Noninvasive Prediction of High-Grade Prostate Cancer via Biparametric MRI Radiomics

doi:10.1002/jmri.27132

CORC > 自动化研究所 > 中国科学院自动化研究所 > 中国科学院分子影像重点实验室

	Noninvasive Prediction of High-Grade Prostate Cancer via Biparametric MRI Radiomics
	Gong, Lixin 1,2,8; Xu, Min 3; Fang, Mengjie 2,7; Zou, Jian 5; Yang, Shudong 6; Yu, Xinyi 3; Xu, Dandan 3; Zhou, Lijuan 3; Li, Hailin 2; He, Bingxi 2,7
刊名	JOURNAL OF MAGNETIC RESONANCE IMAGING
	2020-03-25
页码	8
关键词	prostate cancer radiomics Gleason score biparametric MRI
ISSN号	1053-1807
DOI	10.1002/jmri.27132
通讯作者	Fang, Xiangming(xiangming_fang@njmu.edu.cn) ; Dong, Di(di.dong@ia.ac.cn) ; Tian, Jie(jie.tian@ia.ac.cn)
英文摘要	Background 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
资助项目	National 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]
WOS关键词	RADICAL PROSTATECTOMY ; ANTIGEN ; TRANSITION ; FEATURES ; VOLUMES ; SYSTEM
WOS研究方向	Radiology, Nuclear Medicine & Medical Imaging
语种	英语
出版者	WILEY
WOS记录号	WOS:000521407200001
资助机构	National 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
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/38606]
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	Fang, Xiangming; Dong, Di; Tian, Jie
作者单位	1.Northeastern Univ, Coll Med, Shenyang, Peoples R China 2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China 3.Nanjing Med Univ, Wuxi Peoples Hosp, Imaging Ctr, Wuxi, Jiangsu, Peoples R China 4.Beihang Univ, Sch Med, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing, Peoples R China 5.Nanjing Med Univ, Wuxi Peoples Hosp, Ctr Clin Res, Wuxi, Jiangsu, Peoples R China 6.Nanjing Med Univ, Wuxi Peoples Hosp, Dept Pathol, Wuxi, Jiangsu, Peoples R China 7.Univ Chinese Acad Sci, Beijing, Peoples R China 8.Northeastern Univ, Biol Informat Engn Sch, Shenyang, Peoples R China
推荐引用方式 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.