A Predictive Model Study on Differentiating Intrahepatic Cholangiocarcinoma From Hepatocellular Carcinoma Using Contrast-Enhanced Ultrasound, Shear Wave Elastography, and Clinical Feature-Based Nomogram.

Objectives: This study sought to design and verify a nomogram that utilizes ultrasonographic and clinical indicators to differentiate between intrahepatic cholangiocarcinoma (ICC) and hepatocellular carcinoma (HCC).

Methods: From November 2022 to September 2024, 136 patients with confirmed ICC or HCC were enrolled and randomly assigned to training and validation groups in a 7:3 ratio. Preoperative B-mode ultrasound, contrast-enhanced ultrasound, two-dimensional shear wave elastography features, and clinical indicators were retrieved and compared. Least Absolute Shrinkage and Selection Operator regression and multivariate logistic regression analysis were used to identify independent factors and develop a predictive nomogram. The model's evaluation focused on discrimination, calibration, and clinical utility.

Results: Significant predictive factors for ICC include a history of hepatitis, levels of alpha-fetoprotein and carbohydrate antigen 19-9, rim-like arterial phase hyperenhancement, and the stiffness ratio between the lesion and liver parenchyma. With AUC values of 0.987 (95% CI: 0.969, 1.000) for the training set and 0.926 (95% CI: 0.813, 1.000) for the validation set, the nomogram exhibited strong differentiation capabilities between the two entities.

Conclusions: The nomogram combining multimodal indicators achieved high AUC values in both the validation and test sets (AUC = 0.926-0.987), demonstrating robust diagnostic accuracy for distinguishing ICC from HCC. This tool could aid in clinical decision-making for these challenging diagnoses.