 FOV: 450 mm. 
                 FOV: 400 mm. 
                 FOV: 400 mm. 
               
               
                 RFOV: 80%. 
                 RFOV: 75%. 
                 RFOV: 90%. 
               
               
                 TR/TE: Minimum/100 ms. 
                 TR/TE: 1800/30 ms. 
                 TR/TE: In phase. 
               
               
                   
                 TI: 165 ms. 
                 FLIP ANGLE: 10 ms. 
               
               
                 THICKNESS/GAP: 
                 THICKNESS/GAP: 
                 THICKNESS: 
               
               
                 8/1 mm. 
                 7/1 mm. 
                 5.0 mm. 
               
               
                 NSA: 2. 
                 NSA: 2. 
                 NSA: 1. 
               
               
                 MATRIX: 320/129. 
                 MATRIX: 320/320. 
                 MATRIX: 128/256. 
               
               
                 SLICES: 24. 
                 SLICES: 24. 
                 SLICES: 128. 
               
               
                 ACQUISITION 
                 ACQUISITION 
                 ACQUISITION 
               
               
                 TIME: 40 s. 
                 TIME: 7:27 min. 
                 TIME: 17.5 s. 
               
               
                   
                   
                 PHASES: BASAL 
               
               
                   
               
            
           
         
       
     
     DETAILED DESCRIPTION OF THE INVENTION 
     In this invention, the authors of the present invention demonstrate that computerized optical analysis of conventional non-contrast-enhanced MR (magnetic resonance) images of the liver enables the detection of steatohepatitis by NASH-MRI and significant fibrosis by Fibro-MRI in patients suffering from NAFLD. By using the methods of the present invention it is possible to forecast the rate of disease progression, to support therapeutic decision-making, and to monitor potential therapeutic effects. 
     Diagnoses of liver diseases have long relied on liver biopsy, despite high intra- and inter-observer variability and sampling error. Panel of serum biomarkers to confirm, or rule out, steatohepatitis remained elusive. NASH-test, included in Fibromax®, is a semi-quantitative score with a wide gray zone; Owl-Liver® accurately predicts steatohepatitis but needs to be analyzed in a centralized laboratory that undermines accessibility; combination of CK-18 and FGF-21 failed to confirm usefulness in the diagnosis of NASH. 
     Non-invasive diagnosis of significant fibrosis in NAFLD is also a challenge. NAFLD Fibrosis Score was specifically developed in NAFLD but in the validation process, it showed a wide gray zone with no predictive capacity. Noninvasive markers imported from hepatitis C have been tested in NAFLD like Sydney Index, FIB-4, Forns&#39;s Index and APRI. However, a poor correlation between serum biomarkers of liver fibrosis (APRI, FIB4, AST/ALT ratio, European Liver Panel and Liver stiffness measurement) was reported in diabetic patients. Agreement was good on the absence of advanced liver disease but poor agreement on the presence of advanced disease. 
