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

文献类型：期刊论文

作者Min, Xiangde1; Li, Min2; Dong, Di3,4; Feng, Zhaoyan1; Zhang, Peipei1; Ke, Zan1; You, Huijuan1; Han, Fangfang2; Ma, He2; Tian, Jie3,4,5
刊名EUROPEAN JOURNAL OF RADIOLOGY
出版日期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
DOI10.1016/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.
WOS关键词OVERDIAGNOSIS ; EXPERIENCE ; FEATURES ; RISK
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研究方向Radiology, Nuclear Medicine & Medical Imaging
语种英语
WOS记录号WOS:000467534200003
出版者ELSEVIER IRELAND LTD
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
源URL[http://ir.ia.ac.cn/handle/173211/24569]  
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.

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来源：自动化研究所

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