目的探讨S-Detect技术联合弹性成像辅助常规超声鉴别诊断乳腺肿瘤良恶性的价值。方法回顾性分析2018年4月至2020年1月在汕头大学医学院第一附属医院超声检查的136例乳腺肿块患者的超声诊断资料,对超声科诊断为乳腺影像学报告和数据系统...目的探讨S-Detect技术联合弹性成像辅助常规超声鉴别诊断乳腺肿瘤良恶性的价值。方法回顾性分析2018年4月至2020年1月在汕头大学医学院第一附属医院超声检查的136例乳腺肿块患者的超声诊断资料,对超声科诊断为乳腺影像学报告和数据系统3类及以上的乳腺病灶,依次采用常规超声、应变式弹性成像应变率比值法(strain ratio,SR)、S-Detect检查技术进行横断面研究,并获得相应的良恶性判断结果,比较分析各单独诊断及联合诊断的效能。结果常规超声、SR、S-Detect单独及常规超声+SR、常规超声+S-Detect、常规超声+S-Detect+SR联合诊断乳腺肿瘤等六种方法的受试者工作曲线下面积(area under the receiver operating characteristic curve,AUC)分别为0.776、0.839、0.802、0.861、0.832和0.870。SR、S-Detect、常规超声+SR、常规超声+S-Detect常规超声+SR+S-Detect与常规超声组间比较,差异具有统计学意义(Z值分别为1.49、0.70、2.76、2.52、2.96,P值分别为0.137、0.484、0.006、0.012、0.003)。其中以常规超声+S-Detect+SR联合方法的准确度最高,为84.1%;与病理结果比较,其Kappa值为0.687,一致性最强。结论S-Detect联合应变式弹性成像技术辅助常规超声能显著提高乳腺肿瘤良恶性的诊断效能。展开更多
Breast cancer is one of the most common and deadliest types of cancer among women and early detection is of major importance to decrease mortality rates. Microcalcification clusters and masses are two major indicators...Breast cancer is one of the most common and deadliest types of cancer among women and early detection is of major importance to decrease mortality rates. Microcalcification clusters and masses are two major indicators of malignancy in the early stages of this disease, when mammography is typically used as the screening technology. Computer-Aided Diagnosis (CAD) systems can support the radiologists’ work, by performing a double-reading process, which provides a second opinion that the physician can take into account in the detection process. This paper presents a CAD model based on computer vision procedures for locating suspicious regions that are later analyzed by artificial neural networks, support vector machines and linear discriminant analysis, to classify them into benign or malignant, based on a set of features that are extracted from lesions to characterize their visual content. A genetic algorithm is used to find the subset of features that provide the greatest discriminant power. Our results show that the SVM presented the highest overall accuracy and specificity for classifying microcalcification clusters, while the NN outperformed the rest for mass-classification in the same parameters. Overall accuracy, sensitivity and specificity were measured.展开更多
Objective: Computer classification of sonographic BI-RADS features can aid differentiation of the malignant and benign masses. However, the variability in the diagnosis due to the differences in the observed features ...Objective: Computer classification of sonographic BI-RADS features can aid differentiation of the malignant and benign masses. However, the variability in the diagnosis due to the differences in the observed features between the observations is not known. The goal of this study is to measure the variation in sonographic features between multiple observations and determine the effect of features variation on computer-aided diagnosis of the breast masses. Materials and Methods: Ultrasound images of biopsy proven solid breast masses were analyzed in three independent observations for BI-RADS sonographic features. The BI-RADS features from each observation were used with Bayes classifier to determine probability of malignancy. The observer agreement in the sonographic features was measured by kappa coefficient and the difference in the diagnostic performances between observations was determined by the area under the ROC curve, Az, and interclass correlation coefficient. Results: While some features were repeatedly observed, κ = 0.95, other showed a significant variation, κ = 0.16. For all features, combined intra-observer agreement was substantial, κ = 0.77. The agreement, however, decreased steadily to 0.66 and 0.56 as time between the observations increased from 1 to 2 and 3 months, respectively. Despite the variation in features between observations the probabilities of malignancy estimates from Bayes classifier were robust and consistently yielded same level of diagnostic performance, Az was 0.772-0.817 for sonographic features alone and 0.828-0.849 for sonographic features and age combined. The difference in the performance, ΔAz, between the observations for the two groups was small (0.003-0.044) and was not statistically significant (p < 0.05). Interclass correlation coefficient for the observations was 0.822 (CI: 0.787-0.853) for BI-RADS sonographic features alone and for those combined with age was 0.833 (CI: 0.800-0.862). Conclusion: Despite the differences in the BI-RADS sonographic features between dif展开更多
Objective To develop a computer-aided diagnosis(CAD)system with automatic contouring and morphologic and textural analysis to aid on the classification of breast nodules on ultrasound images.Methods A modified Level S...Objective To develop a computer-aided diagnosis(CAD)system with automatic contouring and morphologic and textural analysis to aid on the classification of breast nodules on ultrasound images.Methods A modified Level Set method was proposed to automatically segment the breast nodules(46malignant and 60benign nodules).Following,16morphologic features and 17texture features from the extracted contour were calculated and principal component analysis(PCA)was applied to find the optimal feature vector dimensions.Fuzzy C-means classifier was utilized to identify the breast nodule as benign or malignant with selected principal vectors.Results The performance of morphologic features was 78.30%for accuracy,67.39%for sensitivity and 86.67%for specificity,while the latter was 72.64%,58.70%and 83.33%,respectively.After the combination of the two features,the result was exactly the same with the morphologic performance.Conclusion This system performs well in classifying the malignant breast nodule from the benign breast nodule.展开更多
