Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
| 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
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| Source Publication | CLINICAL CANCER RESEARCH
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| ISSN | 1078-0432 |
| 2019-06-15 | |
| Volume | 25Issue:12Pages:3538-3547 |
| Corresponding Author | Wang, Kun(gzwangkun@126.com) ; Liu, Zaiyi(zyliu@163.com) ; Tian, Jie(tian@ieee.org) |
| Abstract | 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. |
| DOI | 10.1158/1078-0432.CCR-18-3190 |
| WOS Keyword | METAANALYSIS ; DIAGNOSIS ; IMAGES ; PET/CT |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 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] |
| Funding Organization | National Natural Science Foundation of China ; Beijing Natural Science Foundation ; National Key Research and Development Plan of China ; Chinese Academy of Sciences |
| WOS Research Area | Oncology |
| WOS Subject | Oncology |
| WOS ID | WOS:000472077200009 |
| Publisher | AMER ASSOC CANCER RESEARCH |
| Sub direction classification | 医学影像处理与分析 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/27843 |
| Collection | 中国科学院分子影像重点实验室 |
| Corresponding Author | Wang, Kun; Liu, Zaiyi; Tian, Jie |
| Affiliation | 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 |
| 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. |
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