Deep Learning vs. Radiomics for Predicting Axillary Lymph Node Metastasis of Breast Cancer Using Ultrasound Images: Don't Forget the Peritumoral Region

Abstract

Objective: Axillary lymph node (ALN) metastasis status is important in guiding treatment in breast cancer. The aims were to assess how deep convolutional neural network (CNN) performed compared with radiomics analysis in predicting ALN metastasis using breast ultrasound, and to investigate the value of both intratumoral and peritumoral regions in ALN metastasis prediction. Methods: We retrospectively enrolled 479 breast cancer patients with 2,395 breast ultrasound images. Based on the intratumoral, peritumoral, and combined intra- and peritumoral regions, three CNNs were built using DenseNet, and three radiomics models were built using random forest, respectively. By combining the molecular subtype, another three CNNs and three radiomics models were built. All models were built on training cohort (343 patients 1,715 images) and evaluated on testing cohort (136 patients 680 images) with ROC analysis. Another prospective cohort of 16 patients was enrolled to further test the models. Results: AUCs of image-only CNNs in both training/testing cohorts were 0.957/0.912 for combined region, 0.944/0.775 for peritumoral region, and 0.937/0.748 for intratumoral region, which were numerically higher than their corresponding radiomics models with AUCs of 0.940/0.886, 0.920/0.724, and 0.913/0.693. The overall performance of image-molecular CNNs in terms of AUCs on training/testing cohorts slightly increased to 0.962/0.933, 0.951/0.813, and 0.931/0.794, respectively. AUCs of both CNNs and radiomics models built on combined region were significantly better than those on either intratumoral or peritumoral region on the testing cohort (p < 0.05). In the prospective study, the CNN model built on combined region achieved the highest AUC of 0.95 among all image-only models. Conclusions: CNNs showed numerically better overall performance compared with radiomics models in predicting ALN metastasis in breast cancer. For both CNNs and radiomics models, combining intratumoral, and peritumoral regions achieved significantly better performance.

Keywords: axillary lymph node metastasis; breast cancer; breast ultrasound; deep learning; peritumoral region; radiomics.

Figure 1

Examples of ultrasound slices overlapped with intratumoral regions (green) and peritumoral regions (red) from two patients. (Top) A patient with positive ALN. (Bottom) A patient with negative ALN.

Figure 2

The architecture of the deep CNN used in our study.

Figure 3

The ROC curves of the three image-only deep CNNs and the three image-only radiomics models in both training and testing cohorts. (A) ROC curves of image-only CNNs in training cohort. (B) ROC curves of image-only CNNs in testing cohort. (C) ROC curves of image-only radiomics models in training cohort. (D) ROC curves of image-only radiomic models in testing cohort.

Figure 4

The ROC curves of the three image-molecular deep CNNs and the three image-molecular radiomics models in both training and testing cohorts. (A) ROC curves of image-molecular CNNs in training cohort. (B) ROC curves of image-molecular CNNs in testing cohort. (C) ROC curves of image-molecular radiomics models in training cohort. (D) ROC curves of image-molecular radiomic models in testing cohort.