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Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Using CNN Based on Multiparametric MRI
Zijian Wang1, Hang Sun1, Jing Li2
1College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
Background:
Multiparametric magnetic resonance imaging (MRI) is widely used in breast cancer screening. Accurate prediction of the axillary lymph nodes metastasis (ALNM) is essential for breast cancer surgery and treatment. However, there is no mature and effective discerning method for ALNM based on multiparametric MRI.
Purpose:
To evaluate the ALNM using T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI) sequences, respectively, and construct a quantitative ALNM discerning model of integrated multiparametric MRI.
Study Type:
Retrospective.
Population:
Three-hundred forty-eight breast cancer patients, 163 with ALNM (99.39% females), and 185 without ALNM (100% females). The dataset was randomly divided into the training set (315 cases) and the testing set (33 cases).
Field Strength/Sequence:
1.5 T; T1WI (VIBRANT), T2WI (FSE), and DWI (echo planar imaging [EPI]).
Assessment:
The lesion region of interest images were cropped and sent to a pretrained ResNet50 network. Then, the results of different sequences were sent to a classifier for ensemble learning to construct the ALNM model of multiparametric MRI.
Statistical Tests:
Performance indicators such as accuracy, the receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC) were calculated. Student's t-test, chi-square test, Fisher's exact test, and Delong test were performed, and P < 0.05 was considered statistically significant.
Results:
T2WI performed the best among the three sequences, and achieved the accuracy and AUC of 0.933/0.989 in the testing set. Compared to T1WI with the accuracy and AUC of 0.691/0.806, the increase is significant. While compared to DWI with the accuracy and AUC of 0.800/0.910, the improvement is not significant (P = 0.126). After integrating three sequences, the accuracy and AUC improved to 0.970 and 0.996.
Data Conclusion:
T2WI performed better than DWI and T1WI in discerning ALNM in this breast cancer dataset. The proposed quantitative model of integrated multiparametric MRI could effectively help the ALNM diagnosis.
Level Of Evidence:
1 TECHNICAL EFFICACY STAGE: 2.
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