Fusion of B-mode and shear wave elastography ultrasound features for automated detection of axillary lymph node metastasis in breast carcinoma

In this study, we evaluate and compare the diagnostic performance of ultrasound for non-invasive axillary lymph node (ALN) metastasis detection. The study was based on fusing shear wave elastography (SWE) and B-mode ultrasonography (USG) images. These images were subjected to pre-processing and feature extraction, based on bi-dimensional empirical mode decomposition and higher order spectra methods. The resulting nonlinear features were ranked according to their p-value, which was established with Student's t-test. The ranked features were used to train and test six classification algorithms with 10-fold cross-validation. Initially, we considered B-mode USG images in isolation. A probabilistic neural network (PNN) classifier was able to discriminate positive from negative cases with an accuracy of 74.77% using 15 features. Subsequently, only SWE images were used and as before, the PNN classifier delivered the best result with an accuracy of 87.85% based on 47 features. Finally, we combined SWE and B-mode USG images. Again, the PNN classifier delivered the best result with an accuracy of 89.72% based on 71 features. These three tests indicate that SWE images contain more diagnostically relevant information when compared with B-mode USG. Furthermore, there is scope in fusing SWE and B-mode USG to improve non-invasive ALN metastasis detection.