文摘目的探讨S-Detect技术联合弹性成像辅助常规超声鉴别诊断乳腺肿瘤良恶性的价值。方法回顾性分析2018年4月至2020年1月在汕头大学医学院第一附属医院超声检查的136例乳腺肿块患者的超声诊断资料,对超声科诊断为乳腺影像学报告和数据系统3类及以上的乳腺病灶,依次采用常规超声、应变式弹性成像应变率比值法(strain ratio,SR)、S-Detect检查技术进行横断面研究,并获得相应的良恶性判断结果,比较分析各单独诊断及联合诊断的效能。结果常规超声、SR、S-Detect单独及常规超声+SR、常规超声+S-Detect、常规超声+S-Detect+SR联合诊断乳腺肿瘤等六种方法的受试者工作曲线下面积(area under the receiver operating characteristic curve,AUC)分别为0.776、0.839、0.802、0.861、0.832和0.870。SR、S-Detect、常规超声+SR、常规超声+S-Detect常规超声+SR+S-Detect与常规超声组间比较,差异具有统计学意义(Z值分别为1.49、0.70、2.76、2.52、2.96,P值分别为0.137、0.484、0.006、0.012、0.003)。其中以常规超声+S-Detect+SR联合方法的准确度最高,为84.1%;与病理结果比较,其Kappa值为0.687,一致性最强。结论S-Detect联合应变式弹性成像技术辅助常规超声能显著提高乳腺肿瘤良恶性的诊断效能。
文摘Breast cancer is one of the most common and deadliest types of cancer among women and early detection is of major importance to decrease mortality rates. Microcalcification clusters and masses are two major indicators of malignancy in the early stages of this disease, when mammography is typically used as the screening technology. Computer-Aided Diagnosis (CAD) systems can support the radiologists’ work, by performing a double-reading process, which provides a second opinion that the physician can take into account in the detection process. This paper presents a CAD model based on computer vision procedures for locating suspicious regions that are later analyzed by artificial neural networks, support vector machines and linear discriminant analysis, to classify them into benign or malignant, based on a set of features that are extracted from lesions to characterize their visual content. A genetic algorithm is used to find the subset of features that provide the greatest discriminant power. Our results show that the SVM presented the highest overall accuracy and specificity for classifying microcalcification clusters, while the NN outperformed the rest for mass-classification in the same parameters. Overall accuracy, sensitivity and specificity were measured.
文摘Objective: Computer classification of sonographic BI-RADS features can aid differentiation of the malignant and benign masses. However, the variability in the diagnosis due to the differences in the observed features between the observations is not known. The goal of this study is to measure the variation in sonographic features between multiple observations and determine the effect of features variation on computer-aided diagnosis of the breast masses. Materials and Methods: Ultrasound images of biopsy proven solid breast masses were analyzed in three independent observations for BI-RADS sonographic features. The BI-RADS features from each observation were used with Bayes classifier to determine probability of malignancy. The observer agreement in the sonographic features was measured by kappa coefficient and the difference in the diagnostic performances between observations was determined by the area under the ROC curve, Az, and interclass correlation coefficient. Results: While some features were repeatedly observed, κ = 0.95, other showed a significant variation, κ = 0.16. For all features, combined intra-observer agreement was substantial, κ = 0.77. The agreement, however, decreased steadily to 0.66 and 0.56 as time between the observations increased from 1 to 2 and 3 months, respectively. Despite the variation in features between observations the probabilities of malignancy estimates from Bayes classifier were robust and consistently yielded same level of diagnostic performance, Az was 0.772-0.817 for sonographic features alone and 0.828-0.849 for sonographic features and age combined. The difference in the performance, ΔAz, between the observations for the two groups was small (0.003-0.044) and was not statistically significant (p < 0.05). Interclass correlation coefficient for the observations was 0.822 (CI: 0.787-0.853) for BI-RADS sonographic features alone and for those combined with age was 0.833 (CI: 0.800-0.862). Conclusion: Despite the differences in the BI-RADS sonographic features between dif
文摘Objective To develop a computer-aided diagnosis(CAD)system with automatic contouring and morphologic and textural analysis to aid on the classification of breast nodules on ultrasound images.Methods A modified Level Set method was proposed to automatically segment the breast nodules(46malignant and 60benign nodules).Following,16morphologic features and 17texture features from the extracted contour were calculated and principal component analysis(PCA)was applied to find the optimal feature vector dimensions.Fuzzy C-means classifier was utilized to identify the breast nodule as benign or malignant with selected principal vectors.Results The performance of morphologic features was 78.30%for accuracy,67.39%for sensitivity and 86.67%for specificity,while the latter was 72.64%,58.70%and 83.33%,respectively.After the combination of the two features,the result was exactly the same with the morphologic performance.Conclusion This system performs well in classifying the malignant breast nodule from the benign breast nodule.
