CASIA OpenIR  > 中国科学院分子影像重点实验室
Multiparametric MRI-based radiomics analysis for the prediction of breast tumor regression patterns after neoadjuvant chemotherapy
Zhuang, Xiaosheng1,2; Chen, Chi3,4; Liu, Zhenyu4,5; Zhang, Liulu1; Zhou, Xuezhi4,6; Cheng, Minyi1; Ji, Fei1; Zhu, Teng1; Lei, Chuqian1,7; Zhang, Junsheng1,2; Jiang, Jingying8,9; Tian, Jie4,5,6,8,9; Wang, Kun1
Source PublicationTRANSLATIONAL ONCOLOGY
ISSN1936-5233
2020-11-01
Volume13Issue:11Pages:8
Corresponding AuthorJiang, Jingying(jingyingjiang@buaa.edu.cn) ; Tian, Jie(jie.tian@ia.ac.cn) ; Wang, Kun(gzwangkun@126.com)
AbstractObjectives: 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.
DOI10.1016/j.tranon.2020.100831
WOS KeywordPATHOLOGICAL COMPLETE RESPONSE ; CANCER PATIENTS ; PROGNOSIS ; THERAPY
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational 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 Research AreaOncology
WOS SubjectOncology
WOS IDWOS:000573633100011
PublisherELSEVIER SCIENCE INC
Sub direction classification医学影像处理与分析
Citation statistics
Cited Times:8[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/42036
Collection中国科学院分子影像重点实验室
Corresponding AuthorJiang, Jingying; Tian, Jie; Wang, Kun
Affiliation1.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
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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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