Introduction: Contrast-enhanced sonography is a useful tool for evaluating the vascularity of liver
tumors, because it allows visualization of the blood perfusion. Moreover, the method
is simple, easy, and minimal invasive, being usually performed on an out-patient basis.
Aim: The objective of this study was to evaluate the liver masses using microbubble contrast-enhanced
sonography (using a second generation contrast agent).
Subjects and methods: Thirty lesions were evaluated with dynamic contrast harmonic imaging in wideband
pulse inversion mode comprising 22 malignancies (14 hepatocellular carcinomas, 8 metastases)
and 8 benign lesions (hemangiomas). The size of the focal liver lesions ranged from
1 to 11cm. In the 8 cases of metastatic cancer, the primarily affected organ was the
colon (n=4), stomach (2), pancreas (n=1) and one gall bladder (n=1). Scanning was
performed using a Hitachi 8500 scanner with a convex 2–5MHz transducer. Final diagnosis
was determined with a combination between imaging investigation (ultrasound, computer
tomography, endoscopic ultrasound), histopathology (liver biopsy in 2 HCCs and endoscopic
biopsy in patients with gastrointestinal cancer and liver metastases), as well as
a follow-up for at least six months. All perfusion phases of the liver lesions (arterial,
portal-venous and late phase) were evaluated.
Results: Based on the literature, contrast enhancement patterns of different lesions were
summarized and defined a diagnosis algorithm for the characterisation of liver masses.
Peripheral nodular or rimlike enhancement in arterial phase, with centripetal filling
in portal and late phases was considered to be characteristic for hemangiomas. Hypervascular
metastases had enhancement in arterial phase, while absence or peripheral enhancement
in arterial and portal phases was observed in hyovascular metastases. HCC lesions
were hypervascular in arterial phase, becoming iso- or hypoechoic in portal and late
phases. Mean accuracy for the detection and characterization of focal liver lesions
based on the proposed algorithm was higher than 90%.
Conclusion: The pattern-based classification of the contrast-enhanced sonographic findings is
clearly useful for the differentiatiation of liver tumors.