CASIA OpenIR
Development of a radiomics nomogram based on the 2D and 3D CT features to predict the survival of non-small cell lung cancer patients
Yang, Lifeng1; Yang, Jingbo1; Zhou, Xiaobo2; Huang, Liyu1; Zhao, Weiling2; Wang, Tao3; Zhuang, Jian4; Tian, Jie5
Source PublicationEUROPEAN RADIOLOGY
ISSN0938-7994
2019-05-01
Volume29Issue:5Pages:2196-2206
Corresponding AuthorHuang, Liyu(huangly@mail.xidian.edu.cn) ; Tian, Jie(jie.tian@ia.ac.cn)
AbstractObjectivesThe aim of this study was to develop a radiomics nomogram by combining the optimized radiomics signatures extracted from 2D and/or 3D CT images and clinical predictors to assess the overall survival of patients with non-small cell lung cancer (NSCLC).MethodsOne training cohort of 239 and two validation datasets of 80 and 52 NSCLC patients were enrolled in this study. Nine hundred seventy-five radiomics features were extracted from each patient's 2D and 3D CT images. Least absolute shrinkage and selection operator (LASSO) regression was used to select features and generate a radiomics signature. Cox hazard survival analysis and Kaplan-Meier were performed in both cohorts. The radiomics nomogram was developed by integrating the optimized radiomics signature and clinical predictors, its calibration and discrimination were evaluated.ResultsThe radiomics signatures were significantly associated with NSCLC patients' survival time. The signature derived from the combined 2D and 3D features showed a better prognostic performance than those from 2D or 3D alone. Our radiomics nomogram integrated the optimal radiomics signature with clinical predictors showed a significant improvement in the prediction of patients' survival compared with clinical predictors alone in the validation cohort. The calibration curve showed predicted survival time was very close to the actual one.ConclusionsThe radiomics signature from the combined 2D and 3D features further improved the predicted accuracy of survival prognosis for the patients with NSCLC. Combination of the optimal radiomics signature and clinical predictors performed better for individualied survival prognosis estimation in patients with NSCLC. These findings might affect trearment strategies and enable a step forward for precise medicine.Key Points center dot We found both 2D and 3D radiomics signature have favorable prognosis, but 3D signature had a better performance.center dot The radiomics signature generated from the combined 2D and 3D features had a better predictive performance than those from 2D or 3D features.center dot Integrating the optimal radiomics signature with clinical predictors significantly improved the predictive power in patients' survival compared with clinical TNM staging alone.
KeywordNon-small cell lung cancer Radiomics Tomography x-ray computed Nomogram
DOI10.1007/s00330-018-5770-y
WOS KeywordPROGNOSTIC-FACTORS ; TNM CLASSIFICATION ; HETEROGENEITY ; INFORMATION ; PROPOSALS ; EDITION ; IMAGES
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2017YFA0205202] ; National Natural Science Foundation of China[U1401255] ; National Natural Science Foundation of China[61672422]
Funding OrganizationNational Key Research and Development Program of China ; National Natural Science Foundation of China
WOS Research AreaRadiology, Nuclear Medicine & Medical Imaging
WOS SubjectRadiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000463157200004
PublisherSPRINGER
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/24949
Collection中国科学院自动化研究所
Corresponding AuthorHuang, Liyu; Tian, Jie
Affiliation1.Xidian Univ, Sch Life Sci & Technol, Xian 710071, Shaanxi, Peoples R China
2.Wake Forest Sch Med, Dept Radiol, Med Ctr Blvd, Winston Salem, NC 27157 USA
3.Shaanxi Prov Peoples Hosp, Dept Radiol, Xian 710068, Shaanxi, Peoples R China
4.Guangdong Gen Hosp, Dept Radiol, Guangzhou 510080, Guangdong, Peoples R China
5.Chinese Acad Sci, Key Lab Mol Imaging, Beijing 100080, Peoples R China
Recommended Citation
GB/T 7714
Yang, Lifeng,Yang, Jingbo,Zhou, Xiaobo,et al. Development of a radiomics nomogram based on the 2D and 3D CT features to predict the survival of non-small cell lung cancer patients[J]. EUROPEAN RADIOLOGY,2019,29(5):2196-2206.
APA Yang, Lifeng.,Yang, Jingbo.,Zhou, Xiaobo.,Huang, Liyu.,Zhao, Weiling.,...&Tian, Jie.(2019).Development of a radiomics nomogram based on the 2D and 3D CT features to predict the survival of non-small cell lung cancer patients.EUROPEAN RADIOLOGY,29(5),2196-2206.
MLA Yang, Lifeng,et al."Development of a radiomics nomogram based on the 2D and 3D CT features to predict the survival of non-small cell lung cancer patients".EUROPEAN RADIOLOGY 29.5(2019):2196-2206.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Yang, Lifeng]'s Articles
[Yang, Jingbo]'s Articles
[Zhou, Xiaobo]'s Articles
Baidu academic
Similar articles in Baidu academic
[Yang, Lifeng]'s Articles
[Yang, Jingbo]'s Articles
[Zhou, Xiaobo]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Yang, Lifeng]'s Articles
[Yang, Jingbo]'s Articles
[Zhou, Xiaobo]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.