     Moreover, image-based non-invasive methods are growing interest. Ultrasonography, transient elastography, acoustic radiation force impulse, magnetic resonance spectroscopy, or magnetic resonance elastography have been utilized for NASH diagnosis. Ultrasonography has shown 60-94% sensitivity and 84-95% specificity in hepatic steatosis detection, which is acceptable for first-line steatosis screening in clinical practice, but cannot distinguish NASH from simple steatosis. Transient elastography (FibroScan®; Echosens, Paris, France) has shown a good diagnostic accuracy stratifying advanced fibrosis in NAFLD. However, transient elastography has been found non-useful for the diagnosis of NASH. Nevertheless, more than 10% of patients cannot be assessed because of procedure failures due, mainly, to high body mass index (BMI). Hence, threshold defining advanced fibrosis stage remains controversial and requires higher scores of kPa to define cirrhosis, compared with cut-offs accepted for viral hepatitis. Magnetic resonance spectroscopy (MRS) enables the evaluation, in vivo, of liver molecular composition and the detection of steatosis with high accuracy, being a reference method for steatosis but fails detecting NASH. Magnetic resonance elastography (MRE) has been shown to be accurate in fibrosis staging but its availability is low in most Centers and needs further external validation. The main limitation of these image-based methods has been the inability to detect steatohepatitis. Novel developments in the MR field such as gadolinium probes targeted to type-collagen, have shown good preliminary results, but they need to be translated into a standard clinical setting. 
     In contrast, as shown herein, NASHMRI and FibroMRI can accurately predict steatohepatitis and fibrosis staging. MRI can access deep tissue, and enables whole liver analysis. It is useful in decision-making with respect to donor liver management prior to liver transplantation, or in calculating the speed of fibrosis progression in liver diseases which share steatosis as a major feature such as, for example, viral hepatitis or alcohol-related liver diseases. Since ionizing radiation is avoided, the proposed system is suitable for monitoring fibrosis and steatohepatitis progression over time. 
     Further, NASHMRI and FibroMRI are independent of the MR system manufacturer (Philips vs. GE, in our current study). Score data are related to the presence of steatohepatitis or fibrosis; the lower the scores the lower the probability of suffering from steatohepatitis, or significant fibrosis. The opposite is also valid i.e. the higher the score the greater the risk of showing steatohepatitis, or significant fibrosis. 
     Technical limitation is segmentation error. This is because the method is based on an optical analysis of image features to quantify fairly delicate differences that are not visible to the naked eye. Vessels or other structures in the sample could be confounding factors resulting in under- or over-estimating the degree of fibrosis or inflammation. This has been resolved (in the current analysis) by excluding areas containing blood vessels, biliary tract, or focal lesions and, as well, all the samples with &gt;30% pixels outside the segmented area. 
     In conclusion, NASHMRI and FibroMRI are useful in diagnosing steatohepatitis and significant fibrosis in patients with NAFLD. The methods offer clear advantages over liver biopsy since they are innocuous; enable the screening of large numbers of patients, and the analysis of the whole liver using user-friendly software. The disease could be staged with respect to severity, and could provide information on prognosis, supporting clinical decisionmaking, and monitoring response to therapy. 
     The following examples merely serve to illustrate the invention and thus they do not limit the same. 
     Examples 
     Material and Methods 
     Study Design and Patients 
     This was a prospective, multi-centered cohort study that included well-characterized biopsy-proven NAFLD patients. All patients provided informed consent for liver biopsy and MRI study. Inclusion criteria were:
         a. adults (&gt;18 years of age) who showed diffuse hyperechogenic liver on ultrasonography;   b. impairment in biochemical liver profile;   c. metabolic syndrome features including an increased HOMA-IR.       

