Zhou, Hongyu, Dong, Di, Chen, Bojiang, Fang, Mengjie, Cheng, Yue 
ORCID: 0000-0003-4338-3788, Gan, Yuncun, Zhang, Rui, Zhang, Liwen, Zang, Yali, Liu, Zhenyu et al (show 3 more authors)
(2018)
Diagnosis of Distant Metastasis of Lung Cancer: Based on Clinical and Radiomic Features.
TRANSLATIONAL ONCOLOGY, 11 (1).
pp. 31-36.
Abstract
<h4>Objectives</h4>To analyze the distant metastasis possibility based on computed tomography (CT) radiomic features in patients with lung cancer.<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusion</h4>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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Lung, Cancer, Lung Cancer, 4.1 Discovery and preclinical testing of markers and technologies, 4 Detection, screening and diagnosis, Cancer |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 23 Jan 2019 11:39 |
| Last Modified: | 13 Apr 2024 20:02 |
| DOI: | 10.1016/j.tranon.2017.10.010 |
| Open Access URL: | https://reader.elsevier.com/reader/sd/pii/S1936523... |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3031670 |
Dimensions
Dimensions
