Objectives: Liver surface nodularity (LSN) is a recognized non-invasive biomarker of cirrhosis. This study introduces auto-LSN, an artificial intelligence (AI)-based algorithm for fully automated LSN quantification, assesses its association with fibrosis stage and its non-inferiority in diagnostic performance for advanced chronic liver disease (ACLD) and cirrhosis compared to the FDA-approved, semi-automated liver boundary analysis (LBA) software.
Materials and methods: This retrospective, bicentric study included patients with chronic liver disease risk factors who underwent CT and liver biopsy between April 2014 and March 2020. Fibrosis stages were grouped into F3-F4 (ACLD) vs F0-F2, and F4 (cirrhosis) vs F0-F3 per the METAVIR. LSN was measured with auto-LSN and LBA. Their association with fibrosis grade and diagnostic accuracy for ACLD and cirrhosis were compared using a -0.05 non-inferiority margin. Mann-Whitney-Wilcoxon tests, Spearman correlation, and area under the receiver operating characteristic curve (AUC) were used.
Results: In 127 patients (68 ± 12 years; 97 men), auto-LSN demonstrated a positive correlation with fibrosis stage (ρ = 0.59; 95% CI [0.48, 0.68]), similar to LBA (ρ = 0.44; 95% CI [0.32, 0.55]), both p < 0.001, with differences within the non-inferiority margin ([0.03, 0.26]). Auto-LSN achieved AUCs of 0.79 (95% CI [0.70, 0.87]) for ACLD and 0.84 (95% CI [0.76, 0.91]) for cirrhosis, comparable to LBA's AUCs of 0.73 (95% CI [0.64, 0.82]) and 0.74 (95% CI [0.66, 0.83]), respectively. All differences were within the non-inferiority margin.
Conclusion: Auto-LSN correlates positively with fibrosis stage and provides non-inferior diagnostic performance compared to LBA. Its full automation and accuracy support its potential for opportunistic screening and objective patient monitoring.
Key points: Question LSN is a key radiological feature for non-invasive ACLD diagnosis. However, current LSN quantification software is only semi-automated, thus time-consuming. Findings The fully automated auto-LSN algorithm for LSN quantification achieved statistically non-inferior diagnostic performance compared to existing semi-automated software for the detection of ACLD and cirrhosis. Clinical relevance Auto-LSN, as a fully automated solution, offers a reliable alternative to existing semi-automated software, enabling mass opportunistic screening of the general population-by evaluating all CT scans performed for any indication-and supporting objective follow-up of at-risk patients.
Keywords: Deep learning; Diagnostic imaging; Early diagnosis; Fibrosis; Liver cirrhosis.
© 2026. The Author(s), under exclusive licence to European Society of Radiology.