Title: Diagnosis of Distant Metastasis of Lung Cancer: Based on Clinical and Radiomic Features
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| Authors: Zhou, HY; Dong, D; Chen, BJ; Fang, MJ; Cheng, Y; Gan, YC; Zhang, R; Zhang, LW; Zang, YL; Liu, ZY; Zheng, HR; Li, WM; Tian, J
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| Author Full Names: Zhou, Hongyu; Dong, Di; Chen, Bojiang; Fang, Mengjie; Cheng, Yue; Gan, Yuncun; Zhang, Rui; Zhang, Liwen; Zang, Yali; Liu, Zhenyu; Zheng, Hairong; Li, Weimin; Tian, Jie
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| Source: TRANSLATIONAL ONCOLOGY, 11 (1):31-36; 10.1016/j.tranon.2017.10.010 FEB 2018
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| Language: English
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| Abstract: 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.
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| ISSN: 1936-5233
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| IDS Number: FT9EM
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| Unique ID: WOS:000423454900005
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