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Prognostic Value of Deep Learning PET/CT-Based Radiomics: Potential Role for Future Individual Induction Chemotherapy in Advanced Nasopharyngeal Carcinoma
Peng, Hao1; Dong, Di2,3; Fang, Meng-Jie2,3; Li, Lu4,5; Tang, Ling-Long1; Chen, Lei1; Li, Wen-Fei1; Mao, Yan-Ping1; Fan, Wei5; Liu, Li-Zhi6; Tian, Li6; Lin, Ai-Hua7; Sun, Ying1; Tian, Jie2,8,9; Ma, Jun1
Source PublicationCLINICAL CANCER RESEARCH
ISSN1078-0432
2019-07-15
Volume25Issue:14Pages:4271-4279
Abstract

Purpose: We aimed to evaluate the value of deep learning on positron emission tomography with computed tomography (PET/CT)-based radiomics for individual induction chemotherapy (IC) in advanced nasopharyngeal carcinoma (NPC). Experimental Design: We constructed radiomics signatures and nomogram for predicting disease-free survival (DFS) based on the extracted features from PET and CT images in a training set (n = 470), and then validated it on a test set (n = 237). Harrell's concordance indices (C-index) and time-independent receiver operating characteristic (ROC) analysis were applied to evaluate the discriminatory ability of radiomics nomogram, and compare radiomics signatures with plasma Epstein-Barr virus (EBV) DNA. Results: A total of 18 features were selected to construct CT-based and PET-based signatures, which were significantly associated with DFS (P < 0.001). Using these sig-natures, we proposed a radiomics nomogram with a C-index of 0.754 [95% confidence interval (95% CI), 0.709-0.800] in the training set and 0.722 (95% CI, 0.652-0.792) in the test set. Consequently, 206 (29.1%) patients were stratified as high-risk group and the other 501 (70.9%) as low-risk group by the radiomics nomogram, and the corresponding 5-year DFS rates were 50.1% and 87.6%, respectively (P < 0.0001). High-risk patients could benefit from IC while the low-risk could not. Moreover, radiomics nomogram performed significantly better than the EBV DNA-based model (C-index: 0.754 vs. 0.675 in the training set and 0.722 vs. 0.671 in the test set) in risk stratification and guiding IC. Conclusions: Deep learning PET/CT-based radiomics could serve as a reliable and powerful tool for prognosis prediction and may act as a potential indicator for individual IC in advanced NPC.

KeywordAdvanced Nasopharyngeal Carcinoma
DOI10.1158/1078-0432.CCR-18-3065
WOS KeywordBARR-VIRUS DNA ; INTENSITY-MODULATED RADIOTHERAPY ; CONCURRENT CHEMORADIOTHERAPY ; INTRATUMOR HETEROGENEITY ; NEOADJUVANT CHEMOTHERAPY ; STAGING SYSTEM ; MULTICENTER ; REGRESSION ; SURVIVAL ; OUTCOMES
Indexed BySCI
Language英语
Funding ProjectInnovation Team Development Plan of the Ministry of Education[IRT_17R110] ; Program of Introducing Talents of Discipline to Universities[B14035] ; Health and Medical Collaborative Innovation Project of Guangzhou City, China[201400000001] ; Natural Science Foundation of Guangdong Province[2017A030312003] ; National Science and Technology Pillar Program[2014BAI09B10] ; National Natural Science Foundation of China[81572658] ; National Natural Science Foundation of China[81402516] ; National Natural Science Foundation of China[81230056] ; Youth Innovation Promotion Association CAS[2017175] ; Beijing Natural Science Foundation[L182061] ; National Key R&D Program of China[2017YFC1309100] ; National Key R&D Program of China[2017YFC1308700] ; National Natural Science Foundation of China[81501616] ; National Natural Science Foundation of China[81771924] ; National Key R&D Program of China[2017YFA0205200] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81227901] ; National Key R&D Program of China[2017YFA0205200] ; National Natural Science Foundation of China[81771924] ; National Natural Science Foundation of China[81501616] ; National Key R&D Program of China[2017YFC1308700] ; National Key R&D Program of China[2017YFC1309100] ; Beijing Natural Science Foundation[L182061] ; Youth Innovation Promotion Association CAS[2017175] ; National Natural Science Foundation of China[81230056] ; National Natural Science Foundation of China[81402516] ; National Natural Science Foundation of China[81572658] ; National Science and Technology Pillar Program[2014BAI09B10] ; Natural Science Foundation of Guangdong Province[2017A030312003] ; Health and Medical Collaborative Innovation Project of Guangzhou City, China[201400000001] ; Program of Introducing Talents of Discipline to Universities[B14035] ; Innovation Team Development Plan of the Ministry of Education[IRT_17R110]
WOS Research AreaOncology
WOS SubjectOncology
WOS IDWOS:000478018100010
PublisherAMER ASSOC CANCER RESEARCH
Citation statistics
Cited Times:50[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/27779
Collection中国科学院分子影像重点实验室
Corresponding AuthorTian, Jie; Ma, Jun
Affiliation1.Sun Yat Sen Univ, Guangdong Key Lab Nasopharyngeal Carcinoma Diag &, Collaborat Innovat Ctr Canc Med,Dept Radiat Oncol, Canc Ctr,State Key Lab Oncol Southern China, Guangzhou, Guangdong, Peoples R China
2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Beijing, Peoples R China
4.Southern Med Univ, Nanfang Hosp, Dept Radiat Oncol, Guangzhou, Guangdong, Peoples R China
5.Sun Yat Sen Univ, Collaborat Innovat Ctr Canc Med, State Key Lab Oncol South China, Dept Nucl Med,Canc Ctr, Guangzhou, Guangdong, Peoples R China
6.Sun Yat Sen Univ, Collaborat Innovat Ctr Canc Med, State Key Lab Oncol Southern China, Imaging Diag & Intervent Ctr,Canc Ctr, Guangzhou, Guangdong, Peoples R China
7.Sun Yat Sen Univ, Sch Publ Hlth, Dept Med Stat & Epidemiol, Guangzhou, Guangdong, Peoples R China
8.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing, Peoples R China
9.Xidian Univ, Sch Life Sci & Technol, Minist Educ, Engn Res Ctr Mol & Neuro Imaging, Xian, Shaanxi, Peoples R China
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Peng, Hao,Dong, Di,Fang, Meng-Jie,et al. Prognostic Value of Deep Learning PET/CT-Based Radiomics: Potential Role for Future Individual Induction Chemotherapy in Advanced Nasopharyngeal Carcinoma[J]. CLINICAL CANCER RESEARCH,2019,25(14):4271-4279.
APA Peng, Hao.,Dong, Di.,Fang, Meng-Jie.,Li, Lu.,Tang, Ling-Long.,...&Ma, Jun.(2019).Prognostic Value of Deep Learning PET/CT-Based Radiomics: Potential Role for Future Individual Induction Chemotherapy in Advanced Nasopharyngeal Carcinoma.CLINICAL CANCER RESEARCH,25(14),4271-4279.
MLA Peng, Hao,et al."Prognostic Value of Deep Learning PET/CT-Based Radiomics: Potential Role for Future Individual Induction Chemotherapy in Advanced Nasopharyngeal Carcinoma".CLINICAL CANCER RESEARCH 25.14(2019):4271-4279.
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