Support Vector Machine Parameter Optimization for Positron Emission Tomography Images for Estimation of Recurrent Laryngeal Nerve Injury with Thyroid Nodules

Abstract

This study aimed to explore positron emission tomography-computed tomography (PET-CT) images based on support vector machine (SVM) algorithm for the classification of thyroid nodules (TN) and its evaluation value in postoperative injury rate (PPIR) of recurrent laryngeal nerve (RLN). The parameters of the SVM algorithm were optimized using the particle swarm optimization (PSO) algorithm. A total of 58 patients who were diagnosed with TN by PET/CT at a hospital were divided into a group with benign nodules (group B, 25 cases) and a group with malignant nodules (group M, 33 cases). The characteristics of the PET-CT images and difference in the max standardized uptake value (SUVmax) of PET-CT were analyzed. The PPIR of RLN was calculated. It was found that when the number of iterations was 19, the fitness and the classification accuracy of the SVM algorithm was 98.3% and 91.1%, respectively. When SUVmax = 4.56, its sensitivity and specificity were 81.33% and 76.18%, respectively. The SUVmax of group B was much lower ( $P<0.01$ ). It indicated that the established method could realize higher classification accuracy on TN and was of great significance in the evaluation of the PPIR of RLN.