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

文献类型：期刊论文

作者Zhang, Lu1; Wu, Xiangjun3,4; Liu, Jing1; Zhang, Bin1; Mo, Xiaokai1; Chen, Qiuying1; Fang, Jin1; Wang, Fei1; Li, Minmin1; Chen, Zhuozhi1
刊名JOURNAL OF MAGNETIC RESONANCE IMAGING
出版日期2020-08-09
页码12
关键词nasopharyngeal carcinoma deep learning distant metastasis-free survival induction chemotherapy chemoradiotherapy
ISSN号1053-1807
DOI10.1002/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.
WOS关键词CONCURRENT CHEMORADIOTHERAPY ; RADIOMICS ; MULTICENTER ; NEOADJUVANT ; SIGNATURE ; DNA
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研究方向Radiology, Nuclear Medicine & Medical Imaging
语种英语
WOS记录号WOS:000557279200001
出版者WILEY
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
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.Jinan Univ, Affiliated Hosp 1, Dept Radiol, 613 Huangpu West Rd, Guangzhou 510627, Guangdong, Peoples R China
2.Sun Yat Sen Univ, Zhongshan Hosp, Dept Radiol, Zhongshan, Peoples R China
3.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China
4.Univ Chinese Acad Sci, Sch Artificial Intelligence, 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.

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来源：自动化研究所

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