Juan Zhou1, Lu-Yang Luo2, Qi Dou2, Hao Chen2,3, Cheng Chen2, Gong-Jie Li1, Ze-Fei Jiang4, Pheng-Ann Heng2. 1. Department of Radiology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, 10071, People's Republic of China. 2. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong. 3. Imsight Medical Technology Co., Ltd., Nanshan, Shenzhen, 518000, People's Republic of China. 4. Department of Breast Cancer, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China, 100071.
Abstract
BACKGROUND: The usefulness of 3D deep learning-based classification of breast cancer and malignancy localization from MRI has been reported. This work can potentially be very useful in the clinical domain and aid radiologists in breast cancer diagnosis. PURPOSE: To evaluate the efficacy of 3D deep convolutional neural network (CNN) for diagnosing breast cancer and localizing the lesions at dynamic contrast enhanced (DCE) MRI data in a weakly supervised manner. STUDY TYPE: Retrospective study. SUBJECTS: A total of 1537 female study cases (mean age 47.5 years ±11.8) were collected from March 2013 to December 2016. All the cases had labels of the pathology results as well as BI-RADS categories assessed by radiologists. FIELD STRENGTH/SEQUENCE: 1.5 T dynamic contrast-enhanced MRI. ASSESSMENT: Deep 3D densely connected networks were trained under image-level supervision to automatically classify the images and localize the lesions. The dataset was randomly divided into training (1073), validation (157), and testing (307) subsets. STATISTICAL TESTS: Accuracy, sensitivity, specificity, area under receiver operating characteristic curve (ROC), and the McNemar test for breast cancer classification. Dice similarity for breast cancer localization. RESULTS: The final algorithm performance for breast cancer diagnosis showed 83.7% (257 out of 307) accuracy (95% confidence interval [CI]: 79.1%, 87.4%), 90.8% (187 out of 206) sensitivity (95% CI: 80.6%, 94.1%), 69.3% (70 out of 101) specificity (95% CI: 59.7%, 77.5%), with the area under the curve ROC of 0.859. The weakly supervised cancer detection showed an overall Dice distance of 0.501 ± 0.274. DATA CONCLUSION: 3D CNNs demonstrated high accuracy for diagnosing breast cancer. The weakly supervised learning method showed promise for localizing lesions in volumetric radiology images with only image-level labels. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1144-1151.
BACKGROUND: The usefulness of 3D deep learning-based classification of breast cancer and malignancy localization from MRI has been reported. This work can potentially be very useful in the clinical domain and aid radiologists in breast cancer diagnosis. PURPOSE: To evaluate the efficacy of 3D deep convolutional neural network (CNN) for diagnosing breast cancer and localizing the lesions at dynamic contrast enhanced (DCE) MRI data in a weakly supervised manner. STUDY TYPE: Retrospective study. SUBJECTS: A total of 1537 female study cases (mean age 47.5 years ±11.8) were collected from March 2013 to December 2016. All the cases had labels of the pathology results as well as BI-RADS categories assessed by radiologists. FIELD STRENGTH/SEQUENCE: 1.5 T dynamic contrast-enhanced MRI. ASSESSMENT: Deep 3D densely connected networks were trained under image-level supervision to automatically classify the images and localize the lesions. The dataset was randomly divided into training (1073), validation (157), and testing (307) subsets. STATISTICAL TESTS: Accuracy, sensitivity, specificity, area under receiver operating characteristic curve (ROC), and the McNemar test for breast cancer classification. Dice similarity for breast cancer localization. RESULTS: The final algorithm performance for breast cancer diagnosis showed 83.7% (257 out of 307) accuracy (95% confidence interval [CI]: 79.1%, 87.4%), 90.8% (187 out of 206) sensitivity (95% CI: 80.6%, 94.1%), 69.3% (70 out of 101) specificity (95% CI: 59.7%, 77.5%), with the area under the curve ROC of 0.859. The weakly supervised cancer detection showed an overall Dice distance of 0.501 ± 0.274. DATA CONCLUSION: 3D CNNs demonstrated high accuracy for diagnosing breast cancer. The weakly supervised learning method showed promise for localizing lesions in volumetric radiology images with only image-level labels. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1144-1151.