Radiomics of Multiparametric MRI for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer: A Multicenter Study

doi:10.1158/1078-0432.CCR-18-3190

	Radiomics of Multiparametric MRI for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer: A Multicenter Study
	Liu, Zhenyu 2,3; Li, Zhuolin 4; Qu, Jinrong 5; Zhang, Renzhi 6,7; Zhou, Xuezhi 2,8; Li, Longfei 2,9; Sun, Kai 2,8; Tang, Zhenchao 2; Jiang, Hui 5; Li, Hailiang 5
刊名	CLINICAL CANCER RESEARCH
	2019-06-15
卷号	25 期号:12 页码:3538-3547
ISSN号	1078-0432
DOI	10.1158/1078-0432.CCR-18-3190
通讯作者	Wang, Kun(gzwangkun@126.com) ; Liu, Zaiyi(zyliu@163.com) ; Tian, Jie(tian@ieee.org)
英文摘要	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.
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]
WOS关键词	METAANALYSIS ; DIAGNOSIS ; IMAGES ; PET/CT
WOS研究方向	Oncology
语种	英语
出版者	AMER ASSOC CANCER RESEARCH
WOS记录号	WOS:000472077200009
资助机构	National Natural Science Foundation of China ; Beijing Natural Science Foundation ; National Key Research and Development Plan of China ; Chinese Academy of Sciences
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/27842]
专题	中国科学院自动化研究所
通讯作者	Wang, Kun; Liu, Zaiyi; Tian, Jie
作者单位	1.Guangdong Prov Peoples Hosp, Dept Radiol, Guangzhou, Guangdong, Peoples R China 2.Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Beijing, Peoples R China 4.Kunming Med Univ, Yunnan Canc Hosp, Affiliated Hosp 3, Dept Radiol, Kunming, Yunnan, Peoples R China 5.Zhengzhou Univ, Henan Canc Hosp, Affiliated Canc Hosp, Dept Radiol, Zhengzhou, Henan, Peoples R China 6.Chinese Acad Med Sci, Natl Clin Res Ctr Canc, Canc Hosp, Dept Diagnost Radiol,Natl Canc Ctr, Beijing, Peoples R China 7.Peking Union Med Coll, Beijing, Peoples R China 8.Xidian Univ, Sch Life Sci & Technol, Minist Educ, Engn Res Ctr Mol & Neuro Imaging, Xian, Shaanxi, Peoples R China 9.Zhengzhou Univ, Collaborat Innovat Ctr Internet Healthcare, Zhengzhou, Henan, Peoples R China 10.Guangdong Prov Peoples Hosp, Dept Breast Canc, Guangzhou, Guangdong, 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.