The aim of this study is to develop a computer-aided diagnosis method to help classify medical images of neck lymph nodes in head and neck cancer patients. According to the current practice guidelines, the classification of lymph node status is critical for patient stratification before treatment. Take extra-nodal extension (ENE) of metastatic neck lymph nodes, the status of ENE has been considered a single factor affecting the decision of whether systemic treatment with toxicity should be given to patients with otherwise non-advanced cancer status. Medical imaging prior to surgery serves as tools for clinical staging and determining the extent of neck lymph node dissection during the tumor resection surgery. The information contained in these images may also help determine the status of ENE and thus stratify patients for more precise treatment. In the current practice, there has been not a reliable computer-aided tool for this task. In this study, we used open-source software to investigate radiomic features that help distinguish malignant from benign and ENE from non-ENE lymph nodes. We have identified 89 features that can differentiate malignant from benign and 4 features that can differentiate ENE from non-ENE lymph nodes. Furthermore, we fed the significant features to a multilayer perceptron neural network to predict malignancy and ENE of lymph nodes and achieved 84% and 77% of accuracy in each task, respectively.
Keywords: Head and neck cancer; Lymph nodes; Magnetic resonance imaging; Radiomics.