Editorial for “Attention‐based Deep Learning for the Preoperative Differentiation of Axillary Lymph Node Metastasis in Breast Cancer on DCE‐MRI”

Breast cancer is the commonest cancer in US women and the second most important cause of death in women after lung carcinoma. Axillary lymph node metastasis (ALNM) is the commonest form of metastases in these patients. ALNM has very significant role in the evaluation of breast cancer staging, management of the therapy, and prognosis of patients, which forms an important index for postoperative, radiotherapy, and chemotherapy. The axillary lymph node (ALN) metastases along with the postoperative complications helps in the prediction of the diagnosis and the prognosis. Dynamic contrast enhanced (DCE) gives information on the vascular status of the tumor and also the malignant potential of the tumor. Recent studies have shown the handcrafted tumoral features with the help of the 2D DCE-MRI can evaluate nodal metastasis by the use of classical machine learning and also deep learning. The involvement of the lymph node will increase the chances of breast cancer recurrence. So for this, the accurate noninvasive evaluation of the nodal involvement is important for cancer staging, risk stratification, and also for prognosis. Radiomics has shown the important role of sentinel lymph node (SLN) status but still all techniques are supposed to be very sensitive to various acquisition parameters. The prediction model can be made and easily identify the preoperative prediction of SLN metastasis with the use of the deep learning based (DLB) features and comparing with radiomics. Deep learning has become a very important tool, which is capable of high-level learning and also various task-adaptive imaging features. It can also have better feature extraction from multiple levels without explicit definition, leading to very high level of feature abstraction. Deep learning can use the Convolutional neural networks (CNNs) to extract features by applying convolution layers, which are made up of very small-sized fields or kernels, leading to various pooling layers, which can reduce the size of the resultant feature space. This kernel will be applied for image extraction in the training of the network. Segmentation: Mass segmentation of large data in the image sequences due to significant workload of manual segmentation along with the existence of subjectivity. This will lead to early and improved segmentation function. Various segmentation processes like interactive and semiautomatic segmentation methods and techniques are used for the analysis of growth identified as pixels in various regions. Each region is divided by the seed pixel for the beginning of the lesion and similar pixels having similar properties and combined to form the region. This region will lead to the seed pixel of the surrounding structures, which will grow to meet the conditions, which can be included, and a region grows. Feature extraction and selection is an important basis for quantifying and expressing image information, correct extraction, and selection of effective radiomic signatures for evaluation of the ALN status. Classification can be done after the process of segmentation, feature extraction, and selection of the lymph node metastasis for prediction results and better lesion diagnosis. The three classifiers are the SVM classifier, KNN classifier, and LDA classifier, and these are important for the prediction of the ALNM of breast cancer. Thus, the use of various radiomic features of the primary tumor is extracted and analyzed by the use of the DCE-MRI by several machine learning methods.