CASIA OpenIR  > 中国科学院分子影像重点实验室
Specific Borrmann classification in advanced gastric cancer by an ensemble multilayer perceptron network: a multicenter research
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
Source PublicationMEDICAL PHYSICS
ISSN0094-2405
2021-07-14
Volume48Issue:9Pages:5017-5028
Abstract

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.

Keywordadvanced gastric cancer Borrmann classification ensemble learning multilayer perceptron networks radiomics
DOI10.1002/mp.15094
WOS KeywordRADIOMIC NOMOGRAM ; IV ; CT ; CLASSIFIERS ; INFORMATION ; PROGNOSIS ; SELECTION
Indexed BySCI
Language英语
WOS Research AreaRadiology, Nuclear Medicine & Medical Imaging
WOS SubjectRadiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000680934900001
PublisherWILEY
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/45629
Collection中国科学院分子影像重点实验室
Corresponding AuthorZhou, Junlin; Shan, Xiuhong; Tian, Jie
Affiliation1.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
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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,2021,48(9):5017-5028.
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,48(9),5017-5028.
MLA Wang, Siwen,et al."Specific Borrmann classification in advanced gastric cancer by an ensemble multilayer perceptron network: a multicenter research".MEDICAL PHYSICS 48.9(2021):5017-5028.
Files in This Item: Download All
File Name/Size DocType Version Access License
MP2021-Wang-Specific(2265KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wang, Siwen]'s Articles
[Dong, Di]'s Articles
[Zhang, Wenjuan]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wang, Siwen]'s Articles
[Dong, Di]'s Articles
[Zhang, Wenjuan]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wang, Siwen]'s Articles
[Dong, Di]'s Articles
[Zhang, Wenjuan]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: MP2021-Wang-Specific Borrmann classification in advanced gastric cancer by an ensemble multilayer.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.