From Liver to Brain: A 2.5D Deep Learning Model for Predicting Hepatic Encephalopathy Using Opportunistic...

This study aims to develop a 2.5D deep learning framework leveraging non-contrast CT scans for early prediction of hepatic encephalopathy (HE) in hepatitis B-related acute-on-chronic liver failure (ACLF) patients. This retrospective study enrolled 228 ACLF patients meeting APASL criteria from two centers. Participants were divided into training (n = 102), internal validation (n = 44), and external testing (n = 82) cohorts. Non-contrast CT scans (5 mm slices) from six scanner models were preprocessed to 1 × 1 × 1 mm³ isotropic resolution with windowing (30-110 HU). Liver ROIs were manually segmented by two radiologists. The image center on the maximal cross-sectional slice and its adjacent slices (±1/2/4) were extracted to form 2.5D inputs. Deep learning models (DenseNet121, DenseNet201, ResNet50, InceptionV3) were employed for feature extraction. Multi-instance learning methods, including probability likelihood histograms and bag-of-words, were used for feature fusion. Machine learning classifiers (Logistic Regression, RandomForest, LightGBM) with 5-fold cross validation were built for HE prediction. DenseNet121 demonstrated the best slice-level prediction performance (validation AUC: 0.698). The LightGBM classifier with MIL fusion achieved AUCs of 0.969 (training), 0.886 (validation), and 0.829 (external testing), outperforming other fusion methods. Grad-CAM visualizations confirmed model attention to peri-portal fibrotic regions, demonstrating anatomical relevance. The MIL-based 2.5D deep learning model effectively predicts HE risk using routine non-contrast CT in ACLF patients, providing a non-invasive method for individualized risk assessment.