CASIA OpenIR
CT-Based Radiomic Signature as a Prognostic Factor in Stage IV ALK-Positive Non-small-cell Lung Cancer Treated With TKI Crizotinib: A Proof-of-Concept Study
Li, Hailin1,2; Zhang, Rui1; Wang, Siwen2,3; Fang, Mengjie2,3; Zhu, Yongbei2,4; Hu, Zhenhua2,3; Dong, Di2,3; Shi, Jingyun5; Tian, Jie2,4
Source PublicationFRONTIERS IN ONCOLOGY
ISSN2234-943X
2020-02-18
Volume10Pages:9
Corresponding AuthorHu, Zhenhua(zhenhua.hu@ia.ac.cn) ; Dong, Di(di.dong@ia.ac.cn) ; Shi, Jingyun(shijingyun89179@126.com) ; Tian, Jie(jie.tian@ia.ac.cn)
AbstractObjectives: To identify a computed tomography (CT)-based radiomic signature for predicting progression-free survival (PFS) in stage IV anaplastic lymphoma kinase (ALK)-positive non-small-cell lung cancer (NSCLC) patients treated with tyrosine kinase inhibitor (TKI) crizotinib. Materials and Methods: This retrospective proof-of-concept study included a cohort of 63 stage IV ALK-positive NSCLC patients who had received TKI crizotinib therapy for model construction and validation. Another independent cohort including 105 stage IV EGFR-positive NSCLC patients was also used for external validation in EGFR-TKI treatment. We initially extracted 481 quantitative three-dimensional features derived from manually segmented tumor volumes of interest. Pearson's correlation analysis along with the least absolute shrinkage and selection operator (LASSO) penalized Cox proportional hazards regression was successively performed to select critical radiomic features. A CT-based radiomic signature for PFS prediction was obtained using multivariate Cox regression. The performance evaluation of the radiomic signature was conducted using the concordance index (C-index), time-dependent receiver operating characteristic (ROC) analysis, and Kaplan-Meier survival analysis. Results: A radiomic signature containing three features showed significant prognostic performance for ALK-positive NSCLC patients in both the training cohort (C-index, 0.744; time-dependent AUC, 0.895) and the validation cohort (C-index, 0.717; time-dependent AUC, 0.824). The radiomic signature could significantly risk-stratify ALK-positive NSCLC patients (hazard ratio, 2.181; P < 0.001) and outperformed other prognostic factors. However, no significant association with PFS was captured for the radiomic signature in the EGFR-positive NSCLC cohort (log-rank tests, P = 0.41). Conclusions: The CT-based radiomic features can capture valuable information regarding the tumor phenotype. The proposed radiomic signature was found to be an effective prognostic factor in stage IV ALK mutated nonsynchronous nodules in NSCLC patients treated with a TKI.
Keywordcomputed tomography radiomics non-small-cell lung cancer tyrosine kinase inhibitor resistance anaplastic lymphoma kinase
DOI10.3389/fonc.2020.00057
WOS KeywordTYROSINE KINASE ; RESISTANCE ; CHEMOTHERAPY ; REGRESSION ; INHIBITOR
Indexed BySCI
Language英语
Funding ProjectNational Key R&D Program of China[2017YFC1308700] ; National Key R&D Program of China[2017YFA0205200] ; National Key R&D Program of China[2017YFC1309100] ; National Key R&D Program of China[2016YFC0102600] ; National Key R&D Program of China[2016YFA0100900] ; National Key R&D Program of China[2016YFA0100902] ; National Natural Science Foundation of China[91959130] ; National Natural Science Foundation of China[81971776] ; National Natural Science Foundation of China[81771924] ; National Natural Science Foundation of China[81501616] ; National Natural Science Foundation of China[61622117] ; National Natural Science Foundation of China[81671759] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81671851] ; National Natural Science Foundation of China[81527805] ; Beijing Natural Science Foundation[L182061] ; Beijing Natural Science Foundation[JQ19027] ; Bureau of International Cooperation of Chinese Academy of Sciences[173211KYSB20160053] ; Beijing Nova Program[Z181100006218046] ; Scientific Instrument Developing Project of the Chinese Academy of Sciences[YZ201502] ; Scientific Instrument Developing Project of the Chinese Academy of Sciences[YZ201672] ; Youth Innovation Promotion Association CAS[2017175] ; National Key R&D Program of China[2017YFC1308700] ; National Key R&D Program of China[2017YFA0205200] ; National Key R&D Program of China[2017YFC1309100] ; National Key R&D Program of China[2016YFC0102600] ; National Key R&D Program of China[2016YFA0100900] ; National Key R&D Program of China[2016YFA0100902] ; National Natural Science Foundation of China[91959130] ; National Natural Science Foundation of China[81971776] ; National Natural Science Foundation of China[81771924] ; National Natural Science Foundation of China[81501616] ; National Natural Science Foundation of China[61622117] ; National Natural Science Foundation of China[81671759] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81671851] ; National Natural Science Foundation of China[81527805] ; Beijing Natural Science Foundation[L182061] ; Beijing Natural Science Foundation[JQ19027] ; Bureau of International Cooperation of Chinese Academy of Sciences[173211KYSB20160053] ; Beijing Nova Program[Z181100006218046] ; Scientific Instrument Developing Project of the Chinese Academy of Sciences[YZ201502] ; Scientific Instrument Developing Project of the Chinese Academy of Sciences[YZ201672] ; Youth Innovation Promotion Association CAS[2017175]
Funding OrganizationNational Key R&D Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Bureau of International Cooperation of Chinese Academy of Sciences ; Beijing Nova Program ; Scientific Instrument Developing Project of the Chinese Academy of Sciences ; Youth Innovation Promotion Association CAS
WOS Research AreaOncology
WOS SubjectOncology
WOS IDWOS:000518612200001
PublisherFRONTIERS MEDIA SA
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/38334
Collection中国科学院自动化研究所
Corresponding AuthorHu, Zhenhua; Dong, Di; Shi, Jingyun; Tian, Jie
Affiliation1.Harbin Univ Sci & Technol, Sch Automat, Harbin, Peoples R China
2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
4.Beihang Univ, Sch Med, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing, Peoples R China
5.Tongji Univ, Sch Med, Shanghai Pulm Hosp, Dept Radiol, Shanghai, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Li, Hailin,Zhang, Rui,Wang, Siwen,et al. CT-Based Radiomic Signature as a Prognostic Factor in Stage IV ALK-Positive Non-small-cell Lung Cancer Treated With TKI Crizotinib: A Proof-of-Concept Study[J]. FRONTIERS IN ONCOLOGY,2020,10:9.
APA Li, Hailin.,Zhang, Rui.,Wang, Siwen.,Fang, Mengjie.,Zhu, Yongbei.,...&Tian, Jie.(2020).CT-Based Radiomic Signature as a Prognostic Factor in Stage IV ALK-Positive Non-small-cell Lung Cancer Treated With TKI Crizotinib: A Proof-of-Concept Study.FRONTIERS IN ONCOLOGY,10,9.
MLA Li, Hailin,et al."CT-Based Radiomic Signature as a Prognostic Factor in Stage IV ALK-Positive Non-small-cell Lung Cancer Treated With TKI Crizotinib: A Proof-of-Concept Study".FRONTIERS IN ONCOLOGY 10(2020):9.
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