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Zhou H, Dong D, Chen B, Fang M, Cheng Y, Gan Y, Zhang R, Zhang L, Zang Y, Liu Z, Zheng H, Li W, Tian J. Diagnosis of Distant Metastasis of Lung Cancer: Based on Clinical and Radiomic Features. Transl Oncol 2018; 11:31-36. [PMID: 29156383 PMCID: PMC5697996 DOI: 10.1016/j.tranon.2017.10.010] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2017] [Revised: 10/24/2017] [Accepted: 10/26/2017] [Indexed: 02/05/2023] Open
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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Affiliation(s)
- Hongyu Zhou
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Ave., SZ University Town, Shenzhen, China, 518055; CAS Key Lab of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing, China
| | - Di Dong
- CAS Key Lab of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing, China
| | - Bojiang Chen
- Department of respiratory and critical care medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Mengjie Fang
- CAS Key Lab of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Yue Cheng
- Department of respiratory and critical care medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Yuncun Gan
- Department of respiratory and critical care medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Rui Zhang
- Department of respiratory and critical care medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Liwen Zhang
- CAS Key Lab of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Yali Zang
- CAS Key Lab of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Zhenyu Liu
- CAS Key Lab of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Hairong Zheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Ave., SZ University Town, Shenzhen, China, 518055.
| | - Weimin Li
- Department of respiratory and critical care medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China.
| | - Jie Tian
- CAS Key Lab of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing, China.
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