A multicenter multinational retrospective study of the 1-year natural history of LI-RADS 3 observations in patients with cirrhosis.
Authors
Affiliations (14)
Affiliations (14)
- Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy.
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Stephen M. Ross School of Business, University of Michigan, Ann Arbor, MI, USA.
- Department of Emergency Medicine, Yale University, New Haven, CT, USA.
- Department of Biomedicine, Neuroscience and Advanced Diagnostics, University of Palermo, Palermo, Italy.
- Department of Radiology, University of Washington, Seattle, WA, USA.
- Department of Radiology, Ospedale per gli Infermi di Faenza, AUSL Romagna, Faenza, Italy.
- Department of Radiology, Morgagni-Pierantoni Hospital, AUSL Romagna, Forlì, Italy.
- Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy.
- Department of Nuclear Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.
- Department of Gastroenterology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, Milan, Italy.
- Department of Radiology, University of California San Diego, San Diego, CA, USA.
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. [email protected].
Abstract
To assess the 1-year natural history of liver imaging reporting and data system (LI-RADS) 3 observations on contrast-enhanced MRI in cirrhotic patients across multiple international centers, and to identify clinical and imaging predictors of progression using multivariable and machine learning models. This retrospective study included 347 cirrhotic patients with 540 LI-RADS 3 observations from six centers across three countries, each with 12 (±3) months of follow-up MRI. Observations were reassessed using LI-RADS v2018 criteria. Generalized linear mixed-effects models and machine learning (LASSO, random forest) evaluated predictors of progression. Area under the curve (AUC) analysis assessed the predictive performance of clinical and imaging variables. Within one year, 28% of LI-RADS 3 observations progressed: 14% to LI-RADS 4 and 14% to LI-RADS 5. Independent predictors of progression included lesion size, with an odds ratio (OR: 1.12, 95% CI: 1.01-1.24), Child-Pugh Class C (OR: 8.36, 95% CI: 1.01-69.27), and alcohol-related liver disease (OR: 0.24, 95% CI: 0.06-0.94). Enhancing capsule and untreated hepatitis C virus were significant in univariable analysis. Imaging features improved predictive accuracy, increasing AUC from 0.65 to 0.72 (p = 0.01). A lesion size cut-off of 9.5 mm was associated with increased progression risk. One in four LI-RADS 3 observations progress within one year. Lesion size, liver function, and etiology are key predictors. Integration of imaging features enhances risk stratification and supports more personalized follow-up strategies for indeterminate liver lesions. Question Identifying which LI-RADS 3 liver observations progress to malignancy remains challenging; evidence from large, standardized, multicenter MRI cohorts is lacking. Findings In this large multinational study, 28% of LI-RADS 3 observations progressed within one year; lesion size, liver dysfunction, and disease etiology were key independent predictors. Clinical relevance LI-RADS 3 observations show significant progression risk, with imaging features improving prediction models and guiding surveillance strategies for early HCC detection.