Monday, November 26 | 11:00 a.m.-11:10 a.m. | SSC06-04 | Room N229
Liver surface nodularity -- a quantitative biomarker evident on CT scans -- accurately stages chronic liver disease nearly as well as traditional liver biopsy, according to researchers from Alabama.The conventional method for staging liver diseases involves invasive biopsy. Exploring the potential of a noninvasive option for staging, presenter Dr. Andrew Smith, PhD, and colleagues from the University of Alabama at Birmingham reviewed the CT scans of 193 patients at their institution. The patients were diagnosed with hepatitis C virus infection, which often leads to serious liver fibrosis.
To stage the extent of disease, Smith and colleagues calculated liver surface nodularity scores using computer software to analyze the patients' CT scans. The nodularity scores correlated with the patients' degree of liver fibrosis, they found. This allowed the clinicians to use the scores to stage liver fibrosis by examining the CT scans rather than by performing an invasive liver biopsy.
A comparison of the liver surface nodularity technique and conventional liver biopsy revealed that the noninvasive method was nearly as accurate as biopsy. The accuracy improved even more when the nodularity scoring method was combined with another noninvasive staging method referred to as the fibrosis-4 (FIB-4) index.
The liver surface nodularity score on CT and FIB-4 score are easy to obtain and could help stage liver fibrosis noninvasively and accurately in patients with chronic liver disease, the group noted.



















![Images show the pectoralis muscles of a healthy male individual who never smoked (age, 66 years; height, 178 cm; body mass index [BMI, calculated as weight in kilograms divided by height in meters squared], 28.4; number of cigarette pack-years, 0; forced expiratory volume in 1 second [FEV1], 97.6% predicted; FEV1: forced vital capacity [FVC] ratio, 0.71; pectoralis muscle area [PMA], 59.4 cm2; pectoralis muscle volume [PMV], 764 cm3) and a male individual with a smoking history and chronic obstructive pulmonary disorder (COPD) (age, 66 years; height, 178 cm; BMI, 27.5; number of cigarette pack-years, 43.2, FEV1, 48% predicted; FEV1:FVC, 0.56; PMA, 35 cm2; PMV, 480.8 cm3) from the Canadian Cohort Obstructive Lung Disease (i.e., CanCOLD) study. The CT image is shown in the axial plane. The PMV is automatically extracted using the developed deep learning model and overlayed onto the lungs for visual clarity.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/03/genkin.25LqljVF0y.jpg?auto=format%2Ccompress&crop=focalpoint&fit=crop&h=112&q=70&w=112)