Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning.
Microcalcification is an effective indicator of early breast cancer. To improve the diagnostic accuracy of microcalcifications, this study evaluates the performance of deep learning-based models on large datasets for its discrimination. A semi-automated segmentation method was used to characterize all microcalcifications. A discrimination classifier model was constructed to assess the accuracies of microcalcifications and breast masses, either in isolation or combination, for classifying breast lesions. Performances were compared to benchmark models. Our deep learning model achieved a discriminative accuracy of 87.3% if microcalcifications were characterized alone, compared to 85.8% with a support vector machine. The accuracies were 61.3% for both methods with masses alone and improved to 89.7% and 85.8% after the combined analysis with microcalcifications. Image segmentation with our deep learning model yielded 15, 26 and 41 features for the three scenarios, respectively. Overall, deep learning based on large datasets was superior to standard methods for the discrimination of microcalcifications. Accuracy was increased by adopting a combinatorial approach to detect microcalcifications and masses simultaneously. This may have clinical value for early detection and treatment of breast cancer.
Department of Radiology, Affiliated Nanhai Hospital of Southern Medical University, Foshan 528200, Guangdong, China.
Jinhua
J
Wang
School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, Guangdong, China.
Xi
X
Yang
School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, Guangdong, China.
Hongmin
H
Cai
Department of Radiology, Affiliated Nanhai Hospital of Southern Medical University, Foshan 528200, Guangdong, China.
Wanchang
W
Tan
Department of Radiology, Affiliated Nanhai Hospital of Southern Medical University, Foshan 528200, Guangdong, China.
Cangzheng
C
Jin
Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, Guangdong, China.
Li
L
Li
D000328:Adult; D000368:Aged; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology*; D002114:Calcinosis / Q000000981:diagnostic imaging* / Q000473:pathology*; D002681:China; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008327:Mammography / Q000379:methods*; D008875:Middle Aged; D012680:Sensitivity and Specificity