Objective: To facilitate the prediction of characteristics of SPNs in CT of lungs to diagnose early-stage lung cancer. Methods: All data was acquired from a cohort study. The synthetic minority over-sampling technique (SMOTE) was used to account for raw data in order to balance the original training data set. Input data, which included Curvelettransformation textural features, together with 3 patient demographic characteristics, and 9 morphological features were used to establish a SVM machine prediction model. Longitudinal data as the test data set was used to evaluate the classification performance of predicting early-stage lung cancer. Results: Using the SMOTE as a pre-processing procedure, the original training data was balanced with a ratio of malignant to benign cases of 1:1. Accuracy based on cross-evaluation (f=5) for the original unbalanced data and balanced data was 80％ and 97％, respectively. Based on Curvelettransformation textural features and other features, the SVM prediction model had good classification performance for early-stage lung cancer, with an area under the curve of the SVMs of 0.949 (P < 0.001). In addition to this, we found the textural feature (standard deviation) for benign cases had a higher change in the follow-up period than malignant cases. Conclusions: With textural features extracted from a Curvelet transformation and other parameters, a support vector machine prediction model sensitive to early-stage lung cancer can increase the rate of diagnosis for early-stage lung cancer. This scheme can be used as an auxiliary tool to differentiate between benign and malignant early-stage lung cancers in CT images.
Curvelet transformation support vector machine longitudinal data early-stagelungcancer
Tao Sun Regina Zhang Haifeng Wu Xia Li Zhigang Liang WenHe MD Lei Zhang Yueming He Xiuhua Guo
School of Public Health and Family Medicine,Capital MedicalUniversity,Beijing,100069,China College of Arts and Sciences,Emory University,Atlanta,30322,USA School of Public Health and Family Medicine,Capital Medical University,Beijing,100069,China Department of Radiology,Xuan Wu Hospital,Capital Medical University,Beijing,100050,China Department of Radiology,Friendship Hospital,Capital Medical University,Beijing,100053,China department of Radiology,Chao Yang Hospital,Capital Medical University,Beijing,100020,China department of Radiology,Fu Xing Hospital,Capital Medical University,Beijing,100038,China School of Public Health and Family Medicine,Capital Medical University,Beijing,100069,China Beijing