Noninvasive Classification of Advanced Liver Fibrosis Using Clinical Data and Non-Contrast MRI: Fusion of Clinical, Radiomic, and Deep Learning Models.

Noninvasive identification of advanced liver fibrosis (ALF), commonly defined as Scheuer stage S3 or higher, is important for the management of patients with liver disease, as it indicates an increased risk of progression to cirrhosis and helps determine the need for closer monitoring and timely intervention. In this retrospective study, we developed and evaluated clinical, deep learning (DL), radiomics, and multimodal fusion models for the noninvasive classification of advanced liver fibrosis using non-contrast magnetic resonance imaging (MRI) combined with clinical data. A total of 366 patients from three clinical centers between May 2018 and June 2024 were included. Radiomics and DL features were extracted from preoperative multiparametric non-contrast MRI, including T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI). Three single-modality models, namely clinical, radiomic, and DL models, were constructed, and two multimodal fusion models were further developed, including one based on feature-level fusion and the other on decision-level integration. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). Among all models, the decision-level fusion model achieved the best overall performance, with an AUC of 0.7891 in the internal validation cohort and 0.7851 in the external validation cohort. In addition, it demonstrated superior clinical net benefit and good calibration compared with the feature-level fusion, clinical, radiomic, and DL models. Overall, the proposed decision-level fusion model showed potential for the noninvasive identification of advanced liver fibrosis by integrating routine MRI and clinical data; however, its external validation performance indicates that further refinement and validation are needed before clinical application.