Differentiation of Suspicious Microcalcifications Using Deep Learning: DCIS or IDC.
Authors
Affiliations (9)
Affiliations (9)
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China (W.X., S.D., G.M., C.Z.). Electronic address: [email protected].
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China (W.X., S.D., G.M., C.Z.). Electronic address: [email protected].
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China (W.X., S.D., G.M., C.Z.). Electronic address: [email protected].
- The Second Clinical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China (N.W., Y.H., S.W.). Electronic address: [email protected].
- The Second Clinical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China (N.W., Y.H., S.W.). Electronic address: [email protected].
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China (W.X., S.D., G.M., C.Z.). Electronic address: [email protected].
- The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China (G.S.). Electronic address: [email protected].
- The Second Clinical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China (N.W., Y.H., S.W.). Electronic address: [email protected].
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China (Y.A.). Electronic address: [email protected].
Abstract
To explore the value of a deep learning-based model in distinguishing between ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) manifesting suspicious microcalcifications on mammography. A total of 294 breast cancer cases (106 DCIS and 188 IDC) from two centers were randomly allocated into training, internal validation and external validation sets in this retrospective study. Clinical variables differentiating DCIS from IDC were identified through univariate and multivariate analyses and used to build a clinical model. Deep learning features were extracted using Resnet101 and selected by minimum redundancy maximum correlation (mRMR) and least absolute shrinkage and selection operator (LASSO). A deep learning model was developed using deep learning features, and a combined model was constructed by combining these features with clinical variables. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of each model. Multivariate logistic regression identified lesion type and BI-RADS category as independent predictors for differentiating DCIS from IDC. The clinical model incorporating these factors achieved an AUC of 0.67, sensitivity of 0.53, specificity of 0.81, and accuracy of 0.63 in the external validation set. In comparison, the deep learning model showed an AUC of 0.97, sensitivity of 0.94 and specificity of 0.92, accuracy of 0.93. For the combined model, the AUC, sensitivity, specificity and accuracy were 0.97, 0.96, 0.92 and 0.95, respectively. The diagnostic efficacy of the deep learning model and combined model was comparable (p>0.05), and both models outperformed the clinical model (p<0.05). Deep learning provides an effective non-invasive approach to differentiate DCIS from IDC presenting as suspicious microcalcifications on mammography.