ABSTRACT
Objective@#To assess the diagnostic performance of contrast-enhanced spectral mammography (CESM) in suspected breast lesions.@*Methods@#A total of 97 patients with suspected breast cancer identified by clinical examination or screening underwent two-views CESM examination on the basis of digital breast tomosynthesis (DBT) combined with full-field digital mammography (FFDM), and they were finally confirmed by biopsy or pathology. Three senior radiologists analyzed images, including lesion visibility, lesion characteristics, enhancement type, degree of enhancement, BIRDS classification, etc. Finally, based on the pathology, we compared the CESM+DBT+FFDM and DBT+FFDM two models according to sensitivity, specificity and ROC for diagnostic performance.@*Results@#There were a total of 120 lesions. Eighty-nine lesions were malignant, 31 benign; CESM was not enhanced in 2 cases, mild enhancement was performed in 22 cases, moderately intensive in 15 cases, highly intensive in 81 cases, and 2 cases were not enhanced; mass-enhanced in 96 cases, including ring-enhanced in 12 cases, 22 cases of non-mass type. The sensitivities of the combination of CESM and not combination of CESM were 91.0% and 80.9%, respectively, and the specificities were 93.5% and 87.1%, respectively. The area under the ROC curve of combination of CESM was higher than the without combination of CESM (0.923 and 0.900, P<0.05), The difference was statistically significant.@*Conclusion@#For suspicious lesions, CESM examination can improve the diagnosis accuracy of breast cancer.
ABSTRACT
Objective To assess the diagnostic performance of contrast-enhanced spectral mammography (CESM) in suspected breast lesions. Methods A total of 97 patients with suspected breast cancer identified by clinical examination or screening underwent two-views CESM examination on the basis of digital breast tomosynthesis (DBT) combined with full-field digital mammography (FFDM), and they were finally confirmed by biopsy or pathology. Three senior radiologists analyzed images, including lesion visibility, lesion characteristics, enhancement type, degree of enhancement, BIRDS classification, etc. Finally, based on the pathology, we compared the CESM+DBT+FFDM and DBT+FFDM two models according to sensitivity, specificity and ROC for diagnostic performance. Results There were a total of 120 lesions. Eighty-nine lesions were malignant, 31 benign; CESM was not enhanced in 2 cases, mild enhancement was performed in 22 cases, moderately intensive in 15 cases, highly intensive in 81 cases, and 2 cases were not enhanced; mass-enhanced in 96 cases, including ring-enhanced in 12 cases, 22 cases of non-mass type. The sensitivities of the combination of CESM and not combination of CESM were 91.0% and 80.9%, respectively, and the specificities were 93.5% and 87.1%, respectively. The area under the ROC curve of combination of CESM was higher than the without combination of CESM (0.923 and 0.900, P<0.05), The difference was statistically significant. Conclusion For suspicious lesions, CESM examination can improve the diagnosis accuracy of breast cancer.
ABSTRACT
Objective To evaluate the diagnostic performance of digital breast tomosynthesis (DBT) breast X-ray photography image texture characteristics based deep learning classification model on differentiating malignant masses. Methods Retrospectively collected 132 cases with simplex breast lesions (89 benign lesions and 43 malignant lesions) which were confirmed by pathology and DBT during January 2016 to December 2016 in Nanfang Hospital. DBT was performed before biopsy and surgery. Image of cranio-caudal view (CC) and medio-lateral oblique (MLO) were captured. The lesion area was segmented to acquire ROI by ITK-SNAP software. Then the processed images were input into MATLAB R2015b to establish a feature model for extracting texture features. The characteristics with high correlation was analyzed from Fisher score and one sample t test. We built up support vector machine (SVM) classification model based on extracted texture and added neural network model (CNN) for deep learning classification model. We randomly assigned collected cases into training group and validation group. The diagnosis of benign and malignant lesions were served as the reference. The efficiency was evaluated by ROC classification model. Result We extracted 82 texture characteristics from 132 images of leisure (132 images of CC and 132 images of MLO) by establishing deep learning classification model of breast lesions. We randomly chose and combined characteristics from 15 texture characteristics with statistical significance, then differentiated benign and malignant by SVM classification model. After 50 iterations on each combination of characteristics, the average diagnostic efficacy was compared to obtained the one with higher efficacy. Nine of CC and 8 of MLO was selected. The result showed that the sensitivity, specificity, accuracy and area under curve (AUC) of the model to differentiate simplex breast lesions for CC were 0.68, 0.77, 0.74 and 0.74, for MLO were 0.71, 0.71, 0.71 and 0.76. Conclusions MLO has better diagnostic performance for the diagnosis than CC. The deep learning classification model on breast lesions which was built upon DBT image texture characteristics on MLO could differentiate malignant masses effectively.