CASIA OpenIR  > 中国科学院分子影像重点实验室
Exploring the predictive value of additional peritumoral regions based on deep learning and radiomics: A multicenter study
Wu, Xiangjun1,2; Dong, Di1,2; Zhang, Lu3; Fang, Mengjie1,2; Zhu, Yongbei1,2; He, Bingxi1,2; Ye, Zhaoxiang4; Zhang, Minming5; Zhang, Shuixing3; Tian, Jie2,6
Source PublicationMEDICAL PHYSICS
ISSN0094-2405
2021-03-30
Pages12
Abstract

Purpose The present study assessed the predictive value of peritumoral regions on three tumor tasks, and further explored the influence of peritumors with different sizes. Methods We retrospectively collected 333 samples of gastrointestinal stromal tumors from the Second Affiliated Hospital of Zhejiang University School of Medicine, and 183 samples of gastrointestinal stromal tumors from Tianjin Medical University Cancer Hospital. We also collected 211 samples of laryngeal carcinoma and 233 samples of nasopharyngeal carcinoma from the First Affiliated Hospital of Jinan University. The tasks of three tumor datasets were risk assessment (gastrointestinal stromal tumor), T3/T4 staging prediction (laryngeal carcinoma), and distant metastasis prediction (nasopharyngeal carcinoma), respectively. First, deep learning and radiomics were respectively used to construct peritumoral models, to study whether the peritumor had predictive value on three tumor datasets. Furthermore, we defined different sizes peritumors including fixed size (not considering tumor size) and adaptive size (according to average tumor radius) to explore the influence of peritumor of different sizes and types of tumors. Finally, we visualized the deep learning and radiomic models to observe the influence of the peritumor in three datasets. Results The performance of intra-peritumors are better than intratumors alone in three datasets. Specifically, the comparisons of area under receiver operating characteristic curve in the testing set between intra-peritumoral and intratumoral models are: 0.908 vs 0.873 (P value: 0.037) in gastrointestinal stromal tumor datasets, 0.796 vs 0.756 (P value: 0.188) in laryngeal carcinoma datasets and 0.660 vs 0.579 (P value: 0.431) in nasopharyngeal carcinoma datasets. Furthermore, for gastrointestinal stromal tumor datasets, deep learning is more stable to learn peritumors with both fixed and adaptive size than radiomics. For laryngeal carcinoma datasets, the intra-peritumoral radiomic model could make model performance more balanced. For nasopharyngeal carcinoma datasets, radiomics is also more suitable for modeling peritumors than deep learning. The size of the peritumor is critical in this task, and only the performance of 1.5 mm-4.5 mm peritumors is stable. Conclusions Our results indicate that peritumors have additional predictive value in three tumor datasets through deep learning or radiomics. The definitions of the peritumoral region and artificial intelligence method also have great influence on the performance of the peritumor.

Keyworddeep learning peritumor radiomics
DOI10.1002/mp.14767
Indexed BySCI
Language英语
Funding ProjectNational Key R&D Program of China[2017YFC1309100] ; National Key R&D Program of China[2017YFC1308700] ; National Key R&D Program of China[2017YFA0205200] ; National Key R&D Program of China[2017YFA0700401] ; 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[81527805] ; Beijing Natural Science Foundation[L182061] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB 38040200] ; Instrument Developing Project of the Chinese Academy of Sciences[YZ201502] ; Chinese Academy of Sciences[GJJSTD20170004] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005] ; Youth Innovation Promotion Association CAS[2017175]
Funding OrganizationNational 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 ; Instrument Developing Project of the Chinese Academy of Sciences ; Chinese Academy of Sciences ; Youth Innovation Promotion Association CAS
WOS Research AreaRadiology, Nuclear Medicine & Medical Imaging
WOS SubjectRadiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000634759600001
PublisherWILEY
Sub direction classification医学影像处理与分析
Citation statistics
Cited Times:7[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/44127
Collection中国科学院分子影像重点实验室
Corresponding AuthorYe, Zhaoxiang; Zhang, Minming; Zhang, Shuixing; Tian, Jie
Affiliation1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China
3.Jinan Univ, Affiliated Hosp 1, Dept Radiol, Guangzhou, Peoples R China
4.Tianjin Med Univ Canc Inst & Hosp, Dept Radiol, Tianjin, Peoples R China
5.Zhejiang Univ, Affiliated Hosp 2, Dept Radiol, Sch Med, Hangzhou, Peoples R China
6.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med & Engn, 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
Wu, Xiangjun,Dong, Di,Zhang, Lu,et al. Exploring the predictive value of additional peritumoral regions based on deep learning and radiomics: A multicenter study[J]. MEDICAL PHYSICS,2021:12.
APA Wu, Xiangjun.,Dong, Di.,Zhang, Lu.,Fang, Mengjie.,Zhu, Yongbei.,...&Tian, Jie.(2021).Exploring the predictive value of additional peritumoral regions based on deep learning and radiomics: A multicenter study.MEDICAL PHYSICS,12.
MLA Wu, Xiangjun,et al."Exploring the predictive value of additional peritumoral regions based on deep learning and radiomics: A multicenter study".MEDICAL PHYSICS (2021):12.
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