Machine Learning-Based Radiomics Nomogram Using Magnetic Resonance Images for Prediction of Neoadjuvant Chemotherapy Efficacy in Breast Cancer Patients

doi:10.3389/fonc.2020.01410

HFCAS OpenIR

	Machine Learning-Based Radiomics Nomogram Using Magnetic Resonance Images for Prediction of Neoadjuvant Chemotherapy Efficacy in Breast Cancer Patients
	Chen, Shujun 1,2,3; Shu, Zhenyu 4; Li, Yongfeng 1,2,5; Chen, Bo 1,2,6; Tang, Lirong 1,2,3; Mo, Wenju 1,2,5; Shao, Guoliang 1,2,3; Shao, Feng 1,2,7
	2020-08-13
发表期刊	FRONTIERS IN ONCOLOGY
ISSN	2234-943X
通讯作者	Shao, Guoliang(shaogl@zjcc.org.cn) ; Shao, Feng(shaofeng@zjcc.org.cn)
摘要	Purpose:The construction and validation of a radiomics nomogram based on machine learning using magnetic resonance image (MRI) for predicting the efficacy of neoadjuvant chemotherapy (NACT) in patients with breast cancer (BCa). Methods:This retrospective investigation consisted of 158 patients who were diagnosed with BCa and underwent MRI before NACT, of which 33 patients experienced pathological complete response (pCR) by the postoperative pathological examination. The patients with BCa were divided into the training set (n= 110) and test set (n= 48) randomly. The features were selected by the maximum relevance minimum redundancy (mRMR) and absolute shrinkage and selection operator (LASSO) algorithm in the training set. In return, the radiomics signature was established using machine learning. The predictive score of each patient was calculated using the radiomics signature formula. Finally, the predictive scores and clinical factors were used to perform the multivariate logistic regression and construct the nomogram. Receiver operating characteristics (ROC) analyses were used to assess and validate the diagnostic accuracy of the nomogram in the test set. Lastly, the usefulness of the nomogram was confirmed via decision curve analysis (DCA). Results:The radiomics signature was well-discriminated in the training set [AUC 0.835, specificity 71.32%, and sensitivity 82.61%], and test set (AUC 0.834, specificity 73.21%, and sensitivity 80%). Containing the radiomics signature and hormone status, the radiomics nomogram showed good calibration and discrimination in the training set [AUC 0.888, specificity 79.31%, and sensitivity 86.96%] and test set (AUC 0.879, specificity 82.19%, and sensitivity 83.57%). The decision curve indicated the clinical usefulness of our nomogram. Conclusion:Our radiomics nomogram showed good discrimination in patients with BCa who experience pCR after NACT. The model may aid physicians in predicting how specific patients may respond to BCa treatments in the future.
关键词	radiomics nomogram breast cancer neoadjuvant chemotherapy pathological complete response machine learning
DOI	10.3389/fonc.2020.01410
关键词[WOS]	TEXTURE ANALYSIS ; RECTAL-CANCER ; MRI ; PET/CT ; FEATURES ; MODELS
收录类别	SCI
语种	英语
资助项目	Social Development Project of Zhejiang Public Welfare Technology Application[LGF18H180006] ; Zhejiang Provincial Health and Health Commission General Project[2020KY068]
项目资助者	Social Development Project of Zhejiang Public Welfare Technology Application ; Zhejiang Provincial Health and Health Commission General Project
WOS研究方向	Oncology
WOS类目	Oncology
WOS记录号	WOS:000566226200001
出版者	FRONTIERS MEDIA SA
引用统计	被引频次：34[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.hfcas.ac.cn:8080/handle/334002/102884
专题	中国科学院合肥物质科学研究院
通讯作者	Shao, Guoliang; Shao, Feng
作者单位	1.Univ Chinese Acad Sci, Canc Hosp, Zhejiang Canc Hosp, Hangzhou, Peoples R China 2.Chinese Acad Sci, Inst Canc & Basic Med IBMC, Hangzhou, Peoples R China 3.Zhejiang Canc Hosp, Dept Radiol, Hangzhou, Peoples R China 4.Zhejiang Prov Peoples Hosp, Affiliated Peoples Hosp, Dept Radiol, Hangzhou Med Coll, Hangzhou, Peoples R China 5.Zhejiang Canc Hosp, Dept Breast Surg, Hangzhou, Peoples R China 6.Zhejiang Canc Hosp, Dept Pathol, Hangzhou, Peoples R China 7.Zhejiang Canc Hosp, Dept Gynecol Oncol, Hangzhou, Peoples R China
推荐引用方式 GB/T 7714	Chen, Shujun,Shu, Zhenyu,Li, Yongfeng,et al. Machine Learning-Based Radiomics Nomogram Using Magnetic Resonance Images for Prediction of Neoadjuvant Chemotherapy Efficacy in Breast Cancer Patients[J]. FRONTIERS IN ONCOLOGY,2020,10.
APA	Chen, Shujun.,Shu, Zhenyu.,Li, Yongfeng.,Chen, Bo.,Tang, Lirong.,...&Shao, Feng.(2020).Machine Learning-Based Radiomics Nomogram Using Magnetic Resonance Images for Prediction of Neoadjuvant Chemotherapy Efficacy in Breast Cancer Patients.FRONTIERS IN ONCOLOGY,10.
MLA	Chen, Shujun,et al."Machine Learning-Based Radiomics Nomogram Using Magnetic Resonance Images for Prediction of Neoadjuvant Chemotherapy Efficacy in Breast Cancer Patients".FRONTIERS IN ONCOLOGY 10(2020).