     Exclusion criteria were significant alcohol abuse (&gt;30 g/day in men and &gt;20 g/day in women) and evidence of viral or autoimmune hepatitis, HIV, drug-induced fatty liver or other metabolic liver diseases (such as hemochromatosis or Wilson&#39;s disease), together with pregnancy and parenteral nutrition. 
     Patients enrolled in the study were classified according to sex, age, fibrosis stage and steatohepatitis presence. Patients underwent a complete medical history, physical examination, blood tests, liver biopsy and imaging study. An overnight (12 h) fasting blood sample was taken for routine analyses that included ALT, AST, alkaline phosphatase, γGT, cholesterol and triglycerides. Fasting samples of serum obtained after centrifugation were stored in aliquots at −80° C. until assayed. Serum insulin levels were measured by electrochemiluminescence immunoassay, using an Elecsys 1010/2010 autoanalyzer (Elecsys MODULAR ANALYTICS E170; Roche, Basil, Switzerland). CK-18 were measured using a human ELISA Kit (Abnova, Walnut, Calif., USA). Transient elastography was measured using Fibroscan®, Echosens, France. Height and weight were determined at baseline, and the body mass index (BMI) was calculated as weight (in kg)÷height (in m2). 
     Histological Staging and Grading 
     Percutaneous liver biopsies were performed under local anesthesia and ultrasound guidance. Liver specimens were obtained, after an overnight fast, by “tru-cut” needle (sample length/diameter=20/1.2 mm) using a biopsy gun and, at least, one sample per patient was extracted. Length of liver specimens was recorded and, in conjunction with the number of portal tracts, the sample was considered useful, or not, for histological diagnosis and fibrosis staging. Samples showing &lt;10 mm or &lt;10 portal tracts were excluded. 
     Biopsies were routinely processed, formalin-fixed and paraffin-embedded. A single pathologist, who was blinded with respect to provenance of the samples, assessed hematoxylin-eosin, reticulin and Masson&#39;s trichrome-stains to determine the grading and staging assignments. This scoring system comprises four semi-quantitatively features: steatosis, lobular inflammation, hepatocellular ballooning, and fibrosis. Steatohepatitis presence was calculated according to the Kleiner NAFLD Activity Score (NAS Score) and an additional score for fibrosis. NAS Score provides a composite score that comprises the degree of steatosis (score 0-3), lobular inflammation (score 0-3) and hepatocyte ballooning (score 0-2). 
     NAS score &gt;5 suggests NASH, while NAS score &lt;3 indicates simple steatosis. 
     Hepatic steatosis was quantified as the percentage of hepatocytes containing fat droplets, graded on a scale of 0-3 through subjective visual estimation of cells containing fat vacuoles. 
     Steatosis grades were broadly categorized for severity: grade 0 or normal (up to 5% of hepatocytes affected), grade 1 or mild (5-33% of cells affected), grade 2 or moderate (33-66% showing steatosis), and grade 3 or severe (&gt;66% of hepatocytes showed fat storage). 
     Lobular inflammation was assessed as grade 0 (non-inflammation), grade 1 (&lt;2 foci/×200 field), grade 2 (2-4 foci/×200 field) and grade 3 (&gt;4 foci/×200 field). 
     Ballooning was evaluated as stage 0 (none), stage 1 (a few balloon cells) and stage 2 (many cells or prominent ballooning). 
     Finally, fibrosis staging was performed according to a 5-level scale: F0=absence; F1=perisinusoidal or periportal; F2=perisinusoidal and portal/periportal, F3=bridging fibrosis, F4=cirrhosis. A further 2-level scale of fibrosis was applied: mild (F0-F1) and advanced fibrosis (F2-F3-F4). 
     Magnetic Resonance Image (MRI) Acquisition 
     MRIs were acquired at the six University Hospitals using General Electric (Milwaukee, Conn. USA) or Philips (Best, NL.) 1.5-Tesla whole-body systems. Patients were examined while they were on supine position using a standard torso coil centered over the liver without contrast, heeded by the technologist to individual breath-hold capacity. MRIs were sent to the referral Center for processing. The entire liver was imaged and 6 sections selected. Selected protocols for this study were performed in axial plane, SSFSE-T2 (Single Shot Fast Spin Echo T2-weighted), FAST-STIR (Fast Short inversion Time Inversion Recovery), inPHASE-outPHASE (in and out Phase) and DYNAMIC (See Table 1, Imaging Parameters, below).
         INSTRUCTIONS
           Arms up if possible   Breath hold must be consistent   Supine position   Contrast: none   
               

