Published online Dec 21, 2026. doi: 10.3748/wjg.121435
Revised: June 16, 2026
Accepted: July 6, 2026
Published online: December 21, 2026
Processing time: 216 Days and 3.8 Hours
The hepatic venous pressure gradient (HVPG) is critical for the assessment of portal hypertension, but measuring the HVPG is invasive and costly. Current radiomics machine learning prediction models still exhibit certain limitations in clinical practice.
To investigate a new transformer-based regression model for predicting the HVPG in patients with chronic liver diseases non-invasively.
This retrospective study randomly divided eligible patients from September 2017 to August 2023 into the training, internal testing and temporally independent validation sets. Computed tomography images from four sections were pre-processed and then input into the transformer network combined with transfer learning strategy for feature extraction, regression analysis and prediction of the HVPG values. The prediction and diagnostic performance of our model was investigated and compared with that of other non-invasive prediction methods across distinct HVPG thresholds.
The original datasets included 228 patients for training, 57 for internal testing, and 74 for temporally independent validation sets. The predictive HVPGs were strongly correlated with (r = 0.949, 0.741 and 0.717, P < 0.001) and showed acceptable agreement with the actual HVPGs in all the datasets. The overall prediction accuracies of our model for binary, ternary, and quinary HVPG classification intervals were 90.8%, 80.4% and 64.0%, respectively. For different binary classification intervals (HVPG > 5, ≥ 10, ≥ 12, ≥ 16, and ≥ 20 mmHg), the majority of area under the receiver operating characteristic curve in the three datasets for our model prediction of the HVPG exceeded 0.90, which outperformed conventional non-invasive HVPG prediction methods.
A new regression machine learning model based on the transformer network has been developed to be promising for non-invasively predicting the HVPG across the entire spectrum of portal venous pressure.
Core Tip: This retrospective study developed and validated a non-invasive regression machine learning model to predict the hepatic venous pressure gradient values in chronic liver diseases. Utilizing the transformer network combined with transfer learning based on contrast-enhanced computed tomography images from four pivotal sections, our model efficiently predicted the entire spectrum of portal venous pressure (from 1 to 37 mmHg) and showed good diagnostic performance for clinically significant hepatic venous pressure gradient thresholds including 5, 10, 12, 16 and 20 mmHg, outperforming conventional non-invasive methods. Moreover, the time-saving approach in this study minimized region of interest delineation workload, demonstrating potential for routine clinical application.