Comparative analysis of deep learning and radiomics models in predicting hepatocellular carcinoma differentiation via ultrasound.

ABSTRACT:

Objective

This study aimed to develop and compare predictive models for hepatocellular carcinoma (HCC) differentiation using ultrasound-based radiomics and deep learning, and to evaluate the clinical utility of a combined model.

Methods

Radiomics and deep learning models were constructed from grayscale ultrasound images. A combined model integrating both approaches was developed. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Sensitivity, specificity, accuracy, and area under the curve (AUC) were compared, and statistical significance was evaluated with the DeLong test.

Results

The radiomics model achieved an AUC of 0.736 (95% CI: 0.578-0.893), while the deep learning model ac

SUBMITTER: Xu L 

PROVIDER: S-EPMC12511121 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature