Editorial for “Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Using CNN Based on Multiparametric MRI”

Breast cancer is the most common cancer and the leading cause of cancer death among women worldwide. The axillary lymph node (ALN) is the initial and primary site for metastatic breast cancer, making ALN status important for breast cancer classification, prognosis, and tumor staging. ALN dissection is the gold standard methodology for detecting ALN metastasis (ALNM). Currently, there are no reliable presurgical methods for predicting ALNM. Complete ALN dissection is highly invasive and is the primary cause for postoperative complications. Consequently, there is a demand for noninvasive approaches for ALNM detection; however, noninvasive methods still cannot quantitatively and reliably predict ALMN. Even with qualitative MRI, subtle changes in tissues owing to micrometastasis in ALN cannot be visually distinguished from normal lymph nodes. Machine learning methods applied to qualitative MRI have facilitated significant progress in the diagnosis, treatment, and prognosis of ALNM. The most common approach involves extracting radiomics features and classifying using traditional machine learning algorithms, such as least absolute shrinkage and selection operator regression, support vector machine, or random forest. These processes exploit features such as the shape and size of ALN, fatty hilum, breast vascularity, and cortical thickness to predict ALNM; however, these algorithms do not perform well with large datasets or features, require carefully selecting features and kernels, and are highly sensitive to noise. Regarding image and feature classification, innovations in deep learning have improved both speed and accuracy. The application of convolutional neural networks (CNNs) in qualitative MRI has shown promising results. Although training CNNs is time and resource intensive, deployment requires little time or processing power and could be easily implemented in clinical settings. Current research in CNN classification of ALNM involves extracting features from MR slices or using preprocessed MR images. Several studies have employed variations of CNN to detect the presence of ALNM. The best deep learning models used multiparametric MRI with T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) MRI as the model input, resulting in an area under the receiver operating characteristic curve (AUC) as high as 0.910. Still, these studies had several limitations. The CNN model performs best when the sample size is adequate and there is high-resolution, noiseless MRI, both of which these studies were lacking. Moreover, as retrospective studies, unintentional sample selection bias was possible. In this issue of JMRI, Wang et al. propose a machine learning classification model based on multiparametric MRI to help diagnose ALNM in breast cancer patients. Based on multiparametric MRI, this study shows that the use of the integrated model approach can provide significantly better ALNM prediction than conventional single-sequence classification models previously studied. Three-hundred forty-eight breast cancer patients (163 with ALNM and 185 non-ALNM) completed MR scans of their lesions then underwent ALN dissection to verify the presence of ALNM. Two-dimensional MRI of the breast were cropped to extract the region of interest containing the tumor and peritumor, then scaled to 224 224 pixels. Each cropped slice was labeled as ALNM or non-ALNM. Using a ResNet50 CNN, the authors show that classification through ensemble learning of two parametric combinations of T1WI + T2WI (AUC = 0.993) and T1WI + T2WI + DWI (AUC = 0.996) exceeded the performance of an individual sequence alone (AUC = 0.806 for T1W1). Furthermore, the results from the classification of DWI scans alone (AUC = 0.910) demonstrate that noncontrast DWI is just as effective, compared to DCE in the study by Ren et al., in the detection of ALMN; this finding encourages the continued use of unenhanced DWI in the development of radiomic signatures for cancer detection. While the sample size is smaller than ideal for effective classification using CNN, the