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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
发表期刊CLINICAL CANCER RESEARCH
ISSN1078-0432
2019-06-15
卷号25期号:12页码:3538-3547
通讯作者Wang, Kun(gzwangkun@126.com) ; Liu, Zaiyi(zyliu@163.com) ; Tian, Jie(tian@ieee.org)
摘要Purpose: 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
关键词[WOS]METAANALYSIS ; DIAGNOSIS ; IMAGES ; PET/CT
收录类别SCI
语种英语
资助项目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] ; 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]
项目资助者National Natural Science Foundation of China ; Beijing Natural Science Foundation ; National Key Research and Development Plan of China ; Chinese Academy of Sciences
WOS研究方向Oncology
WOS类目Oncology
WOS记录号WOS:000472077200009
出版者AMER ASSOC CANCER RESEARCH
七大方向——子方向分类医学影像处理与分析
引用统计
被引频次:243[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/27843
专题中国科学院分子影像重点实验室
通讯作者Wang, Kun; Liu, Zaiyi; Tian, Jie
作者单位1.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
推荐引用方式
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.
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