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

根据贝叶斯定理，我们前瞻性地评估了基于模式的对比增强超声分类对肝脏肿瘤鉴别诊断的有用性。在注射半乳糖-棕榈酸造影剂后，对最初303个病变的增强模式进行回顾性分类，并采用多元logistic回归分析，以识别能够区分肝脏肿瘤的增强模式。然后，我们使用我们回顾性设计的基于模式的增强分类对283例肝脏肿瘤进行前瞻性诊断。七种增强模式被发现是不同肝脏肿瘤的重要预测因子。肝细胞癌的典型强化模式为动脉期和门脉期均质或异质性强化，转移性肝癌的典型强化模式为动脉期肿瘤周围血管的存在，门脉期呈环形强化或灌注缺损，血管瘤的典型强化模式为动脉期和门脉期的周围结节性强化。基于增强模式组合的前瞻性诊断的敏感性、特异性和准确性对肝细胞癌的敏感性、特异性和准确性分别为93.2%、96.2%和94.0%，对转移的敏感性、特异性和准确性分别为87%、9%、99.6%和98.2%，对血管瘤的敏感性、特异性和准确性分别为95.6%、94.1%和94.3%。基于模式的对比增强的美国发现的分类是有用的区分肝脏肿瘤。

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.