Nomogram based on Sonazoid contrast-enhanced ultrasound to differentiate intrahepatic cholangiocarcinoma and poorly differentiated hepatocellular carcinoma: a prospective multicenter study.

OBJECTIVES

The aim of this study was to develop a predictive model based on Sonazoid contrast-enhanced ultrasound (SCEUS) and clinical features to discriminate poorly differentiated hepatocellular carcinoma (P-HCC) from intrahepatic cholangiocarcinoma (ICC).

PATIENTS AND METHOD

Forty-one ICC and forty-nine P-HCC patients were enrolled in this study. The CEUS LI-RADS category was assigned according to CEUS LI-RADS version 2017. Based on SCEUS and clinical features, a predicated model was established. Multivariate logistic regression analysis and LASSO logistic regression were used to identify the most valuable features, 400 times repeated 3-fold cross-validation was performed on the nomogram model and the model performance was determined by its discrimination, calibration, and clinical usefulness.

RESULTS

Multivariate logistic regression and LASSO logistic regression indicated that age (> 51 y), viral hepatitis (No), AFP level (≤  20 µg/L), washout time (≤  45 s), and enhancement level in the Kupffer phase (Defect) were valuable predictors related to ICC. The area under the receiver operating characteristic (AUC) of the nomogram was 0.930 (95% CI: 0.856-0.973), much higher than the subjective assessment by the sonographers and CEUS LI-RADS categories. The calibration curve showed that the predicted incidence was more consistent with the actual incidence of ICC, and 400 times repeated 3-fold cross-validation revealed good discrimination with a mean AUC of 0.851. Decision curve analysis showed that the nomogram could increase the net benefit for patients.

CONCLUSIONS

The nomogram based on SCEUS and clinical features can effectively differentiate P-HCC from ICC.