Published online May 28, 2026. doi: 10.3748/wjg.v32.i20.112559
Revised: January 20, 2026
Accepted: February 25, 2026
Published online: May 28, 2026
Processing time: 166 Days and 0.5 Hours
Liver inflammatory lesions and malignancies often have overlapping manifestations and contrast-enhanced ultrasound (CEUS) enhancement modes, particularly in hepatitis or cirrhosis settings. Conventional biomarkers, such as alpha-fetoprotein (AFP) and the Model for End-Stage Liver Disease (MELD) score, have limited diagnostic accuracy in this setting. Therefore, an interpretable, multi-parameter CEUS-based machine learning (ML) tool may improve differential diagnosis and support clinical decision-making.
To construct a ML model based on multi-parameter features and clinical variables of CEUS for differential diagnosis of inflammatory liver lesions and malignancies, and to evaluate its diagnostic efficiency and interpretability.
From January 2018 to November 2023, 621 cases of liver lesions diagnosed by pathology or clinical follow-up were retrospectively evaluated, including 306 cases of inflammatory lesions and 315 cases of malignancies. Based on a 6:2:2 ratio, the cases were assigned to training, validation, and test sets. Their basic information, disease history, CEUS parameters, and laboratory indicators were collected. We constructed five ML models, namely, Logistic regression (LR), decision tree, random forest, XGBoost, and support vector machine (SVM), based on the mlr3 framework in R. Cross-validation and grid search helped achieve hyperparameter optimization, with area under the curve (AUC) as the major optimization goal. Accuracy, AUC, sensitivity, and specificity were determined for model performance evaluation, while interpretability analyzed using the SHapley Additive exPlanations (SHAP) method. Moreover, model performance was compared with traditional diagnostic indexes, AFP score, and MELD score.
Malignant and inflammatory lesion groups were markedly different in lesion morphology, uniform enhancement, cirrhosis, hepatitis, blood flow signals, calcification, age, lesion size, wash-in/out time, AFP score, and MELD score (all P < 0.001). During testing, LR and SVM performed best, with accuracy reaching 94.35%. The AUCs of LR on the training, validation, and test sets were 0.957, 0.958, and 0.965, respectively, superior to the AFP score (AUC = 0.908, 0.890, and 0.917, respectively) and MELD score (AUC = 0.844, 0.855, and 0.840, respectively; all P < 0.05). SHAP analysis showed that blood flow signals and wash-in time had the most significant influence on model prediction. The model stability evaluation indicated that LR had the best generalization ability (smallest overall stability difference: 0.00738).
The CEUS-based multi-parametric nomogram model shows excellent performance in the differential diagnosis of liver inflammatory lesions and malignant tumors, which is significantly superior to that of traditional biomarkers.
Core Tip: We developed and externally tested (within an independent test set) a multi-parametric contrast-enhanced ultrasound-based, SHapley Additive exPlanations-interpretable machine learning nomogram to differentiate hepatic inflammatory lesions from malignant tumors. Logistic regression showed the best overall generalization (area under the curve = 0.965; accuracy = 94.35%) and significantly outperformed alpha-fetoprotein and Model for End-Stage Liver Disease scores. Blood flow signals and wash-in time, as the key driving factors of model decision-making, can offer an intuitive and clinically relevant diagnostic reference basis.