CASIA OpenIR
Noninvasive Prediction of High-Grade Prostate Cancer via Biparametric MRI Radiomics
Gong, Lixin1,2,3; Xu, Min4; Fang, Mengjie3,5; Zou, Jian6; Yang, Shudong7; Yu, Xinyi4; Xu, Dandan4; Zhou, Lijuan4; Li, Hailin3; He, Bingxi3,5; Wang, Yan4; Fang, Xiangming4; Dong, Di3,5; Tian, Jie1,2,3,8
Source PublicationJOURNAL OF MAGNETIC RESONANCE IMAGING
ISSN1053-1807
2020-03-25
Pages8
Corresponding AuthorFang, Xiangming(xiangming_fang@njmu.edu.cn) ; Dong, Di(di.dong@ia.ac.cn) ; Tian, Jie(jie.tian@ia.ac.cn)
AbstractBackground Gleason score (GS) is a histologic prognostic factor and the basis of treatment decision-making for prostate cancer (PCa). Treatment regimens between lower-grade (GS <= 7) and high-grade (GS >7) PCa differ largely and have great effects on cancer progression. Purpose To investigate the use of different sequences in biparametric MRI (bpMRI) of the prostate gland for noninvasively distinguishing high-grade PCa. Study Type Retrospective. Population In all, 489 patients (training cohort: N = 326; test cohort: N = 163) with PCa between June 2008 and January 2018. Field Strength/Sequence 3.0T, pelvic phased-array coils, bpMRI including T-2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI); apparent diffusion coefficient map extracted from DWI. Assessment The whole prostate gland was delineated. Radiomic features were extracted and selected using the Kruskal-Wallis test, the minimum redundancy-maximum relevance, and the sequential backward elimination algorithm. Two single-sequence radiomic (T2WI, DWI) and two combined (T2WI-DWI, T2WI-DWI-Clinic) models were respectively constructed and validated via logistic regression. Statistical Tests The Kruskal-Wallis test and chi-squared test were utilized to evaluate the differences among variable groups. P < 0.05 determined statistical significance. The area under the receiver operating characteristic curve (AUC), specificity, sensitivity, and accuracy were used to evaluate model performance. The Delong test was conducted to compare the differences between the AUCs of all models. Result All radiomic models showed significant (P < 0.001) predictive performances. Between the single-sequence radiomic models, the DWI model achieved the most encouraging results, with AUCs of 0.801 and 0.787 in the training and test cohorts, respectively. For the combined models, the T2WI-DWI models acquired an AUC of 0.788, which was almost the same with DWI in the test cohort, and no significant difference was found between them (training cohort: P = 0.199; test cohort: P = 0.924). Data Conclusion Radiomics based on bpMRI can noninvasively identify high-grade PCa before the operation, which is helpful for individualized diagnosis of PCa. Level of Evidence 4 Technical Efficacy Stage 2
Keywordprostate cancer radiomics Gleason score biparametric MRI
DOI10.1002/jmri.27132
WOS KeywordRADICAL PROSTATECTOMY ; ANTIGEN ; TRANSITION ; FEATURES ; VOLUMES ; SYSTEM
Indexed BySCI
Language英语
Funding ProjectNational Key R&D Program of China[2017YFC1308700] ; National Key R&D Program of China[2017YFA0205200] ; National Key R&D Program of China[2017YFC1309100] ; National Key R&D Program of China[2017YFC0114300] ; National Natural Science Foundation of China[91959130] ; National Natural Science Foundation of China[81971776] ; 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[81671851] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[81271629] ; Beijing Natural Science Foundation[L182061] ; Bureau of International Cooperation of Chinese Academy of Sciences[173211KYSB20160053] ; Instrument Developing Project of the Chinese Academy of Sciences[YZ201502] ; Youth Innovation Promotion Association CAS[2017175]
Funding OrganizationNational Key R&D Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Bureau of International Cooperation of Chinese Academy of Sciences ; Instrument Developing Project of the Chinese Academy of Sciences ; Youth Innovation Promotion Association CAS
WOS Research AreaRadiology, Nuclear Medicine & Medical Imaging
WOS SubjectRadiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000521407200001
PublisherWILEY
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/38607
Collection中国科学院自动化研究所
Corresponding AuthorFang, Xiangming; Dong, Di; Tian, Jie
Affiliation1.Northeastern Univ, Coll Med, Shenyang, Peoples R China
2.Northeastern Univ, Biol Informat Engn Sch, Shenyang, Peoples R China
3.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China
4.Nanjing Med Univ, Wuxi Peoples Hosp, Imaging Ctr, Wuxi, Jiangsu, Peoples R China
5.Univ Chinese Acad Sci, Beijing, Peoples R China
6.Nanjing Med Univ, Wuxi Peoples Hosp, Ctr Clin Res, Wuxi, Jiangsu, Peoples R China
7.Nanjing Med Univ, Wuxi Peoples Hosp, Dept Pathol, Wuxi, Jiangsu, Peoples R China
8.Beihang Univ, Sch Med, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Gong, Lixin,Xu, Min,Fang, Mengjie,et al. Noninvasive Prediction of High-Grade Prostate Cancer via Biparametric MRI Radiomics[J]. JOURNAL OF MAGNETIC RESONANCE IMAGING,2020:8.
APA Gong, Lixin.,Xu, Min.,Fang, Mengjie.,Zou, Jian.,Yang, Shudong.,...&Tian, Jie.(2020).Noninvasive Prediction of High-Grade Prostate Cancer via Biparametric MRI Radiomics.JOURNAL OF MAGNETIC RESONANCE IMAGING,8.
MLA Gong, Lixin,et al."Noninvasive Prediction of High-Grade Prostate Cancer via Biparametric MRI Radiomics".JOURNAL OF MAGNETIC RESONANCE IMAGING (2020):8.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Gong, Lixin]'s Articles
[Xu, Min]'s Articles
[Fang, Mengjie]'s Articles
Baidu academic
Similar articles in Baidu academic
[Gong, Lixin]'s Articles
[Xu, Min]'s Articles
[Fang, Mengjie]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Gong, Lixin]'s Articles
[Xu, Min]'s Articles
[Fang, Mengjie]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.