EUS is the most sensitive imaging procedure for the detection of small solid pancreatic masses and is accurate in determining vascular invasion of the portal venous system. Even compared to the new CT-techniques EUS p...EUS is the most sensitive imaging procedure for the detection of small solid pancreatic masses and is accurate in determining vascular invasion of the portal venous system. Even compared to the new CT-techniques EUS provides excellent results in preoperative staging of solid pancreatic tumors. Compared to helical CT-techniques EUS is less accurate in detecting tumor involvement of superior mesenteric artery. EUS staging and EUS-guided FNA can be performed in a single-step procedure, to establish the diagnosis of cancer. There is no known negative impact of tumor cell seeding due to EUS-FNA. Without FNA EUS and additional methods are not able to reliably distinguish between inflammatory and malignant masses.展开更多
AIM: To evaluate the ultrasonog raphy (EUS) features of gastric gastrointestinal stromal tumors (GISTs) as compared with gastric leiomyomas and then to determine the EUS features that could predict malignant GISTs.MET...AIM: To evaluate the ultrasonog raphy (EUS) features of gastric gastrointestinal stromal tumors (GISTs) as compared with gastric leiomyomas and then to determine the EUS features that could predict malignant GISTs.METHODS: We evaluated the endoscopic EUS features in 53 patients with gastric mesenchymal tumors conf irmed by histopathologic diagnosis. The GISTs were classif ied into benign and malignant groups according to the histological risk classif ication.RESULTS: Immunohistochemical analyses demon-strated 7 leiomyomas and 46 GISTs. Inhomogenicity, hyperechogenic spots, a marginal halo and higher echogenicity as compared with the surrounding muscle layer appeared more frequently in the GISTs than in the leiomyomas (P < 0.05). The presence of at least two of these four features had a sensitivity of 89.1% and a specifi city of 85.7% for predicting GISTs. Except for tumor size and irregularity of the border, most of the EUS features were not helpful for predicting the malignant potential of GISTs. On multivariate analysis, only the maximal diameter of the GISTs was an independent predictor. The optimal size for predicting malignant GISTs was 35 mm. The sensitivity and specificity using this value were 92.3% and 78.8%, respectively.CONCLUSION: EUS may help to differentiate gastric GISTs from gastric leiomyomas. Once GISTs are suspected, surgery should be considered if the size is greater than 3.5 cm.展开更多
Objective: To analyze the non-periodic, unstable and even chaotic echoes scattered from microbubbles which are extremely sensitive and may easily collapse, fragment or shrink when ultrasound contrast agents are expose...Objective: To analyze the non-periodic, unstable and even chaotic echoes scattered from microbubbles which are extremely sensitive and may easily collapse, fragment or shrink when ultrasound contrast agents are exposed to ultrasound (US) irradiation. Methods: The combined time-frequency analysis was applied to the original signals instead of the traditional Fourier spectral analysis technique. Results: The results obtained from simulation as well as experiment showed that the subharmonic, 2nd harmonic and ultra harmonic of the microbubbles occurred during the oscillation and varied with time. The dependence on the incident ultrasonic amplitude and microbubble parameters were established. Conclusion: The transient echoes backscattered from the ultrasound agent in the evaluation of the blood perfusion can be analyzed thoroughly by the technique of combined-frequency analysis and the time detail of the frequency contents can be revealed.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
Objective To assess the value of transvaginal CDFI in the diagnosis of malignant ovariantumors and to discriminate the benign from malignant ovarian tumors. Methods 96 patients pelvic masses were studied by transvagin...Objective To assess the value of transvaginal CDFI in the diagnosis of malignant ovariantumors and to discriminate the benign from malignant ovarian tumors. Methods 96 patients pelvic masses were studied by transvaginal ultrasonograph mass scoring, and by CDFI through pulsatility index (PI) and resistance index (RI) of peripheral blood flow of mass analyses. Results Mass ultrasonic scoring : scoring >9, 91.9% masses were malignant tumors. Scoring ≤<9, 91.5% masses were benign tumors. PI and Rl of peripheral blood flow of masses were detected by CDFI: Pl< 1 .0 . 92.5% cases; RI<0.5, 97.4% cases were malignant tumors. The diagnosis match rate of malignant ovarian tumors was 97.0% . The diagnosis match rate of benign ovarian tumor was 96 .2% . The diagnosis match rate was improved. Conclusion Ovarian mass ultrasonic scoring>9 and PI< 1 .0 , RI<0 .5 of mass peripheral blood flow are the special ultrasonic features of malignant ovarian tumor and the better methods to distinguish the benign ovarian tumor from the malignant one.展开更多
文摘EUS is the most sensitive imaging procedure for the detection of small solid pancreatic masses and is accurate in determining vascular invasion of the portal venous system. Even compared to the new CT-techniques EUS provides excellent results in preoperative staging of solid pancreatic tumors. Compared to helical CT-techniques EUS is less accurate in detecting tumor involvement of superior mesenteric artery. EUS staging and EUS-guided FNA can be performed in a single-step procedure, to establish the diagnosis of cancer. There is no known negative impact of tumor cell seeding due to EUS-FNA. Without FNA EUS and additional methods are not able to reliably distinguish between inflammatory and malignant masses.
