Deep Learning Method with a Convolutional Neural Network for Image Classification of Normal and Metastatic Axillary Lymph Nodes on Breast Ultrasonography

Purpose: To investigate the ability of deep learning (DL) using convolutional neural networks (CNNs) for distinguishing between normal and metastatic axillary lymph nodes on ultrasound images by comparing the diagnostic performance of radiologists.

Materials And Methods: We retrospectively gathered 300 images of normal and 328 images of axillary lymph nodes with breast cancer metastases for training. A DL model using the CNN architecture Xception was developed to analyze test data of 50 normal and 50 metastatic lymph nodes. A board-certified radiologist with 12 years' experience. (Reader 1) and two residents with 3- and 1-year experience (Readers 2, 3), respectively, scored these test data with and without the assistance of the DL system for the possibility of metastasis. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated.

Results: Our DL model had a sensitivity of 94%, a specificity of 88%, and an AUC of 0.966. The AUC of the DL model was not significantly different from that of Reader 1 (0.969; p = 0.881) and higher than that of Reader 2 (0.913; p = 0.101) and Reader 3 (0.810; p < 0.001). With the DL support, the AUCs of Readers 2 and 3 increased to 0.960 and 0.937, respectively, which were comparable to those of Reader 1 (p = 0.138 and 0.700, respectively).

Conclusion: Our DL model demonstrated great diagnostic performance for differentiating benign from malignant axillary lymph nodes on breast ultrasound and for potentially providing effective diagnostic support to residents.