CASIA OpenIR
Multi-parametric MRI-based radiomics signature for discriminating between clinically significant and insignificant prostate cancer: Cross-validation of a machine learning method
Min, Xiangde1; Li, Min2; Dong, Di3,4; Feng, Zhaoyan1; Zhang, Peipei1; Ke, Zan1; You, Huijuan1; Han, Fangfang2; Ma, He2; Tian, Jie3,4,5; Wang, Liang1
Source PublicationEUROPEAN JOURNAL OF RADIOLOGY
ISSN0720-048X
2019-06-01
Volume115Pages:16-21
Corresponding AuthorMa, He(mahe@bmie.neu.edu.cn) ; Tian, Jie(jie.tian@ia.ac.cn) ; Wang, Liang(wang6@tjh.tjmu.edu.cn)
AbstractPurpose: 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.
KeywordMagnetic resonance imaging Prostatic neoplasms Neoplasm grading Radiomics
DOI10.1016/j.ejrad.2019.03.010
WOS KeywordOVERDIAGNOSIS ; EXPERIENCE ; FEATURES ; RISK
Indexed BySCI
Language英语
Funding ProjectNational 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
Funding OrganizationNational 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
WOS Research AreaRadiology, Nuclear Medicine & Medical Imaging
WOS SubjectRadiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000467534200003
PublisherELSEVIER IRELAND LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/24570
Collection中国科学院自动化研究所
Corresponding AuthorMa, He; Tian, Jie; Wang, Liang
Affiliation1.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
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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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