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
AIM:To automate breast cancer diagnosis and to study the inter-observer and intra-observer variations in the manual evaluations.METHODS:Breast tissue specimens from sixty cases were stained separately for estrogen rec...AIM:To automate breast cancer diagnosis and to study the inter-observer and intra-observer variations in the manual evaluations.METHODS:Breast tissue specimens from sixty cases were stained separately for estrogen receptor(ER),progesterone receptor(PR)and human epidermal growth factor receptor-2(HER-2/neu).All cases were assessed by manual grading as well as image analysis.The manual grading was performed by an experienced expert pathologist.To study inter-observer and intra-observer variations,we obtained readings from another pathologist as the second observer from a different laboratory who has a little less experience than the first observer.We also took a second reading from the second observer to study intra-observer variations.Image analysis was carried out using in-house developed software(TissueQuant).A comparison of the results from image analysis and manual scoring of ER,PR and HER-2/neu was also carried out.RESULTS:The performance of the automated analysis in the case of ER,PR and HER-2/neu expressions was compared with the manual evaluations.The performance of the automated system was found to correlate well with the manual evaluations.The inter-observer variations were measured using Spearman correlation coefficient r and 95%confidence interval.In the case of ER expression,Spearman correlation r=0.53,in the case of PR expression,r=0.63,and in the case of HER-2/neu expression,r=0.68.Similarly,intra-observer variations were also measured.In the case of ER,PR and HER-2/neu expressions,r=0.46,0.66 and 0.70,respectively.CONCLUSION:The automation of breast cancer diagnosis from immunohistochemically stained specimens is very useful for providing objective and repeatable evaluations.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 different observations, the diagnostic performance of computer-aided analysis for differentiating breast masses did not change. Through continual retraining, the computer-aided analysis provides consistent diagnostic performance independent of the variations in the observed sonographic features.展开更多
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AIM:To automate breast cancer diagnosis and to study the inter-observer and intra-observer variations in the manual evaluations.METHODS:Breast tissue specimens from sixty cases were stained separately for estrogen receptor(ER),progesterone receptor(PR)and human epidermal growth factor receptor-2(HER-2/neu).All cases were assessed by manual grading as well as image analysis.The manual grading was performed by an experienced expert pathologist.To study inter-observer and intra-observer variations,we obtained readings from another pathologist as the second observer from a different laboratory who has a little less experience than the first observer.We also took a second reading from the second observer to study intra-observer variations.Image analysis was carried out using in-house developed software(TissueQuant).A comparison of the results from image analysis and manual scoring of ER,PR and HER-2/neu was also carried out.RESULTS:The performance of the automated analysis in the case of ER,PR and HER-2/neu expressions was compared with the manual evaluations.The performance of the automated system was found to correlate well with the manual evaluations.The inter-observer variations were measured using Spearman correlation coefficient r and 95%confidence interval.In the case of ER expression,Spearman correlation r=0.53,in the case of PR expression,r=0.63,and in the case of HER-2/neu expression,r=0.68.Similarly,intra-observer variations were also measured.In the case of ER,PR and HER-2/neu expressions,r=0.46,0.66 and 0.70,respectively.CONCLUSION:The automation of breast cancer diagnosis from immunohistochemically stained specimens is very useful for providing objective and repeatable evaluations.
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 different observations, the diagnostic performance of computer-aided analysis for differentiating breast masses did not change. Through continual retraining, the computer-aided analysis provides consistent diagnostic performance independent of the variations in the observed sonographic features.
