MGZ Mamba: A Medical Global Zoom Mamba Network for Preoperative Prediction of Post-TIPS Hepatic Encephalopathy

Transjugular intrahepatic portosystemic shunt (TIPS) is an effective treatment for portal hypertension, but post-TIPS hepatic encephalopathy (HE) remains a major complication. Conventional clinical scores use limited clinical and biochemical variables and cannot fully characterize preoperative imaging phenotypes. Although contrast-enhanced abdominal computed tomography(CT) contains morphological, textural, and spatial cues, available datasets are small, CT sequences are redundant, and local details must be modeled together with broader context. We therefore propose Medical Global Zoom Mamba (MGZ Mamba). A medical visual invariance constraint stratifies CT slices by feature density using the nonzero-pixel ratio and gray-level entropy, allowing multiple training instances to be constructed from each patient sequence. A double-nested parallel aggregate attention module combines multi-receptive-field convolution with global channel recalibration, while a feature rearrangement 2D scan module introduces dynamic windows and spatial-neighborhood rearrangement alongside the original two-dimensional selective scan branch. In patient-level five-fold cross-validation on the Post-TIPS Hepatic Encephalopathy Dataset of Shanxi Medical University (HE-SMU), MGZ Mamba achieved 81.65% accuracy, 82.76% recall, 80.53% F1-score, and an ROC-AUC of 0.829. Relative to the strongest baseline for each metric, accuracy, recall, and F1-score increased by 0.41, 1.07, and 0.56 percentage points, respectively, and ROC-AUC increased by 0.007. These findings support MGZ Mamba as an imaging-based approach for preoperative risk stratification and may provide complementary evidence for clinical assessment and treatment planning.