A transformer-based framework for liver stiffness classification using multi-modality body MRI in children and adults

Magnetic resonance elastography (MRE) provides a noninvasive method to quantify liver stiffening, a surrogate biomarker for monitoring liver fibrosis. However, the availability of MRE remains limited, especially outside the United States, in part due to cost. This study aims to develop a deep learning-based approach for stratifying liver stiffness using multiparametric MRI images from pediatric and adult patients from multiple sites. We performed multi-site ten-fold cross-validation and achieved an AUROC of 0.80 for liver stiffness stratification. These results demonstrate that our proposed deep learning model may provide a means for categorical estimation of liver stiffening without dedicated elastography.