基金Supported by A Medical Research Institute Grant (2008-1)Pusan National University and a grant from the National R&D Program for Cancer Control, Ministry for Health, Welfare and Family affairs, Republic of Korea (0920050)
文摘AIM: To evaluate the ultrasonog raphy (EUS) features of gastric gastrointestinal stromal tumors (GISTs) as compared with gastric leiomyomas and then to determine the EUS features that could predict malignant GISTs.METHODS: We evaluated the endoscopic EUS features in 53 patients with gastric mesenchymal tumors conf irmed by histopathologic diagnosis. The GISTs were classif ied into benign and malignant groups according to the histological risk classif ication.RESULTS: Immunohistochemical analyses demon-strated 7 leiomyomas and 46 GISTs. Inhomogenicity, hyperechogenic spots, a marginal halo and higher echogenicity as compared with the surrounding muscle layer appeared more frequently in the GISTs than in the leiomyomas (P < 0.05). The presence of at least two of these four features had a sensitivity of 89.1% and a specifi city of 85.7% for predicting GISTs. Except for tumor size and irregularity of the border, most of the EUS features were not helpful for predicting the malignant potential of GISTs. On multivariate analysis, only the maximal diameter of the GISTs was an independent predictor. The optimal size for predicting malignant GISTs was 35 mm. The sensitivity and specificity using this value were 92.3% and 78.8%, respectively.CONCLUSION: EUS may help to differentiate gastric GISTs from gastric leiomyomas. Once GISTs are suspected, surgery should be considered if the size is greater than 3.5 cm.
文摘Objective: To analyze the non-periodic, unstable and even chaotic echoes scattered from microbubbles which are extremely sensitive and may easily collapse, fragment or shrink when ultrasound contrast agents are exposed to ultrasound (US) irradiation. Methods: The combined time-frequency analysis was applied to the original signals instead of the traditional Fourier spectral analysis technique. Results: The results obtained from simulation as well as experiment showed that the subharmonic, 2nd harmonic and ultra harmonic of the microbubbles occurred during the oscillation and varied with time. The dependence on the incident ultrasonic amplitude and microbubble parameters were established. Conclusion: The transient echoes backscattered from the ultrasound agent in the evaluation of the blood perfusion can be analyzed thoroughly by the technique of combined-frequency analysis and the time detail of the frequency contents can be revealed.
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.
文摘Objective To assess the value of transvaginal CDFI in the diagnosis of malignant ovariantumors and to discriminate the benign from malignant ovarian tumors. Methods 96 patients pelvic masses were studied by transvaginal ultrasonograph mass scoring, and by CDFI through pulsatility index (PI) and resistance index (RI) of peripheral blood flow of mass analyses. Results Mass ultrasonic scoring : scoring >9, 91.9% masses were malignant tumors. Scoring ≤<9, 91.5% masses were benign tumors. PI and Rl of peripheral blood flow of masses were detected by CDFI: Pl< 1 .0 . 92.5% cases; RI<0.5, 97.4% cases were malignant tumors. The diagnosis match rate of malignant ovarian tumors was 97.0% . The diagnosis match rate of benign ovarian tumor was 96 .2% . The diagnosis match rate was improved. Conclusion Ovarian mass ultrasonic scoring>9 and PI< 1 .0 , RI<0 .5 of mass peripheral blood flow are the special ultrasonic features of malignant ovarian tumor and the better methods to distinguish the benign ovarian tumor from the malignant one.
