Multiparametric MRI-based radiomics analysis for the prediction of breast tumor regression patterns after neoadjuvant chemotherapy

doi:10.1016/j.tranon.2020.100831

	Multiparametric MRI-based radiomics analysis for the prediction of breast tumor regression patterns after neoadjuvant chemotherapy
	Zhuang, Xiaosheng 1,2; Chen, Chi 3,4; Liu, Zhenyu4,5 ; Zhang, Liulu 1; Zhou, Xuezhi 4,6; Cheng, Minyi 1; Ji, Fei 1; Zhu, Teng 1; Lei, Chuqian 1,7; Zhang, Junsheng 1,2; Jiang, Jingying 8,9; Tian, Jie 4,5,6,8,9; Wang, Kun 1
发表期刊	TRANSLATIONAL ONCOLOGY
ISSN	1936-5233
	2020-11-01
卷号	13 期号:11 页码:8
通讯作者	Jiang, Jingying(jingyingjiang@buaa.edu.cn) ; Tian, Jie(jie.tian@ia.ac.cn) ; Wang, Kun(gzwangkun@126.com)
摘要	Objectives: Breast cancers show different regression patterns after neoadjuvant chemotherapy. Certain regression patterns are associated with more reliable margins in breast-conserving surgery. Our study aims to establish a nomogram based on radiomic features and clinicopathological factors to predict regression patterns in breast cancer patients. Methods: We retrospectively reviewed 144 breast cancer patients who received neoadjuvant chemotherapy and underwent definitive surgery in our center from January 2016 to December 2019. Tumor regression patterns were categorized as type 1 (concentric regression + pCR) and type 2 (multifocal residues + SD + PD) based on pathological results. We extracted 1158 multidimensional features from 2 sequences of MRI images. After feature selection, machine learning was applied to construct a radiomic signature. Clinical characteristics were selected by backward stepwise selection. The combined prediction model was built based on both the radiomic signature and clinical factors. The predictive performance of the combined prediction model was evaluated. Results: Two radiomic features were selected for constructing the radiomic signature. Combined with two significant clinical characteristics, the combined prediction model showed excellent prediction performance, with an area under the receiver operating characteristic curve of 0.902 (95% confidence interval 0.8343-0.9701) in the primary cohort and 0.826 (95% confidence interval 0.6774-0.9753) in the validation cohort. Conclusions: Our study established a unique model combining a radiomic signature and clinicopathological factors to predict tumor regression patterns prior to the initiation of NAC. The early prediction of type 2 regression offers the opportunity to modify preoperative treatments or aids in determining surgical options.
DOI	10.1016/j.tranon.2020.100831
关键词[WOS]	PATHOLOGICAL COMPLETE RESPONSE ; CANCER PATIENTS ; PROGNOSIS ; THERAPY
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[81871513] ; National Natural Science Foundation of China[81922040] ; National Natural Science Foundation of China[81930053] ; National Natural Science Foundation of China[81971656] ; Beijing Natural Science Foundation[7182109] ; Beijing Natural Science Foundation[7202105] ; National Key R&D Program of China[2017YFA0205200] ; Youth Innovation Promotion Association CAS[2019136] ; Natural Science Foundation of Guangdong Province, China[2017A030313882l] ; CSCO-Constant Rui Tumor Research Fund, China[Y-HR2016-067]
项目资助者	National Natural Science Foundation of China ; Beijing Natural Science Foundation ; National Key R&D Program of China ; Youth Innovation Promotion Association CAS ; Natural Science Foundation of Guangdong Province, China ; CSCO-Constant Rui Tumor Research Fund, China
WOS研究方向	Oncology
WOS类目	Oncology
WOS记录号	WOS:000573633100011
出版者	ELSEVIER SCIENCE INC
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：25[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/173211/42036
专题	中国科学院分子影像重点实验室
通讯作者	Jiang, Jingying; Tian, Jie; Wang, Kun
作者单位	1.Guangdong Acad Med Sci, Guangdong Prov Peoples Hosp, Canc Ctr, Dept Breast Canc, 106 Zhongshan ER Lu, Guangzhou 510080, Peoples R China 2.Shantou Univ Med Coll, Shantou 515041, Guangdong, Peoples R China 3.Beihang Univ, Sch Biol Sci & Med Engn, Beijing 100191, Peoples R China 4.Chinese Acad Sci, Inst Automat, Key Lab Mol Imaging, Beijing 100190, Peoples R China 5.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100080, Peoples R China 6.Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Sch Life Sci & Technol, Minist Educ, Xian 710126, Shaanxi, Peoples R China 7.Southern Med Univ, Sch Clin Med 2, Guangzhou 510515, Peoples R China 8.Beihang Univ, Sch Med & Engn, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing 100191, Peoples R China 9.Beihang Univ, Minist Ind & Informat Technol, Key Lab Big Data Based Precis Med, Beijing 100191, Peoples R China
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Zhuang, Xiaosheng,Chen, Chi,Liu, Zhenyu,et al. Multiparametric MRI-based radiomics analysis for the prediction of breast tumor regression patterns after neoadjuvant chemotherapy[J]. TRANSLATIONAL ONCOLOGY,2020,13(11):8.
APA	Zhuang, Xiaosheng.,Chen, Chi.,Liu, Zhenyu.,Zhang, Liulu.,Zhou, Xuezhi.,...&Wang, Kun.(2020).Multiparametric MRI-based radiomics analysis for the prediction of breast tumor regression patterns after neoadjuvant chemotherapy.TRANSLATIONAL ONCOLOGY,13(11),8.
MLA	Zhuang, Xiaosheng,et al."Multiparametric MRI-based radiomics analysis for the prediction of breast tumor regression patterns after neoadjuvant chemotherapy".TRANSLATIONAL ONCOLOGY 13.11(2020):8.