The investigators will assess the ability of ultrasound (US) to measure liver stiffness (cirrhosis) and liver fat content (steatosis).
Non-invasive monitoring of liver fibrosis using magnetic resonance imaging (MRI) can help determine which patients will most benefit from interventional therapies to help reverse the condition. Similarly, quantitative assessment of liver fat content using MRI can assist physicians in identifying patients at risk for hepatic steatohepatitis. Due to the widespread dissemination of US machines and their relative lower cost compared to other imaging modalities, e.g. MRI, the ability of US to assess these parameters widens diagnostic availability. Patients who have undergone an MRI exam to assess liver stiffness (cirrhosis) and/or liver fat content (steatosis) will be asked to undergo an US exam to assess the same parameters. The cirrhosis and steatosis measurements obtained from both exams will be compared. If US-based measurements of liver stiffness and/or liver fat content are shown to be reproducible and accurate when compared to MRI values (will be used as the gold standard), US may become the first-line diagnostic test for these liver conditions.
Study Type
OBSERVATIONAL
Enrollment
35
Patients will undergo a standard liver US protocol for approximately 15 minutes.
Stanford University Hospital
Stanford, California, United States
Stanford University
Stanford, California, United States
Discriminatory value (AUC) of US in assessing liver stiffness
Discriminatory value (the ability of the test to diagnose disease from healthy) will be calculated using nonparametric receiver operating characteristic (ROC) curve analysis. Sensitivity (i.e. true positive rate: the ability of a test to correctly identify patients with the disease being investigated) is plotted on the y axis and 1-specificity (i.e. false positive rate: the test incorrectly identifies patients who do not have the disease being investigated as having the disease) will be plotted on the x-axis. Cutoff points of US liver stiffness values will be chosen to create the graph of sensitivity versus 1-Specificity, i.e. the ROC curve, and the area under the curve (AUC) is used to determine how well US discriminates patients with liver stiffness from patients without liver stiffness. The closer the AUC gets to 1, the more discriminatory the test. An AUC=0.5 is a test that is right 50% of the time, and is no better than flipping a coin.
Time frame: an estimated time of 30 minutes
Discriminatory value (AUC) of US in assessing liver fat content
Discriminatory value (the ability of the test to diagnose disease from healthy) will be calculated using nonparametric receiver operating characteristic (ROC) curve analysis. Sensitivity (i.e. true positive rate: the ability of a test to correctly identify patients with the disease being investigated) is plotted on the y axis and 1-specificity (i.e. false positive rate: the test incorrectly identifies patients who do not have the disease being investigated as having the disease) will be plotted on the x-axis. Cutoff points of US liver fat content values will be chosen to create the graph of sensitivity versus 1-specificity, i.e. the ROC curve, and the area under the curve (AUC) is used to determine how well US discriminates patients with liver fat content from patients without liver fat content. The closer the AUC gets to 1, the more discriminatory the test. An AUC=0.5 is a test that is right 50% of the time, and is no better than flipping a coin.
Time frame: an estimated average of 30 minutes
US Elastography Stiffness
MRI tissue stiffness is reported in shear modulus (S), while US uses Young's modulus (Y). Since Y = 2\*(1+m)\*S, where m is the Poisson's ratio for the material, and since that ratio is between 0.49 and 0.50 for tissue, the US measurements should be three times that of the MRI ones. In particular, treating the MRI measurements as the reference, theory predicts a linear relationship: Y = 0 + 3\*S which can be tested with a linear regression model. The most straightforward means is by calculating the expected Young's modulus values for US from the MR measurements, and regressing them on the observed US measurements. Both US and MRI values are given below.
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Time frame: an estimated average of 30 minutes