Bag of deep features for preoperative prediction of sentinel lymph node metastasis in breast cancer

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Abstract
Breast cancer is the most common female malignancy among women. Sentinel lymph node (SLN) status is a crucial prognostic factor for breast cancer. In this paper, we propose an integrated scheme of deep learning and bag-of-features (BOF) model for preoperative prediction of SLN metastasis. Specifically, convolution neural networks (CNNs) are used to extract deep features from the three 2D representative orthogonal views of a segmented 3D volume of interest. Then, we use a BOF model to furtherly encode the all deep features, which makes features more compact and products high-dimension sparse representation. In particular, a kernel fusion method that assembles all features is proposed to build a discriminative support vector machine (SVM) classifier. The bag of deep feature model is evaluated using the diffusion-weighted magnetic resonance imaging (DWI) database of 172 patients, including 74 SLN and 98 non-SLN. The results show that the proposed method achieves area under the curve (AUC) as high as 0.852 (95% confidence interval (CI): 0.716–0.988) at test set. The results demonstrate that the proposed model can potentially provide a noninvasive approach for automatically predicting prediction of SLN metastasis in patients with breast cancer.
Funding Information
  • Science and Technology Program of Guangzhou (201607010097)
  • Guangdong Provincial Key Laboratory of Medical Image Processing (2014B030301042)
  • Science and Technology Program of Guangdong Province (2017A020211012)
  • National Natural Science Foundation of China (61671230, 31271067)

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