Background/Objectives: The aim was to assess whether a machine learning (ML) algorithm could empower the ability of ultrasound (US) integrated with shear-wave elastography (SWE) to preoperatively define the ALN status in breast cancer (BC). Methods: Patients with at least one histologically proven BC lesion, who underwent preoperative breast US and SWE were retrospectively enrolled. BC lesions were segmented on US and SWE images by three different operators and radiomics features were extracted. A multi-step US and SWE feature selection was performed. A Simple Logistic ML classifier was applied to the dataset to predict the ALN status, its performance assessed through the AUC and Matthews Correlation Coefficient (MCC). The performance of the ML classifier was compared to that of an expert radiologist, who evaluated the US B-mode lymph-node features included in the test set. Results: A total of 133 BC lesions were included and divided into a training set, composed of 89 BC lesions (ALN−: 52; ALN+: 37), and a test set, including 44 BC lesions (ALN−: 24; ALN+: 20). Eight features out of the 1098 radiomics features extracted from US and SWE images were selected to build the predictive model. Simple Logistic classifier showed AUC of 0.685 and 0.677, MCC of 0.387 and 0.375 in the training and test set, respectively. The performance of the expert radiologist was higher than that of the ML classifier (AUC = 0.817), but not significantly different (p = 0.481). Conclusions: The inclusion of SWE-derived radiomics features could aid in the preoperative assessment of ALN status in BC using an ML approach.
Caruso et al. (Sat,) studied this question.