Diagnosis of Distant Metastasis of Lung Cancer: Based on Clinical and Radiomic Features

• Main Authors: Hongyu Zhou, Di Dong, Bojiang Chen, Mengjie Fang, Yue Cheng, Yuncun Gan, Rui Zhang, Liwen Zhang, Yali Zang, Zhenyu Liu, Hairong Zheng, Weimin Li, Jie Tian
• Format: Article
• Language: English
• Published: Elsevier 2018-02-01
• Series: Translational Oncology
• Online Access: http://www.sciencedirect.com/science/article/pii/S1936523317303200
• ISSN: 1936-5233; 1944-7124

OBJECTIVES: To analyze the distant metastasis possibility based on computed tomography (CT) radiomic features in patients with lung cancer. METHODS: This was a retrospective analysis of 348 patients with lung cancer enrolled between 2014 and February 2015. A feature set containing clinical features and 485 radiomic features was extracted from the pretherapy CT images. Feature selection via concave minimization (FSV) was used to select effective features. A support vector machine (SVM) was used to evaluate the predictive ability of each feature. RESULTS: Four radiomic features and three clinical features were obtained by FSV feature selection. Classification accuracy by the proposed SVM with SGD method was 71.02%, and the area under the curve was 72.84% with only the radiomic features extracted from CT. After the addition of clinical features, 89.09% can be achieved. CONCLUSION: The radiomic features of the pretherapy CT images may be used as predictors of distant metastasis. And it also can be used in combination with the patient's gender and tumor T and N phase information to diagnose the possibility of distant metastasis in lung cancer.