     
       
         
           
               
               
            
               
                   
                   
               
               
                   
                 Sequence name 
               
            
           
           
               
               
               
               
               
            
               
                 Imaging 
                 SSFSE- 
                 FAST- 
                 In and out 
                 DYNAM- 
               
               
                 parameters 
                 T2 
                 STIR 
                 of PHASE 
                 IC 
               
               
                   
               
               
                 Scan plane 
                 Axial 
                 Axial 
                 Axial 
                 Axial 
               
               
                 Imaging 
                 Breath hold 
                 Breath hold 
                 Breath hold 
                 Breath hold 
               
               
                 options 
               
               
                 Time (sec) 
                 29.76 + 
                 340.98 ± 
                 38.10 ± 
                 35.44 ± 
               
               
                   
                 10.05 
                 104.10 
                 6.66 
                 90.33 
               
               
                 FOV (mm) 
                 450 
                 400 
                 410 
                 375 
               
               
                 Matrix size 
                 512 × 512 
                 448 × 448 
                 432 × 432 
                 192 × 192 
               
               
                 (px) 
               
               
                   
               
            
           
         
       
         
         
           
             Sec: seconds; FoV: Field of View; mm: millimeters; px: pixel; 
           
         
       
    
     DICOM files, field of view (FoV) and matrix sizes were configured specifically for each MR protocol; minimum and maximum window values were calculated, in order to each slice could be converted into a numerical matrix of pixels within that window range. MR Imaging Processing for the definition of NASH-MRi and Fibro-MRi imaging biomarkers 
     Development or Selection of Imagining Biomarkers 
     Selected patients according to steatohepatitis and fibrosis stage were included. A squared grid segmented MR images. Sample size ranged from 10×10 to 23×23 pixels, depending on image resolution, to resemble optimal liver biopsy area. All regions containing artifacts, such as vessels, hepatobiliary ducts or with &gt;30% of the pixels outside the segmented area, were discarded and only squares comprising liver parenchyma were analyzed. Both, Philips and G&amp;E MRI were processed. 
     MRI features, extraction procedures, and implementation were developed in MATLAB® (Matrix Laboratory, MathWorks, Natick, Mass., USA) programming language. The software tool developed imports DICOM files and parses them, extracting all needed information such as patient demographic data, denomination and number of MRI assessments performed, number of slices, and date of the images.
         a. The whole set of slices corresponding to a new evaluation is shown in a squared grid. Up to 6 consecutive slices must be chosen, preferably those that show the biggest liver section.   b. Selected slices are sequentially shown separately. The user must draw the outline of liver boundaries to segment the whole liver. Once segmented, a grid of squares is automatically over-layered on the segmented area. To achieve a sample size equivalent to 20 mm of tissue, the number of pixels of samples is computed using FoV value and number of rows and columns of the image matrix. Therefore, the numbers of samples vary for different MRI sequences.   c. The software automatically discards those samples with &gt;30% of the surface outside the segmentation line. The user must also discard samples that do not represent homogeneous liver tissue.   d. A total of 84 different mathematical parameters are computed from each sample. The nature of these parameters ranges from simple statistical descriptors such as mean and standard deviation, to energy features like entropy, geometrical properties such as mean surface curvature, and spectrum characteristics.   e. These big data was processed using architectural neural networks and logistic regression. The combination of parameters from the different protocols able to define two imaging biomarkers to predict NASH (NASH-MRi) and fibrosis (Fibro-MRi) were selected.       

     Validation of Imaging Biomarkers 
     Developed imaging biomarkers were validated. No differences were seen according age, gender, steatohepatitis or fibrosis distribution. NASH was present in 44 cases (51%), and significant fibrosis in 33 patients (38%). Fibrosis distribution among the patients was: F0, n=39, F1, n=17, F2, n=18, F3, n=9, F4: n=4. Three protocols were required to accurately detect steatohepatitis and two for fibrosis detection. The average time consumed was 11.23+3.4 minutes. 
     Comparison with Biochemical Biomarkers 
     NASH-MRi was compared with CK-18 and FGF-21 levels and Fibro-MRi with FIB-4, Sydney Index and NAFLD Fibrosis Score using under area ROC curves (AUROC). 
     Data Analysis and Statistics 
     Software package SPSS® 22.0 (SPSS, Chicago, Ill., USA) was used to record data and to perform detailed statistical analysis. Values of p&lt;0.05 were considered statistically significant. Multiple linear regression analysis was used to compare continuous variables. 
     Receiver operating characteristics (ROC) curves, which represent the tradeoff between the true and false positive rate, were used to differentiate the misclassified data between normal and disease status. The AUROC can vary from 0.5 (random discriminatory accuracy) to 1.0 (perfect discriminatory accuracy) 
     AUROC was generated in order to evaluate the diagnostic accuracy of the tools as well as the predictive proficiency with respect to the presence of mild fibrosis (F0-F1), significant fibrosis (F2-F3-F4), and steatohepatitis. NASHMRI and FibroMRI were the outputs of the optical analysis and were defined as predictive models used to foretell steatohepatitis and significant fibrosis. Concordance between numerically calculated NASHMRI and FibroMRI values and histopathologically defined steatohepatitis and fibrosis were compared by Spearman regression coefficient analysis. Sample size was calculated to show a significant difference between histological parameters and NASHMRI and FibroMRI using nQuery advisor v7.0 software. 
     
