Prognostic Value of Deep Learning PET/CT-Based Radiomics: Potential Role for Future Individual Induction Chemotherapy in Advanced Nasopharyngeal Carcinoma

doi:10.1158/1078-0432.CCR-18-3065

	Prognostic Value of Deep Learning PET/CT-Based Radiomics: Potential Role for Future Individual Induction Chemotherapy in Advanced Nasopharyngeal Carcinoma
	Peng, Hao 1; Dong, Di2,3 ; Fang, Meng-Jie2,3 ; Li, Lu 4,5; Tang, Ling-Long 1; Chen, Lei 1; Li, Wen-Fei 1; Mao, Yan-Ping 1; Fan, Wei 5; Liu, Li-Zhi 6
刊名	CLINICAL CANCER RESEARCH
	2019-07-15
卷号	25 期号:14 页码:4271-4279
ISSN号	1078-0432
DOI	10.1158/1078-0432.CCR-18-3065
通讯作者	Tian, Jie(jie.tian@ia.ac.cn) ; Ma, Jun(majun2@mail.sysu.edu.cn)
英文摘要	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.
资助项目	National Natural Science Foundation of China[81230056] ; National Natural Science Foundation of China[81402516] ; National Natural Science Foundation of China[81771924] ; National Natural Science Foundation of China[81501616] ; National Natural Science Foundation of China[81227901] ; 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] ; Beijing Natural Science Foundation[L182061] ; National Key R&D Program of China[2017YFA0205200] ; National Key R&D Program of China[2017YFC1308700] ; National Key R&D Program of China[2017YFC1309100] ; Youth Innovation Promotion Association CAS[2017175]
WOS关键词	BARR-VIRUS DNA ; INTENSITY-MODULATED RADIOTHERAPY ; CONCURRENT CHEMORADIOTHERAPY ; INTRATUMOR HETEROGENEITY ; NEOADJUVANT CHEMOTHERAPY ; STAGING SYSTEM ; MULTICENTER ; REGRESSION ; SURVIVAL ; OUTCOMES
WOS研究方向	Oncology
语种	英语
出版者	AMER ASSOC CANCER RESEARCH
WOS记录号	WOS:000478018100010
资助机构	National Natural Science Foundation of China ; National Science and Technology Pillar Program ; Natural Science Foundation of Guangdong Province ; Health and Medical Collaborative Innovation Project of Guangzhou City, China ; Program of Introducing Talents of Discipline to Universities ; Innovation Team Development Plan of the Ministry of Education ; Beijing Natural Science Foundation ; National Key R&D Program of China ; Youth Innovation Promotion Association CAS
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/27778]
专题	中国科学院自动化研究所
通讯作者	Tian, Jie; Ma, Jun
作者单位	1.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
推荐引用方式 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.