Development and validation of a deep learning model for liver shear stiffness regression using abdominal multiparametric MRI across multiple sites and vendors.

objectiveLiver stiffness measurement is important for assessing chronic liver disease (CLD). MR elastography (MRE) requires specialized hardware and expertise. Non-invasive deep learning (DL) models using multiparametric abdominal MRI may provide an accessible alternative. We sought to develop and validate a DL model for predicting continuous liver shear stiffness from non-contrast multiparametric abdominal MRI and electronic health record (EHR) data across multiple sites and vendors. MATERIALS AND

methodsThis was a retrospective, multi-institutional study. We analyzed 3680 abdominal MRI examinations from 3376 patients with confirmed or suspected CLD. Non-contrast T1-weighted (T1w), T2-weighted (T2w), and diffusion-weighted imaging (DWI) with EHR data were used as inputs. MRE-derived liver shear stiffness served as the reference. A transformer-based multi-channel DL model was trained using multi-site 10-fold cross-validation and evaluated on temporally held-out internal (n = 1224) and independent external (n = 365) test sets. Performance was measured by Pearson's correlation coefficient (r); residual analysis assessed bias.

resultsIn cross-validation, the model achieved an r of 0.78 (95% CI: 0.75, 0.80). On the internal test set, r was 0.77 (95% CI: 0.73, 0.80), and on the external set, r was 0.76 (95% CI: 0.69, 0.83). The model showed no significant bias based on age, sex, or BMI (p > 0.05). In patients with and without steatotic liver disease, r was 0.74 and 0.76, respectively.

conclusionOur transformer-based multi-channel model predicts continuous liver shear stiffness from routinely acquired multiparametric MRI and EHR data with moderate correlation to MRE, representing a potential step toward accessible, non-invasive liver stiffness estimation. KEY POINTS: Question Can routinely acquired multiparametric abdominal MRI and electronic health record data predict liver stiffness across multiple sites and scanner vendors using a deep learning approach? Findings The optimized deep learning model predicted liver stiffness with r = 0.78 in cross-validation and r = 0.76 in external validation using multiparametric MRI and electronic health record data. Clinical relevance This study introduces a preliminary yet robust AI method to estimate liver stiffness from routine multiparametric MRI and EHR data, offering a scalable fibrosis assessment approach suitable for opportunistic evaluation and as a complementary tool when MRE is unavailable.