A Support Vector Machine based Prediction Model for Discrimination of Malignant Pulmonary Nodules from Benign Nodules

Lung cancer is the leading cause of cancer death in the United States and worldwide. Most patients are diagnosed at an advanced stage, usually stage III or IV. Identification of lung cancer patients at an early stage might enable oncologists to surgically remove the tumors. Currently, low dose CT scans are used to identify the malignant nodules in high risk patients. However, screening CT scans yield a high rate of false-positive results. A prediction model was developed for improved discrimination of malignant nodules from benign nodules in patients who underwent lung screening CT. CT images and clinical outcomes of 39 patients were obtained from the National Lung Screening Trial (NLST), National Cancer Institute, National Institute of Health. Images were analyzed to extract computational features relevant to malignancy prediction. A Support Vector Machine (SVM) based model was developed to predict the malignancy of nodules. During pilot studies, our model achieved the following prediction performance: accuracy of 0.74, sensitivity of 0.85, and specificity of 0.61.