CASIA OpenIR
Radiomics of Multiparametric MRI for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer: A Multicenter Study
Liu, Zhenyu1,2; Li, Zhuolin3; Qu, Jinrong4; Zhang, Renzhi5,6; Zhou, Xuezhi1,7; Li, Longfei1,8; Sun, Kai1,7; Tang, Zhenchao1; Jiang, Hui4; Li, Hailiang4; Xiong, Qianqian9,10; Ding, Yingying3; Zhao, Xinming5,6; Wang, Kun9,10; Liu, Zaiyi10,11; Tian, Jie1,2,7,12
Source PublicationCLINICAL CANCER RESEARCH
ISSN1078-0432
2019-06-15
Volume25Issue:12Pages:3538-3547
Corresponding AuthorWang, Kun(gzwangkun@126.com) ; Liu, Zaiyi(zyliu@163.com) ; Tian, Jie(tian@ieee.org)
AbstractPurpose: We evaluated the performance of the newly proposed radiomics of multiparametric MRI (RMM), developed and validated based on a multicenter dataset adopting a radiomic strategy, for pretreatment prediction of pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer. Experimental Design: A total of 586 potentially eligible patients were retrospectively enrolled from four hospitals (primary cohort and external validation cohort 1-3). Quantitative imaging features were extracted from T2-weighted imaging, diffusion-weighted imaging, and contrast-enhanced T1-weighted imaging before NAC for each patient. With features selected using a coarse to fine feature selection strategy, four radiomic signatures were constructed based on each of the three MRI sequences and their combination. RMM was developed based on the best radiomic signature incorporating with independent clinicopathologic risk factors. The performance of RMM was assessed with respect to its discrimination and clinical usefulness, and compared with that of clinical information-based prediction model. Results: Radiomic signature combining multiparametric MRI achieved an AUC of 0.79 (the highest among the four radiomic signatures). The signature further achieved good performances in hormone receptor-positive and HER2-negative group and triple-negative group. RMM yielded an AUC of 0.86, which was significantly higher than that of clinical model in two of the three external validation cohorts. Conclusions: The study suggested a possibility that RMM provided a potential tool to develop a model for predicting pCR to NAC in breast cancer.
DOI10.1158/1078-0432.CCR-18-3190
WOS KeywordMETAANALYSIS ; DIAGNOSIS ; IMAGES ; PET/CT
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[81772012] ; National Natural Science Foundation of China[81771912] ; National Natural Science Foundation of China[81871513] ; National Natural Science Foundation of China[81227901] ; Beijing Natural Science Foundation[7182109] ; National Key Research and Development Plan of China[2017YFA0205200] ; National Key Research and Development Plan of China[2017YFC1309100] ; Chinese Academy of Sciences[GJJSTD20170004] ; National Natural Science Foundation of China[81772012] ; National Natural Science Foundation of China[81771912] ; National Natural Science Foundation of China[81871513] ; National Natural Science Foundation of China[81227901] ; Beijing Natural Science Foundation[7182109] ; National Key Research and Development Plan of China[2017YFA0205200] ; National Key Research and Development Plan of China[2017YFC1309100] ; Chinese Academy of Sciences[GJJSTD20170004]
Funding OrganizationNational Natural Science Foundation of China ; Beijing Natural Science Foundation ; National Key Research and Development Plan of China ; Chinese Academy of Sciences
WOS Research AreaOncology
WOS SubjectOncology
WOS IDWOS:000472077200009
PublisherAMER ASSOC CANCER RESEARCH
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/27843
Collection中国科学院自动化研究所
Corresponding AuthorWang, Kun; Liu, Zaiyi; Tian, Jie
Affiliation1.Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
3.Kunming Med Univ, Yunnan Canc Hosp, Affiliated Hosp 3, Dept Radiol, Kunming, Yunnan, Peoples R China
4.Zhengzhou Univ, Henan Canc Hosp, Affiliated Canc Hosp, Dept Radiol, Zhengzhou, Henan, Peoples R China
5.Chinese Acad Med Sci, Natl Clin Res Ctr Canc, Canc Hosp, Dept Diagnost Radiol,Natl Canc Ctr, Beijing, Peoples R China
6.Peking Union Med Coll, Beijing, Peoples R China
7.Xidian Univ, Sch Life Sci & Technol, Minist Educ, Engn Res Ctr Mol & Neuro Imaging, Xian, Shaanxi, Peoples R China
8.Zhengzhou Univ, Collaborat Innovat Ctr Internet Healthcare, Zhengzhou, Henan, Peoples R China
9.Guangdong Prov Peoples Hosp, Dept Breast Canc, Guangzhou, Guangdong, Peoples R China
10.Guangdong Acad Med Sci, Guangzhou 510080, Guangdong, Peoples R China
11.Guangdong Prov Peoples Hosp, Dept Radiol, Guangzhou, Guangdong, Peoples R China
12.Beihang Univ, Sch Med, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Liu, Zhenyu,Li, Zhuolin,Qu, Jinrong,et al. Radiomics of Multiparametric MRI for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer: A Multicenter Study[J]. CLINICAL CANCER RESEARCH,2019,25(12):3538-3547.
APA Liu, Zhenyu.,Li, Zhuolin.,Qu, Jinrong.,Zhang, Renzhi.,Zhou, Xuezhi.,...&Tian, Jie.(2019).Radiomics of Multiparametric MRI for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer: A Multicenter Study.CLINICAL CANCER RESEARCH,25(12),3538-3547.
MLA Liu, Zhenyu,et al."Radiomics of Multiparametric MRI for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer: A Multicenter Study".CLINICAL CANCER RESEARCH 25.12(2019):3538-3547.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Liu, Zhenyu]'s Articles
[Li, Zhuolin]'s Articles
[Qu, Jinrong]'s Articles
Baidu academic
Similar articles in Baidu academic
[Liu, Zhenyu]'s Articles
[Li, Zhuolin]'s Articles
[Qu, Jinrong]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Liu, Zhenyu]'s Articles
[Li, Zhuolin]'s Articles
[Qu, Jinrong]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

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