AIM: To prospectively evaluate the usefulness of a pattern-based classification of contrast-enhanced sonographic findings for differential diagnosis of hepatic tumors. METHODS: We evaluated the enhancement pattern of ...AIM: To prospectively evaluate the usefulness of a pattern-based classification of contrast-enhanced sonographic findings for differential diagnosis of hepatic tumors. METHODS: We evaluated the enhancement pattern of the contrast-enhanced sonography images in 586 patients with 586 hepatic lesions, consisting of 383 hepatocellular carcinomas, 89 metastases, and 114 hemangiomas. After injecting a galactose-palmitic acid contrast agent, lesions were scanned by contrast- enhanced harmonic gray-scale sonography in three phases: arterial, portal, and late. 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. RESULTS: 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 forhemangioma. The sensitivity, specificity, and accuracy of prospective diagnosis based on the combinations of enhancement patterns, respectively, were 93.2%, 96.2%, and 94.0% for hepatocellular carcinoma, 87.9%, 99.6%, and 98.2% for metastasis, and 95.6%, 94.1%, and 94.3% for hemangioma. CONCLUSION: The pattern-based classification of the contrast-enhanced sonographic findings is useful for differentiating among hepatic tumors.展开更多
AIM:To study the role of time-intensity curve(TIC) analysis parameters in a complex system of neural networks designed to classify liver tumors.METHODS:We prospectively included 112 patients with hepatocellular carcin...AIM:To study the role of time-intensity curve(TIC) analysis parameters in a complex system of neural networks designed to classify liver tumors.METHODS:We prospectively included 112 patients with hepatocellular carcinoma(HCC)(n = 41),hypervascular(n = 20) and hypovascular(n = 12) liver metastases,hepatic hemangiomas(n = 16) or focal fatty changes(n = 23) who underwent contrast-enhanced ultrasonography in the Research Center of Gastroenterology and Hepatology,Craiova,Romania.We recorded full length movies of all contrast uptake phases and post-processed them offline by selecting two areas of interest(one for the tumor and one for the healthy surrounding parenchyma) and consecutive TIC analysis.The difference in maximum intensities,the time to reaching them and the aspect of the late/portal phase,as quantified by the neural network and a ratio between median intensities of the central and peripheral areas were analyzed by a feed forward back propagation multi-layer neural network which was trained to classify data into five distinct classes,corresponding to each type of liver lesion.RESULTS:The neural network had 94.45% training accuracy(95% CI:89.31%-97.21%) and 87.12% testing accuracy(95% CI:86.83%-93.17%).The automatic classification process registered 93.2% sensitivity,89.7% specificity,94.42% positive predictive value and 87.57% negative predictive value.The artificial neural networks(ANN) incorrectly classified as hemangyomas three HCC cases and two hypervascular metastases,while in turn misclassifying four liver hemangyomas as HCC(one case) and hypervascular metastases(three cases).Comparatively,human interpretation of TICs showed 94.1% sensitivity,90.7% specificity,95.11% positive predictive value and 88.89% negative predictive value.The accuracy and specificity of the ANN diagnosis system was similar to that of human interpretation of the TICs(P = 0.225 and P = 0.451,respectively).Hepatocellular carcinoma cases showed contrast uptake during the arterial phase followed by wash-out in the portal and first seconds of the late phases.For the hypovascular metastases did not show significant contrast uptake during the arterial phase,which resulted in negative differences between the maximum intensities.We registered wash-out in the late phase for most of the hypervascular metastases.Liver hemangiomas had contrast uptake in the arterial phase without agent wash-out in the portallate phases.The focal fatty changes did not show any differences from surrounding liver parenchyma,resulting in similar TIC patterns and extracted parameters.CONCLUSION:Neural network analysis of contrastenhanced ultrasonography-obtained TICs seems a promising field of development for future techniques,providing fast and reliable diagnostic aid for the clinician.展开更多
文摘AIM: To prospectively evaluate the usefulness of a pattern-based classification of contrast-enhanced sonographic findings for differential diagnosis of hepatic tumors. METHODS: We evaluated the enhancement pattern of the contrast-enhanced sonography images in 586 patients with 586 hepatic lesions, consisting of 383 hepatocellular carcinomas, 89 metastases, and 114 hemangiomas. After injecting a galactose-palmitic acid contrast agent, lesions were scanned by contrast- enhanced harmonic gray-scale sonography in three phases: arterial, portal, and late. 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. RESULTS: 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 forhemangioma. The sensitivity, specificity, and accuracy of prospective diagnosis based on the combinations of enhancement patterns, respectively, were 93.2%, 96.2%, and 94.0% for hepatocellular carcinoma, 87.9%, 99.6%, and 98.2% for metastasis, and 95.6%, 94.1%, and 94.3% for hemangioma. CONCLUSION: The pattern-based classification of the contrast-enhanced sonographic findings is useful for differentiating among hepatic tumors.
文摘AIM:To study the role of time-intensity curve(TIC) analysis parameters in a complex system of neural networks designed to classify liver tumors.METHODS:We prospectively included 112 patients with hepatocellular carcinoma(HCC)(n = 41),hypervascular(n = 20) and hypovascular(n = 12) liver metastases,hepatic hemangiomas(n = 16) or focal fatty changes(n = 23) who underwent contrast-enhanced ultrasonography in the Research Center of Gastroenterology and Hepatology,Craiova,Romania.We recorded full length movies of all contrast uptake phases and post-processed them offline by selecting two areas of interest(one for the tumor and one for the healthy surrounding parenchyma) and consecutive TIC analysis.The difference in maximum intensities,the time to reaching them and the aspect of the late/portal phase,as quantified by the neural network and a ratio between median intensities of the central and peripheral areas were analyzed by a feed forward back propagation multi-layer neural network which was trained to classify data into five distinct classes,corresponding to each type of liver lesion.RESULTS:The neural network had 94.45% training accuracy(95% CI:89.31%-97.21%) and 87.12% testing accuracy(95% CI:86.83%-93.17%).The automatic classification process registered 93.2% sensitivity,89.7% specificity,94.42% positive predictive value and 87.57% negative predictive value.The artificial neural networks(ANN) incorrectly classified as hemangyomas three HCC cases and two hypervascular metastases,while in turn misclassifying four liver hemangyomas as HCC(one case) and hypervascular metastases(three cases).Comparatively,human interpretation of TICs showed 94.1% sensitivity,90.7% specificity,95.11% positive predictive value and 88.89% negative predictive value.The accuracy and specificity of the ANN diagnosis system was similar to that of human interpretation of the TICs(P = 0.225 and P = 0.451,respectively).Hepatocellular carcinoma cases showed contrast uptake during the arterial phase followed by wash-out in the portal and first seconds of the late phases.For the hypovascular metastases did not show significant contrast uptake during the arterial phase,which resulted in negative differences between the maximum intensities.We registered wash-out in the late phase for most of the hypervascular metastases.Liver hemangiomas had contrast uptake in the arterial phase without agent wash-out in the portallate phases.The focal fatty changes did not show any differences from surrounding liver parenchyma,resulting in similar TIC patterns and extracted parameters.CONCLUSION:Neural network analysis of contrastenhanced ultrasonography-obtained TICs seems a promising field of development for future techniques,providing fast and reliable diagnostic aid for the clinician.
