Your Donations in Action: John E. Miller, BA
Comparing Screening Methods to Predict Cirrhosis Decompensation in Chronic Liver Disease
Chronic liver disease (CLD) can progress to cirrhosis and result in liver decompensation and death. As CLD progresses, liver surface nodularity (LSN) increases and can be quantified from CT images to generate a LSN score, and liver stiffness increases and can be quantified by shear wave US (SWUS) elastography.
In his 2018 RSNA Research Medical Student Grant project, “Comparison of Liver Surface Nodularity Score and Ultrasound Elastography for Predicting Cirrhosis Decompensation,” John E. Miller, BA, a fourth-year medical student at the University of Alabama at Birmingham School of Medicine, compared the ability of the LSN score per CT, liver stiffness per SWUS elastography, Model for End- Stage Liver Disease (MELD) score and Fibrosis-4 (FIB-4) index to predict hepatic decompensation in patients with CLD.
A total of 186 patients had US elastography and CT imaging within six months of each other, and liver decompensation was present in 13%. The accuracy for predicting decompensated CLD at baseline was highest with the LSN score as compared to liver stiffness, MELD score and FIB-4 index. The CT-based LSN score was a better predictor of hepatic decompensation in patients with CLD than liver stiffness per ultrasound shear wave elastography, MELD score or the FIB-4 index.
“Ultrasound elastography is currently a widely- used measure to stage CLD but has limitations including a requirement for patient fasting and a dedicated expensive device, infrequent use in cirrhosis and occasional contraindications and technical failures,” Miller said. “CT imaging requires no patient preparation and is widely applicable and frequently used to evaluate cirrhosis. The CT-based LSN score is also simple to derive and has few technical limitations and failures. The CT-based LSN score can be used as an alternative noninvasive method to stage liver fibrosis, detect clinically significant portal hypertension, and predict future liver decompensation.”