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Full-Text Articles in Diagnosis
Prediction Models For Solitary Pulmonary Nodules Based On Curvelet Textural Features And Clinical Parameters, Jing-Jing Wang, Hai-Feng Wu, Tao Sun, Xia Li, Wei Wang, Li-Xin Tao, Da Huo, Ping-Xin Lv, Wen He, Xiu-Hua Guo
Prediction Models For Solitary Pulmonary Nodules Based On Curvelet Textural Features And Clinical Parameters, Jing-Jing Wang, Hai-Feng Wu, Tao Sun, Xia Li, Wei Wang, Li-Xin Tao, Da Huo, Ping-Xin Lv, Wen He, Xiu-Hua Guo
Research outputs 2013
Lung cancer, one of the leading causes of cancer-related deaths, usually appears as solitary pulmonary nodules (SPNs) which are hard to diagnose using the naked eye. In this paper, curvelet-based textural features and clinical parameters are used with three prediction models [a multilevel model, a least absolute shrinkage and selection operator (LASSO) regression method, and a support vector machine (SVM)] to improve the diagnosis of benign and malignant SPNs. Dimensionality reduction of the original curvelet-based textural features was achieved using principal component analysis. In addition, non-conditional logistical regression was used to find clinical predictors among demographic parameters and morphological features. …