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Specific Borrmann classification in advanced gastric cancer by an ensemble multilayer perceptron network: a multicenter research

文献类型：期刊论文

作者Wang, Siwen1,2; Dong, Di1,2; Zhang, Wenjuan3; Hu, Hui4; Li, Hailin1,5; Zhu, Yongbei1,5; Zhou, Junlin3; Shan, Xiuhong4; Tian, Jie1,5,6,7
刊名MEDICAL PHYSICS
出版日期2021-08-04
页码12
关键词advanced gastric cancer Borrmann classification ensemble learning multilayer perceptron networks radiomics
ISSN号0094-2405
DOI10.1002/mp.15094
通讯作者Zhou, Junlin(ery_zhoujl@lzu.edu.cn) ; Shan, Xiuhong(13913433095@163.com) ; Tian, Jie(jie.tian@ia.ac.cn)
英文摘要Purpose Borrmann classification in advanced gastric cancer (AGC) is necessarily associated with personalized surgical strategy and prognosis. But few radiomics research studies have focused on specific Borrmann classification, and there is yet no consensus regarding what machine learning methods should be the most effective. Methods A combined size of 889 AGC patients was retrospectively enrolled from two centers. Radiomic features were extracted from tumors manually delineated on preoperative computed tomography images. Two classification experiments (Borrmann I/II/III vs. IV and Borrmann II vs. III) were conducted. In each task, we combined three common feature selection methods and five typical machine learning classifiers to construct 15 basic classification models, and then fed the 15 predictions to a designed multilayer perceptron (MLP) network. Results In internal and external validation cohorts, the proposed ensemble MLP yielded good performance with area under curves of 0.767 and 0.702 for Borrmann I/II/III vs. IV, as well as 0.768 and 0.731 for Borrmann II vs. III. Considering the imbalanced distribution of four Borrmann types (I, 2.9%; II, 12.8%; III, 69.5%; IV, 14.7%), the ensemble MLP surpassed the overfitting barrier and attained fine specificity (0.667 and 0.750 for Borrmann I/II/III vs. IV; 0.714 and 0.620 for Borrmann II vs. III) and sensitivity (0.795 and 0.610 for Borrmann I/II/III vs. IV; 0.652 and 0.703 for Borrmann II vs. III). Also, survival analysis showed that patients could be significantly risk stratified by MLP predicted types in both experiments (p < 0.0001, log-rank test). Conclusions This study proposed an MLP-based ensemble learning architecture, which could identify Borrmann type IV automatically and improve the differentiation of Borrmann type II from III. The study provided a new view for specific Borrmann classification in clinical practice.
WOS关键词RADIOMIC NOMOGRAM ; IV ; CT ; CLASSIFIERS ; INFORMATION ; PROGNOSIS ; SELECTION
资助项目National Key R&D Program of China[2017YFA0205200] ; 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[62027901] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81930053] ; National Natural Science Foundation of China[81527805] ; Beijing Natural Science Foundation[L182061] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB 38040200] ; Chinese Academy of Sciences[GJJSTD20170004] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005] ; Youth Innovation Promotion Association CAS[2017175] ; Project of High-Level Talents Team Introduction in Zhuhai City[HLHPTP201703]
WOS研究方向Radiology, Nuclear Medicine & Medical Imaging
语种英语
WOS记录号WOS:000680934900001
出版者WILEY
资助机构National Key R&D Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Strategic Priority Research Program of Chinese Academy of Sciences ; Chinese Academy of Sciences ; Youth Innovation Promotion Association CAS ; Project of High-Level Talents Team Introduction in Zhuhai City
源URL[http://ir.ia.ac.cn/handle/173211/45628]  
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Zhou, Junlin; Shan, Xiuhong; Tian, Jie
作者单位1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, CAS Key Lab Mol Imaging,Beijing Key Lab Mol Imagi, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
3.Lanzhou Univ, Hosp 2, Dept Radiol, Lanzhou 730030, Gansu, Peoples R China
4.Jiangsu Univ, Affiliated Renmin Hosp, Dept Radiol, Zhenjiang 212002, Jiangsu, Peoples R China
5.Beihang Univ, Sch Med & Engn, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing, Peoples R China
6.Xidian Univ, Sch Life Sci & Technol, Engn Res Ctr Mol & Neuro Imaging, Minist Educ, Xian, Shaanxi, Peoples R China
7.Beihang Univ, Minist Ind & Informat Technol, Key Lab Big Data Based Precis Med, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Siwen,Dong, Di,Zhang, Wenjuan,et al. Specific Borrmann classification in advanced gastric cancer by an ensemble multilayer perceptron network: a multicenter research[J]. MEDICAL PHYSICS,2021:12.
APA Wang, Siwen.,Dong, Di.,Zhang, Wenjuan.,Hu, Hui.,Li, Hailin.,...&Tian, Jie.(2021).Specific Borrmann classification in advanced gastric cancer by an ensemble multilayer perceptron network: a multicenter research.MEDICAL PHYSICS,12.
MLA Wang, Siwen,et al."Specific Borrmann classification in advanced gastric cancer by an ensemble multilayer perceptron network: a multicenter research".MEDICAL PHYSICS (2021):12.

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