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

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

doi:10.1016/j.tranon.2017.10.010

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

Transl Oncol. 2018 Feb;11(1):31-36. doi: 10.1016/j.tranon.2017.10.010. Epub 2017 Nov 20.

Authors

Hongyu Zhou¹, Di Dong², Bojiang Chen³, Mengjie Fang⁴, Yue Cheng³, Yuncun Gan³, Rui Zhang³, Liwen Zhang⁴, Yali Zang⁴, Zhenyu Liu⁴, Hairong Zheng⁵, Weimin Li⁶, Jie Tian⁷

Affiliations

¹ 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.
² CAS Key Lab of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing, China.
³ Department of respiratory and critical care medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China.
⁴ CAS Key Lab of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
⁵ 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. Electronic address: hr.zheng@siat.ac.cn.
⁶ Department of respiratory and critical care medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China. Electronic address: weimin003@163.com.
⁷ CAS Key Lab of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing, China. Electronic address: tian@ieee.org.

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.