Analysis of microvasculature morphological features for prediction of metastatic lymph nodes in breast cancer patients via contrast-free ultrasound imaging

Abstract

Axillary lymph nodes (ALNs) are generally among the first to be impacted by the metastatic spread of breast tumors. Consequently, evaluating their condition is of great importance as the removal of affected ALNs can potentially curtail the further spread of cancer cells to other parts of the body. Conventional ultrasound imaging suffers from low sensitivity and specificity and does not provide a comprehensive picture of the status of the node. In this work, we utilize an ultrasound microvessel imaging technique to visualize vascular structures within ALNs and, through several image processing steps, characterize the morphology of these structures. Several morphological metrics are used as biomarkers to differentiate between reactive and metastatic lymph nodes of patients with suspicious ALNs. A group of 68 patients is included in this study. Pathology examination results are used as the gold-standard reference for data labeling. A support vector machine (SVM) model is trained, and its performance is evaluated. Statistical analysis of the discriminative potential of individual biomarkers and receiver operating characteristic (ROC) curve analysis of the trained model are presented. Preliminary results suggest that a sensitivity of 0.88, specificity of 0.88, and area under the curve of 0.94 are achievable. Work supported by NIH-R01CA239548.