Ultrasound Elastography Radiomics as a Predictor of Posthepatectomy Liver Failure

Objectives: To develop a radiomics‑based predictive model for posthepatectomy liver failure (PHLF) using liver shear-wave elastography (SWE) and to evaluate its performance against conventional clinical indicators. Methods: This retrospective study enrolled 948 patients with pathologically confirmed hepatocellular carcinoma who underwent perioperative laboratory tests and SWE at the Third Affiliated Hospital of Naval Medical University (Eastern Hepatobiliary Surgery Hospital) between December 2021 and March 2025. Patients were randomly divided into training (n = 664) and validation (n = 284) cohorts (7:3 ratio). Radiomics features were extracted from SWE images of tumor and adjacent paratumor regions. Four models—tumor rad‑signature, paratumor rad‑signature, clinical, and conjunctive (integrating radiomics and clinical features)—were constructed using these features alongside clinical variables. Multiple machine learning algorithms were applied, and all model selection, including algorithm comparison and hyperparameter tuning, was performed exclusively via cross‑validation on the training cohort; the validation cohort was used only for independent performance evaluation. Results: The locked tumor rad-signature and paratumor rad-signature models yielded area under the curve values of 0.846 and 0.824, respectively, in the independent validation cohort. Multivariate logistic regression identified total protein, prealbumin, prothrombin time, paratumor elasticity value, and transfusion as independent predictors of PHLF. The conjunctive model achieved area under the curve of 0.939 in the training cohort and 0.886 in the independent validation cohort. Calibration curves showed good agreement between predicted and observed probabilities, and a decision curve analysis suggested a potential clinical net benefit in both cohorts. Conclusions: Radiomics models based on liver SWE demonstrated promising predictive ability for PHLF, suggesting a potential role of tissue elasticity in risk stratification. Although this approach may offer value for perioperative risk assessment and preliminary decision support for perioperative management, the analyses should be considered exploratory, and further external validation is necessary before any clinical implementation.