中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 合肥物质科学研究院
  3. 中国科学院合肥物质科学研究院
Machine Learning-Based Radiomics Nomogram Using Magnetic Resonance Images for Prediction of Neoadjuvant Chemotherapy Efficacy in Breast Cancer Patients

文献类型：期刊论文

作者Chen, Shujun1,2,3; Shu, Zhenyu4; Li, Yongfeng1,2,5; Chen, Bo1,2,6; Tang, Lirong1,2,3; Mo, Wenju1,2,5; Shao, Guoliang1,2,3; Shao, Feng1,2,7
刊名FRONTIERS IN ONCOLOGY
出版日期2020-08-13
卷号10
关键词radiomics nomogram breast cancer neoadjuvant chemotherapy pathological complete response machine learning
ISSN号2234-943X
DOI10.3389/fonc.2020.01410
通讯作者Shao, Guoliang(shaogl@zjcc.org.cn) ; Shao, Feng(shaofeng@zjcc.org.cn)
英文摘要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.
WOS关键词TEXTURE ANALYSIS ; RECTAL-CANCER ; MRI ; PET/CT ; FEATURES ; MODELS
资助项目Social Development Project of Zhejiang Public Welfare Technology Application[LGF18H180006] ; Zhejiang Provincial Health and Health Commission General Project[2020KY068]
WOS研究方向Oncology
语种英语
WOS记录号WOS:000566226200001
出版者FRONTIERS MEDIA SA
资助机构Social Development Project of Zhejiang Public Welfare Technology Application ; Zhejiang Provincial Health and Health Commission General Project
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/102883]  
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).

入库方式： OAI收割

来源：合肥物质科学研究院

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。