Multi-parametric MRI-based radiomics signature for discriminating between clinically significant and insignificant prostate cancer: Cross-validation of a machine learning method

doi:10.1016/j.ejrad.2019.03.010

	Multi-parametric MRI-based radiomics signature for discriminating between clinically significant and insignificant prostate cancer: Cross-validation of a machine learning method
	Min, Xiangde 1; Li, Min 2; Dong, Di3,4 ; Feng, Zhaoyan 1; Zhang, Peipei 1; Ke, Zan 1; You, Huijuan 1; Han, Fangfang 2; Ma, He 2; Tian, Jie3,4,5
刊名	EUROPEAN JOURNAL OF RADIOLOGY
	2019-06-01
卷号	115 页码:16-21
关键词	Magnetic resonance imaging Prostatic neoplasms Neoplasm grading Radiomics
ISSN号	0720-048X
DOI	10.1016/j.ejrad.2019.03.010
通讯作者	Ma, He(mahe@bmie.neu.edu.cn) ; Tian, Jie(jie.tian@ia.ac.cn) ; Wang, Liang(wang6@tjh.tjmu.edu.cn)
英文摘要	Purpose: To evaluate the performance of a multi-parametric MRI (mp-MRI)-based radiomics signature for discriminating between clinically significant prostate cancer (csPCa) and insignificant PCa (ciPCa). Materials and methods: Two hundred and eighty patients with pathology-proven PCa were enrolled and were randomly divided into training and test cohorts. Eight hundred and nineteen radiomics features were extracted from mp-MRI for each patient. The minority group in the training cohort was balanced via the synthetic minority over-sampling technique (SMOTE) method. We used minimum-redundancy maximum-relevance (mRMR) selection and the LASSO algorithm for feature selection and radiomics signature building. The classification performance of the radiomics signature for csPCa and ciPCa was evaluated by receiver operating characteristic curve analysis in the training and test cohorts. Results: Nine features were selected for the radiomics signature building. Significant differences in the radiomics signature existed between the csPCa and ciPCa groups in both the training and test cohorts (p < 0.01 for both). The AUC, sensitivity and specificity of the radiomics signature were 0.872 (95% CI: 0.823-0.921), 0.883, and 0.753, respectively, in the training cohort, and 0.823 (95% CI: 0.669-0.976), 0.841, and 0.727, respectively, in the test cohort. Conclusion: Mp-MRI-based radiomics signature have the potential to noninvasively discriminate between csPCa and ciPCa.
资助项目	National Natural Science Foundation of China[81671656] ; National Natural Science Foundation of China[81801668] ; National Natural Science Foundation of China[81771924] ; National Natural Science Foundation of China[81501616] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81671854] ; Beijing Natural Science Foundation[L182061] ; National Key R&D Program of China[2017YFA0205200] ; National Key R&D Program of China[2017YFC1308700] ; National Key R&D Program of China[2017YFC1308701] ; National Key R&D Program of China[2017YFC1309100] ; National Key R&D Program of China[2016YFC0103803] ; Science and Technology Service Network Initiative of the Chinese Academy of Sciences[KFJ-SW-STS-160] ; Beijing Municipal Science and Technology Commission[Z171100000117023] ; Beijing Municipal Science and Technology Commission[Z161100002616022] ; Youth Innovation Promotion Association CAS
WOS关键词	OVERDIAGNOSIS ; EXPERIENCE ; FEATURES ; RISK
WOS研究方向	Radiology, Nuclear Medicine & Medical Imaging
语种	英语
出版者	ELSEVIER IRELAND LTD
WOS记录号	WOS:000467534200003
资助机构	National Natural Science Foundation of China ; Beijing Natural Science Foundation ; National Key R&D Program of China ; Science and Technology Service Network Initiative of the Chinese Academy of Sciences ; Beijing Municipal Science and Technology Commission ; Youth Innovation Promotion Association CAS
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/24569]
专题	中国科学院自动化研究所
通讯作者	Ma, He; Tian, Jie; Wang, Liang
作者单位	1.Huazhong Univ Sci & Technol, Tongji Med Coll, Tongji Hosp, Dept Radiol, 1095 Jie Fang Ave, Wuhan 430030, Hubei, Peoples R China 2.Northeastern Univ, Sino Dutch Biomed & Informat Engn Sch, Shenyang, Liaoning, Peoples R China 3.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China 4.Univ Chinese Acad Sci, Beijing, Peoples R China 5.Beihang Univ, Sch Med, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Min, Xiangde,Li, Min,Dong, Di,et al. Multi-parametric MRI-based radiomics signature for discriminating between clinically significant and insignificant prostate cancer: Cross-validation of a machine learning method[J]. EUROPEAN JOURNAL OF RADIOLOGY,2019,115:16-21.
APA	Min, Xiangde.,Li, Min.,Dong, Di.,Feng, Zhaoyan.,Zhang, Peipei.,...&Wang, Liang.(2019).Multi-parametric MRI-based radiomics signature for discriminating between clinically significant and insignificant prostate cancer: Cross-validation of a machine learning method.EUROPEAN JOURNAL OF RADIOLOGY,115,16-21.
MLA	Min, Xiangde,et al."Multi-parametric MRI-based radiomics signature for discriminating between clinically significant and insignificant prostate cancer: Cross-validation of a machine learning method".EUROPEAN JOURNAL OF RADIOLOGY 115(2019):16-21.