       
         
           
               
             
               
                 TABLE 2 
               
             
            
               
                   
               
               
                 Baseline characteristics of patient population: demography, 
               
               
                 laboratory and histology results. Values presented 
               
               
                 as mean ± SD, unless otherwise stated 
               
            
           
           
               
               
               
            
               
                   
                   
                 Overall cohort 
               
               
                   
                 Parameter 
                 (N = 126) 
               
               
                   
                   
               
               
                   
                 Age; y 
                 51 ± 12 
               
               
                   
                 Male gender; % 
                 66 
               
               
                   
                 Body mass index; kg/m 2   
                 30.59 ± 4.84  
               
               
                   
                 Waist circumference; cm 
                 101.8 ± 11.11 
               
               
                   
                 Hispanic ethnicity; % 
                 100 
               
               
                   
                 Arterial hypertension; % 
                 36.4 
               
               
                   
                 Diabetes; % 
                 37.5 
               
               
                   
                 Cholesterol; mmol/L 
                 8.53 ± 10.9 
               
               
                   
                 Triglycerides; mmol/L 
                  5.87 ± 11.71 
               
               
                   
                 ALT; IU/L 
                 73 ± 44 
               
               
                   
                 AST; IU/L 
                 46 ± 39 
               
               
                   
                 GGT; IU/L 
                 101 ± 101 
               
               
                   
                 Platelet count; ×10 9   
                 233 ± 57  
               
               
                   
                 Fasting glucose; mmol/L 
                  8.06 ± 15.72 
               
               
                   
                 Insulin; mg/dL 
                 14.88 ± 9.29  
               
               
                   
                 Steatosis grade; % 
               
               
                   
                 0 
                 17.9 
               
               
                   
                 1 
                 42.3 
               
               
                   
                 2 
                 24.4 
               
               
                   
                 3 
                 15.4 
               
               
                   
                 NASH; % 
                 51.2 
               
               
                   
                 Fibrosis stage; % 
               
               
                   
                 F0-F1 
                 60 
               
               
                   
                 F2-F3-F4 
                 40 
               
               
                   
                 NAS Score (%) 
               
               
                   
                 &lt;4  
                 37.1 
               
               
                   
                 ≧4     
                 62.9 
               
               
                   
                   
               
               
                   
                 Notes to Table 2: AST: Aspartate Aminotransferase; ALT: Alanine aminotransferase; GGT: Gamma Glutamyl Transferase; NASH: Non-alcoholic Steatohepatitis; NAS score: NAFLD Activity Score. 
               
            
           
         
       
