MRI-Based Deep-Learning Model for Distant Metastasis-Free Survival in Locoregionally Advanced Nasopharyngeal Carcinoma

doi:10.1002/jmri.27308

CORC > 自动化研究所 > 中国科学院自动化研究所 > 中国科学院分子影像重点实验室

	MRI-Based Deep-Learning Model for Distant Metastasis-Free Survival in Locoregionally Advanced Nasopharyngeal Carcinoma
	Zhang, Lu 2; Wu, Xiangjun 3,4; Liu, Jing 2; Zhang, Bin 2; Mo, Xiaokai 2; Chen, Qiuying 2; Fang, Jin 2; Wang, Fei 2; Li, Minmin 2; Chen, Zhuozhi 2
刊名	JOURNAL OF MAGNETIC RESONANCE IMAGING
	2020-08-09
页码	12
关键词	nasopharyngeal carcinoma deep learning distant metastasis-free survival induction chemotherapy chemoradiotherapy
ISSN号	1053-1807
DOI	10.1002/jmri.27308
通讯作者	Tang, Binghang(jmftbh@sina.com) ; Dong, Di(di.dong@ia.ac.cn) ; Zhang, Shuixing(shui7515@126.com)
英文摘要	Background Distant metastasis is the primary cause of treatment failure in locoregionally advanced nasopharyngeal carcinoma (LANPC). Purpose To develop a model to evaluate distant metastasis-free survival (DMFS) in LANPC and to explore the value of additional chemotherapy to concurrent chemoradiotherapy (CCRT) for different risk groups. Study Type Retrospective. Population In all, 233 patients with biopsy-confirmed nasopharyngeal carcinoma (NPC) from two hospitals. Field Strength 1.5T and 3T. Sequence Axial T-2-weighted (T-2-w) and contrast-enhanced T-1-weighted (CET1-w) images. Assessment Deep learning was used to build a model based on MRI images (including axial T-2-w and CET1-w images) and clinical variables. Hospital 1 patients were randomly divided into training (n =169) and validation (n =19) cohorts; Hospital 2 patients were assigned to a testing cohort (n =45). LANPC patients were divided into low- and high-risk groups according to their DMFS (P < 0.05). Kaplan-Meier survival analysis was performed to compare the DMFS of different risk groups and subgroup analysis was performed to compare patients treated with CCRT alone and treated with additional chemotherapy to CCRT in different risk groups, respectively. Statistical Tests Univariate analysis was performed to identify significant clinical variables. The area under the receiver operating characteristic (ROC) curve (AUC) was used to assess the model performance. Results Our deep-learning model integrating the deep-learning signature, node (N) stage (from TNM staging), plasma Epstein-Barr virus (EBV)-DNA, and treatment regimens yielded an AUC of 0.796 (95% confidence interval [CI]: 0.729-0.863), 0.795 (95% CI: 0.540-1.000), and 0.808 (95% CI: 0.654-0.962) in the training, internal validation, and external testing cohorts, respectively. Low-risk patients treated with CCRT alone had longer DMFS than patients treated with additional chemotherapy to CCRT (P < 0.05). Data Conclusion The proposed deep-learning model, based on MRI features and clinical variates, facilitated the prediction of DMFS in LANPC patients. Level of Evidence 3. Technical Efficacy Stage 4.
资助项目	National Natural Science Foundation of China[81571664] ; National Natural Science Foundation of China[81871323] ; National Natural Science Foundation of China[81801665] ; 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 Guangdong Province[2018B030311024] ; Scientific Research General Project of Guangzhou Science Technology and Innovation Commission[201707010328] ; China Postdoctoral Science Foundation[2016M600145]
WOS关键词	CONCURRENT CHEMORADIOTHERAPY ; RADIOMICS ; MULTICENTER ; NEOADJUVANT ; SIGNATURE ; DNA
WOS研究方向	Radiology, Nuclear Medicine & Medical Imaging
语种	英语
出版者	WILEY
WOS记录号	WOS:000557279200001
资助机构	National Natural Science Foundation of China ; National Natural Science Foundation of Guangdong Province ; Scientific Research General Project of Guangzhou Science Technology and Innovation Commission ; China Postdoctoral Science Foundation
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/40350]
专题	自动化研究所_中国科学院分子影像重点实验室
通讯作者	Tang, Binghang; Dong, Di; Zhang, Shuixing
作者单位	1.Sun Yat Sen Univ, Zhongshan Hosp, Dept Radiol, Zhongshan, Peoples R China 2.Jinan Univ, Affiliated Hosp 1, Dept Radiol, 613 Huangpu West Rd, Guangzhou 510627, Guangdong, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 4.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Lu,Wu, Xiangjun,Liu, Jing,et al. MRI-Based Deep-Learning Model for Distant Metastasis-Free Survival in Locoregionally Advanced Nasopharyngeal Carcinoma[J]. JOURNAL OF MAGNETIC RESONANCE IMAGING,2020:12.
APA	Zhang, Lu.,Wu, Xiangjun.,Liu, Jing.,Zhang, Bin.,Mo, Xiaokai.,...&Zhang, Shuixing.(2020).MRI-Based Deep-Learning Model for Distant Metastasis-Free Survival in Locoregionally Advanced Nasopharyngeal Carcinoma.JOURNAL OF MAGNETIC RESONANCE IMAGING,12.
MLA	Zhang, Lu,et al."MRI-Based Deep-Learning Model for Distant Metastasis-Free Survival in Locoregionally Advanced Nasopharyngeal Carcinoma".JOURNAL OF MAGNETIC RESONANCE IMAGING (2020):12.