Ultrasound-based Radiomics Predicts Short-term Outcomes in Hepatitis B Virus-related Acute-on-chronic Liver Failure.

BACKGROUND

The prognosis in hepatitis B virus-associated acute-on-chronic liver failure (HBV-ACLF) is challenging due to heterogeneity. Radiomics may enable noninvasive outcome prediction.

OBJECTIVE

This study aimed to evaluate ultrasound-based radiomics for predicting outcomes in HBV-ACLF.

METHODS

We enrolled 264 HBV-ACLF patients, dividing them into a training cohort (n=184) and a validation cohort (n=80). From hepatic ultrasound images, 455 radiomic features were extracted. Radiomics-based phenotypes were identified through unsupervised hierarchical clustering. A radiomic signature was developed using a Cox-LASSO algorithm to predict 30-day mortality. Furthermore, we integrated the signature with independent clinical predictors via multivariate Cox regression to construct a combined clinical-radiomic nomogram (CCR-nomogram). Integrated discrimination improvement (IDI) and net reclassification improvement (NRI) assessed performance improvements achieved by adding radiomic features to clinical data.

RESULTS

Both clustering and radiomic signature identified two distinct subgroups with significant differences in clinical characteristics and 30-day prognosis. In the training cohort, the signature achieved a C-index of 0.746, replicated in validation with a C-index of 0.747. The CCR-nomogram achieved C-indices of 0.834 and 0.819 for the training and validation cohorts. Incorporating radiomic features significantly improved the CCRnomogram over the signature and clinical-only models, evidenced by IDI of 0.108-0.264 and NRI of 0.292-0.540 in both cohorts (all p0.05).

CONCLUSION

Ultrasound-based radiomics offered prognostic information complementary to clinical data and demonstrated potential to enhance outcome prediction in HBV-ACLF.