Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
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 | |
发表期刊 | EUROPEAN JOURNAL OF RADIOLOGY |
ISSN | 0720-048X |
2019-06-01 | |
卷号 | 115页码:16-21 |
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) |
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. |
关键词 | Magnetic resonance imaging Prostatic neoplasms Neoplasm grading Radiomics |
DOI | 10.1016/j.ejrad.2019.03.010 |
关键词[WOS] | OVERDIAGNOSIS ; EXPERIENCE ; FEATURES ; RISK |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Youth Innovation Promotion Association CAS ; Beijing Municipal Science and Technology Commission[Z161100002616022] ; Beijing Municipal Science and Technology Commission[Z171100000117023] ; Science and Technology Service Network Initiative of the Chinese Academy of Sciences[KFJ-SW-STS-160] ; National Key R&D Program of China[2016YFC0103803] ; National Key R&D Program of China[2017YFC1309100] ; National Key R&D Program of China[2017YFC1308701] ; National Key R&D Program of China[2017YFC1308700] ; National Key R&D Program of China[2017YFA0205200] ; Beijing Natural Science Foundation[L182061] ; National Natural Science Foundation of China[81671854] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81501616] ; National Natural Science Foundation of China[81771924] ; National Natural Science Foundation of China[81801668] ; National Natural Science Foundation of China[81671656] ; 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 |
项目资助者 | 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 |
WOS研究方向 | Radiology, Nuclear Medicine & Medical Imaging |
WOS类目 | Radiology, Nuclear Medicine & Medical Imaging |
WOS记录号 | WOS:000467534200003 |
出版者 | ELSEVIER IRELAND LTD |
七大方向——子方向分类 | 医学影像处理与分析 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/24570 |
专题 | 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 |
通讯作者单位 | 中国科学院自动化研究所 |
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. |
条目包含的文件 | 条目无相关文件。 |
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。
修改评论