Differential diagnosis of hepatic tumors by pattern-based classification according to Bayes theorem

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We prospectively evaluated the usefulness of a pattern-based classification of contrast-enhanced US according to Bayes theorem for differential diagnosis of hepatic tumors. After injecting a galactose-palmitic acid contrast agent, the enhancement patterns of the initial 303 lesions were classified retrospectively, and multiple logistic regression analysis was used to identify enhancement patterns that allowed differentiation between hepatic tumors. We then used the pattern-based classification of enhancement we had retrospectively devised to prospectively diagnose 283 liver tumors. Seven enhancement patterns were found to be significant predictors of different hepatic tumors. The presence of homogeneous or heterogeneous enhancement both in the arterial and portal phase was the typical enhancement pattern for hepatocellular carcinoma, while the presence of peritumoral vessels in the arterial phase and ring enhancement or a perfusion defect in the portal phase was the typical enhancement pattern for metastases, and the presence of peripheral nodular enhancement both in the arterial and portal phase was the typical enhancement pattern for hemangioma. The sensitivity, specificity, and accuracy of prospective diagnosis based on the combinations of enhancement patterns were 93.2%, 96.2%, and 94.0%, respectively, for hepatocellular carcinoma, 87, 9%, 99.6%, and 98.2%, for metastasis, and 95.6%, 94.1%, and 94.3%, for hemangioma. The pattern-based classification of the contrast-enhanced US findings is useful for differentiating among hepatic tumors.