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Evaluation of Lymph Node Metastasis in Advanced Gastric Cancer Using Magnetic Resonance Imaging-Based Radiomics

文献类型：期刊论文

作者Chen, Wujie2,3,4; Wang, Siwen1,5; Dong, Di1,5; Gao, Xuning2,3; Zhou, Kefeng2,3; Li, Jiaying2,3; Lv, Bin2,4; Li, Hailin1,5; Wu, Xiangjun1,5; Fang, Mengjie1,5
刊名FRONTIERS IN ONCOLOGY
出版日期2019-11-22
卷号9页码:11
ISSN号2234-943X
关键词lymph node metastasis magnetic resonance imaging diffusion-weighted imaging advanced gastric cancer radiomics
DOI10.3389/fonc.2019.01265
通讯作者Tian, Jie(jie.tian@ia.ac.cn) ; Xu, Maosheng(xums166@zcmu.edu.cn)
英文摘要Objective: To develop and evaluate a diffusion-weighted imaging (DWI)-based radiomic nomogram for lymph node metastasis (LNM) prediction in advanced gastric cancer (AGC) patients. Overall Study: This retrospective study was conducted with 146 consecutively included pathologically confirmed AGC patients from two centers. All patients underwent preoperative 3.0 T magnetic resonance imaging (MRI) examination. The dataset was allocated to a training cohort (n = 71) and an internal validation cohort (n = 47) from one center along with an external validation cohort (n = 28) from another. A summary of 1,305 radiomic features were extracted per patient. The least absolute shrinkage and selection operator (LASSO) logistic regression and learning vector quantization (LVQ) methods with cross-validations were adopted to select significant features in a radiomic signature. Combining the radiomic signature and independent clinical factors, a radiomic nomogram was established. The MRI-reported N staging and the MRI-derived model were built for comparison. Model performance was evaluated considering receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA). Results: A two-feature radiomic signature was found significantly associated with LNM (p < 0.01, training and internal validation cohorts). A radiomic nomogram was established by incorporating the clinical minimum apparent diffusion coefficient (ADC) and MRI-reported N staging. The radiomic nomogram showed a favorable classification ability with an area under ROC curve of 0.850 [95% confidence interval (CI), 0.758-0.942] in the training cohort, which was then confirmed with an AUC of 0.857 (95% CI, 0.714-1.000) in internal validation cohort and 0.878 (95% CI, 0.696-1.000) in external validation cohort. Meanwhile, the specificity, sensitivity, and accuracy were 0.846, 0.853, and 0.851 in internal validation cohort, and 0.714, 0.952, and 0.893 in external validation cohort, compensating for the MRI-reported N staging and MRI-derived model. DCA demonstrated good clinical use of radiomic nomogram. Conclusions: This study put forward a DWI-based radiomic nomogram incorporating the radiomic signature, minimum ADC, and MRI-reported N staging for individualized preoperative detection of LNM in patients with AGC.
WOS关键词MRI ; GASTRECTOMY
资助项目Key Research Project of Zhejiang TCM Science and Technology Plan[2018ZZ010] ; National Key R&D Program of China[2017YFA0205200] ; National Key R&D Program of China[2017YFC1308700] ; National Key R&D Program of China[2017YFC1309100] ; National Natural Science Foundation of China[81971776] ; National Natural Science Foundation of China[81673745] ; National Natural Science Foundation of China[81771924] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81671854] ; National Natural Science Foundation of China[81671851] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[61671449] ; National Natural Science Foundation of China[61622117] ; Zhejiang Province Natural Science Foundation[LY15H030033] ; Zhejiang Province Natural Science Foundation[LSY19H030001] ; Beijing Natural Science Foundation[L182061] ; Youth Innovation Promotion Association CAS[2017175]
WOS研究方向Oncology
语种英语
出版者FRONTIERS MEDIA SA
WOS记录号WOS:000501254000001
资助机构Key Research Project of Zhejiang TCM Science and Technology Plan ; National Key R&D Program of China ; National Natural Science Foundation of China ; Zhejiang Province Natural Science Foundation ; Beijing Natural Science Foundation ; Youth Innovation Promotion Association CAS
源URL[http://ir.ia.ac.cn/handle/173211/29409]  
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Tian, Jie; Xu, Maosheng
作者单位1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
2.Zhejiang Chinese Med Univ, Coll Clin Med 1, Hangzhou, Zhejiang, Peoples R China
3.Zhejiang Chinese Med Univ, Affiliated Hosp 1, Dept Radiol, Hangzhou, Zhejiang, Peoples R China
4.Zhejiang Chinese Med Univ, Affiliated Hosp 1, Dept Gastroenterol, Hangzhou, Zhejiang, Peoples R China
5.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China
6.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Chen, Wujie,Wang, Siwen,Dong, Di,et al. Evaluation of Lymph Node Metastasis in Advanced Gastric Cancer Using Magnetic Resonance Imaging-Based Radiomics[J]. FRONTIERS IN ONCOLOGY,2019,9:11.
APA Chen, Wujie.,Wang, Siwen.,Dong, Di.,Gao, Xuning.,Zhou, Kefeng.,...&Xu, Maosheng.(2019).Evaluation of Lymph Node Metastasis in Advanced Gastric Cancer Using Magnetic Resonance Imaging-Based Radiomics.FRONTIERS IN ONCOLOGY,9,11.
MLA Chen, Wujie,et al."Evaluation of Lymph Node Metastasis in Advanced Gastric Cancer Using Magnetic Resonance Imaging-Based Radiomics".FRONTIERS IN ONCOLOGY 9(2019):11.

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