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

文献类型：期刊论文

作者Zhuang, Xiaosheng2,3; Chen, Chi4,5; Liu, Zhenyu5,6; Zhang, Liulu2; Zhou, Xuezhi5,7; Cheng, Minyi2; Ji, Fei2; Zhu, Teng2; Lei, Chuqian2,8; Zhang, Junsheng2,3
刊名TRANSLATIONAL ONCOLOGY
出版日期2020-11-01
卷号13期号:11页码:8
ISSN号1936-5233
DOI10.1016/j.tranon.2020.100831
通讯作者Jiang, Jingying(jingyingjiang@buaa.edu.cn) ; Tian, Jie(jie.tian@ia.ac.cn) ; Wang, Kun(gzwangkun@126.com)
英文摘要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.
WOS关键词PATHOLOGICAL COMPLETE RESPONSE ; CANCER PATIENTS ; PROGNOSIS ; THERAPY
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]
WOS研究方向Oncology
语种英语
出版者ELSEVIER SCIENCE INC
WOS记录号WOS:000573633100011
资助机构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
源URL[http://ir.ia.ac.cn/handle/173211/42035]  
专题自动化研究所_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.Beihang Univ, Sch Med & Engn, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing 100191, Peoples R China
2.Guangdong Acad Med Sci, Guangdong Prov Peoples Hosp, Canc Ctr, Dept Breast Canc, 106 Zhongshan ER Lu, Guangzhou 510080, Peoples R China
3.Shantou Univ Med Coll, Shantou 515041, Guangdong, Peoples R China
4.Beihang Univ, Sch Biol Sci & Med Engn, Beijing 100191, Peoples R China
5.Chinese Acad Sci, Inst Automat, Key Lab Mol Imaging, Beijing 100190, Peoples R China
6.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100080, Peoples R China
7.Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Sch Life Sci & Technol, Minist Educ, Xian 710126, Shaanxi, Peoples R China
8.Southern Med Univ, Sch Clin Med 2, Guangzhou 510515, Peoples R China
9.Beihang Univ, Minist Ind & Informat Technol, Key Lab Big Data Based Precis Med, Beijing 100191, Peoples R China
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.

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来源：自动化研究所

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