     
     Example 1. Development and Standardization of NASH-MRI for Detection of Steatohepatitis 
     Estimator E3 (harmonic mean) from MRI protocol SSFSE-T2, estimator E57 (2-2 order contrast) from MRI protocol DYNAMIC, and estimator E73 (weighted mean curvature) from MRI protocol FAST-STIR, were found to be independently associated with NASH. 
     Model coefficients associated with each one of these independent variables were β1=0.079 (OR: 1.08, 95% CI: 1.02-1.15; p=0.015) and β2=0.22 (OR: 1.14, 95% CI: 1.03-1.26; p=0.015). 
     These estimators influence on the predictive equation to obtain the probability of suffering steatohepatitis: 
       NASH-MRI=1/(1 e   (1.654−0.079*E3   _   T2−0.127*E57   _   DYN*E73   _   FAST) ) 
     AUROC obtained was 0.88 (95% CI: 0.77-0.99) in the estimation cohort. Values for sensitivity of 87%, specificity of 74%, positive predictive value (PPV) of 80% and negative predictive value (NPV) of 82% were obtained from prediction based on a cut-off point of 0.5 for NASHMRI. 
     In the validation cohort, NASH-MRI AUROC was 0.84 (95% CI: 0.75-0.92). Using the defined threshold of 0.5 for NASH-MRI prediction, results obtained were: sensitivity 87%, specificity 60%, PPV 71% and NPV 81%. 
     Example 2. Development and Standardization of Fibro-MRI for Detection of Fibrosis 
     Estimator E22 (Pearson&#39;s asymmetry coefficient) from MRI protocol SSFSE-T2 and estimators E3 (harmonic mean), E6 (mode), E31 (column&#39;s mean of multi-oriented co-occurrence matrix) and E75 (maximum of main curvatures) from MRI protocol DYNAMIC were found to be independently associated with fibrosis. Model coefficients associated with each of these independent variables were: β1=1.101 (OR: 3.01, 95% CI: 1.25-7.25; p=0.014); β2=−1.105 (OR: 0.33, 95% CI: 0.14-0.77; p=0.010); β3=−115.737 (OR: 0.08, 95% CI: 0.02-0.14; p=0.046) β4=0.696 (OR: 2.00, 95% CI: 1.19-3.38; p=0.009); and β5=−0.825 (OR: 0.44,v95CI %: 0.21-0.93; p=0.030) should be introduced into the predictive equation to obtain the risk of suffering fibrosis as: 
       Fibro-MRI=1/(1 +e   (−4.207-1.101*E3   _   DYN+1.105*E6   _   DYN+115.737*E22   _   T2−0.696*E31   _   DYN+0.825*E75   _   DYN) ) 
     In the estimation cohort, AUROC was 0.94 (95% CI: 0.87-1.00) with a sensitivity of 81%, specificity of 85%, PPV of 77% and NPV of 86% using a cut-off point of 0.5 for FibroMRI. 
     In the validation cohort, Fibro-MRI AUROC for significant fibrosis was 0.85 (95% CI: 0.77-0.93). Using the defined threshold at 0.5 for FibroMRI prediction, the results obtained were: sensitivity 77%, specificity 80%, PPV 67% and NPV 87%. 
     Example 3. Standardization of NASH-MRI and Fibro-MRI Across MRI Systems 
     NASHMRI, calculated using a GE scanner, showed a similar diagnostic accuracy AUROC=0.76 (95% CI: 0.58-0.96) vs. AUROC=0.83 (95% CI: 0.70-0.96); p=ns in comparison with NASHMRI calculated in patients that underwent MRI with the Philips system. With respect to FibroMRI, evaluations performed using a GE scanner showed an AUROC=0.81 (95% CI: 0.66-0.95) vs. AUROC=0.86 (95% CI: 0.72-0.99) using the Philips system (p=ns). 
     Comparative analysis with non-invasive biochemical markers of steatohepatitis NASHMRI was compared with FGF-21 and CK-18 in the diagnosis of steatohepatitis in a cohort of 64 patients. NASHMRI offered the best diagnostic accuracy with an AUROC of 0.86 (95% CI: 0.76-0.96) for steatohepatitis presence significantly better than CK-18 levels AUROC of 0.56 (95% CI: 0.40-0.71; p&lt;0.05) ( FIG. 1 ). NAS score correlated with both NASH-MRI (r=0.38; p&lt;0.001) and CK-18 levels (r=0.29; p&lt;0.02). 
     Comparative analysis with non-invasive biochemical markers of significant fibrosis Fibro-MRI (AUROC: 0.85; 95% CI: 0.74-0.97) was significantly superior to NFS (AUROC: 0.56; 95% CI:0.41-0.71) and Sydney Index (AUROC: 0.69; 95% CI:0.50-0.87); p&lt;0.05 ( FIG. 2 ). Fibrosis stage correlated with Fibro-MRI (r=0.61; p&lt;0.001), and with NFS (0.52; p&lt;0.001). Moreover, a correlation between NFS and Fibro-MRI was found (r=0.53; p&lt;0.001). Lastly, performance of FibroMRI (AUROC: 0.95; 95% CI: 0.88-1.00) was similar to transient elastography (AUROC: 0.91; 95% CI: 0.81-1.00); p=ns ( FIG. 3 ).