CASIA OpenIR  > 中国科学院分子影像重点实验室
Integrating No.3 lymph nodes and primary tumor radiomics to predict lymph node metastasis in T1-2 gastric cancer
Wang,Xiaoxiao1; Li,Cong2,3; Fang,Mengjie2,3; Zhang,Liwen2,3; Zhong,Lianzhen2,3; Dong,Di2,3; Tian,Jie2,4,5,6; Shan,Xiuhong1
Source PublicationBMC Medical Imaging
ISSN1471-2342
2021-03-23
Volume21Issue:1Pages:10
Abstract

AbstractBackgroundThis study aimed to develope and validate a radiomics nomogram by integrating the quantitative radiomics characteristics of No.3 lymph nodes (LNs) and primary tumors to better predict preoperative lymph node metastasis (LNM) in T1-2 gastric cancer (GC) patients.MethodsA total of 159 T1-2 GC patients who had undergone surgery with lymphadenectomy between March 2012 and November 2017 were retrospectively collected and divided into a training cohort (n?=?80) and a testing cohort (n?=?79). Radiomic features were extracted from both tumor region and No. 3 station LNs based on computed tomography (CT) images per patient. Then, key features were selected using minimum redundancy maximum relevance algorithm and fed into two radiomic signatures, respectively. Meanwhile, the predictive performance of clinical risk factors was studied. Finally, a nomogram was built by merging radiomic signatures and clinical risk factors and evaluated by the area under the receiver operator characteristic curve (AUC) as well as decision curve.ResultsTwo radiomic signatures, reflecting phenotypes of the tumor and LNs respectively, were significantly associated with LN metastasis. A nomogram incorporating two radiomic signatures and CT-reported LN metastasis status showed good discrimination of LN metastasis in both the training cohort (AUC 0.915; 95% confidence interval [CI] 0.832–0.998) and testing cohort (AUC 0.908; 95% CI 0.814–1.000). The decision curve also indicated its potential clinical usefulness.ConclusionsThe nomogram received favorable predictive accuracy in predicting No.3 LNM in T1-2 GC, and the nomogram showed positive role in predicting LNM in No.4 LNs. The nomogram may be used to predict LNM in T1-2 GC and could assist the choice of therapy.

KeywordStomach cancer Lymph nodes Nomogram
DOI10.1186/s12880-021-00587-3
Indexed BySCI
Project Number2017YFC1309100, 2017YFA0205200, 2017YFA0700401, 2017YFC1308700
Language英语
Funding ProjectNational Key R&D Program of China[2017YFC1309100] ; National Key R&D Program of China[2017YFA0205200] ; National Key R&D Program of China[2017YFA0700401] ; National Key R&D Program of China[2017YFC1308700] ; National Natural Science Foundation of China[82022036] ; 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 China[6202790004] ; National Natural Science Foundation of China[81930053] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81501616] ; Zhenjiang Innovation Capacity Building Program (technological infrastructure)-R&D project of China[SS2015023] ; Jiangsu Provincial Key RD Special Fund[BE2015666] ; Beijing Natural Science Foundation[L182061] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB 38040200] ; Project of High-Level Talents Team Introduction in Zhuhai City (Zhuhai)[HLHPTP201703] ; Youth Innovation Promotion Association CAS[2017175] ; Bureau of International Cooperation of Chinese Academy of Sciences[173211KYSB20160053] ; Zhenjiang first people's Hospital Fund[Y2019016-S] ; Jiangsu Innovative team leading talent fund[CXTDC2016006] ; Jiangsu six high peak talent fund[WSW-205] ; Jiangsu 333 talent fund[BRA2020016]
Funding OrganizationNational Key R&D Program of China ; National Natural Science Foundation of China ; Zhenjiang Innovation Capacity Building Program (technological infrastructure)-R&D project of China ; Jiangsu Provincial Key RD Special Fund ; Beijing Natural Science Foundation ; Strategic Priority Research Program of Chinese Academy of Sciences ; Project of High-Level Talents Team Introduction in Zhuhai City (Zhuhai) ; Youth Innovation Promotion Association CAS ; Bureau of International Cooperation of Chinese Academy of Sciences ; Zhenjiang first people's Hospital Fund ; Jiangsu Innovative team leading talent fund ; Jiangsu six high peak talent fund ; Jiangsu 333 talent fund
WOS Research AreaRadiology, Nuclear Medicine & Medical Imaging
WOS SubjectRadiology, Nuclear Medicine & Medical Imaging
WOS IDBMC:10.1186/s12880-021-00587-3
PublisherBioMed Central
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/43297
Collection中国科学院分子影像重点实验室
Corresponding AuthorDong,Di; Tian,Jie; Shan,Xiuhong
Affiliation1.Affiliated People’s Hospital of JiangSu University; Department of Radiology
2.Chinese Academy of Sciences; CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation
3.School of Artificial Intelligence, University of Chinese Academy of Sciences
4.Beihang University; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine
5.Xidian University; Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology
6.Zhuhai People’s Hospital (Affiliated With Jinan University); Zhuhai Precision Medical Center
Recommended Citation
GB/T 7714
Wang,Xiaoxiao,Li,Cong,Fang,Mengjie,et al. Integrating No.3 lymph nodes and primary tumor radiomics to predict lymph node metastasis in T1-2 gastric cancer[J]. BMC Medical Imaging,2021,21(1):10.
APA Wang,Xiaoxiao.,Li,Cong.,Fang,Mengjie.,Zhang,Liwen.,Zhong,Lianzhen.,...&Shan,Xiuhong.(2021).Integrating No.3 lymph nodes and primary tumor radiomics to predict lymph node metastasis in T1-2 gastric cancer.BMC Medical Imaging,21(1),10.
MLA Wang,Xiaoxiao,et al."Integrating No.3 lymph nodes and primary tumor radiomics to predict lymph node metastasis in T1-2 gastric cancer".BMC Medical Imaging 21.1(2021):10.
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