Radiomics Based on MRI as a Biomarker to Guide Therapy by Predicting Upgrading of Prostate Cancer From Biopsy to Radical Prostatectomy

doi:10.1002/jmri.27138

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

	Radiomics Based on MRI as a Biomarker to Guide Therapy by Predicting Upgrading of Prostate Cancer From Biopsy to Radical Prostatectomy
	Zhang, Gu-mu-yang 2,3; Han, Yu-qi 4,5; Wei, Jing-wei 5; Qi, Ya-fei 2,3; Gu, Dong-sheng 5; Lei, Jing 2,3; Yan, Wei-gang 3,6; Xiao, Yu 7; Xue, Hua-dan 2,3; Feng, Feng 2,3
刊名	JOURNAL OF MAGNETIC RESONANCE IMAGING
	2020-03-17
页码	10
关键词	radiomics magnetic resonance imaging prostate cancer Gleason score
ISSN号	1053-1807
DOI	10.1002/jmri.27138
通讯作者	Sun, Hao(jinzy@pumch.cn) ; Jin, Zheng-yu(jinzy@pumch.cn) ; Tian, Jie(jie.tian@ia.ac.cn)
英文摘要	Background Biopsy Gleason score (GS) is crucial for prostate cancer (PCa) treatment decision-making. Upgrading in GS from biopsy to radical prostatectomy (RP) puts a proportion of patients at risk of undertreatment. Purpose To develop and validate a radiomics model based on multiparametric magnetic resonance imaging (mp-MRI) to predict PCa upgrading. Study Type Retrospective, radiomics. Population A total of 166 RP-confirmed PCa patients (training cohort,n =116; validation cohort,n =50) were included. Field Strength/Sequence 3.0T/T-2-weighted (T2W), apparent diffusion coefficient (ADC), and dynamic contrast enhancement (DCE) sequences. Assessment PI-RADSv2 score for each tumor was recorded. Radiomic features were extracted from T2W, ADC, and DCE sequences and Mutual Information Maximization criterion was used to identify the optimal features on each sequence. Multivariate logistic regression analysis was used to develop predictive models and a radiomics nomogram and their performance was evaluated. Statistical Tests Student'stor chi-square were used to assess the differences in clinicopathologic data between the training and validation cohorts. Receiver operating characteristic (ROC) curve analysis was performed and the area under the curve (AUC) was calculated. Results In PI-RADSv2 assessment, 67 lesions scored 5, 70 lesions scored 4, and 29 lesions scored 3. For each sequence, 4404 features were extracted and the top 20 best features were selected. The radiomics model incorporating signatures from the three sequences achieved better performance than any single sequence (AUC: radiomics model 0.868, T2W 0.700, ADC 0.759, DCE 0.726). The combined mode incorporating radiomics signature, clinical stage, and time from biopsy to RP outperformed the clinical model and radiomics model (AUC: combined model 0.910, clinical model 0.646, radiomics model 0.868). The nomogram showed good performance (AUC 0.910) and calibration (P-values: training cohort 0.624, validation cohort 0.294). Data Conclusion Radiomics based on mp-MRI has potential to predict upgrading of PCa from biopsy to RP. Level of Evidence 3 Technical Efficacy Stage 5
资助项目	National Natural Science Foundation of China[91859119] ; National Natural Science Foundation of China[81901742] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[81771924] ; National Public Welfare Basic Scientific Research Project of Chinese Academy of Medical Sciences[2018PT32003] ; National Public Welfare Basic Scientific Research Project of Chinese Academy of Medical Sciences[2019PT320008] ; Clinical and Translational Research Project of Chinese Academy of Medical Sciences[2019XK320028] ; National Key Research and Development Program of China[2016YFC0103803] ; National Key Research and Development Program of China[2016YFA0201401] ; National Key Research and Development Program of China[2016YFC0103702] ; National Key Research and Development Program of China[2016YFC0103001] ; National Key Research and Development Program of China[2017YFC1308700] ; National Key Research and Development Program of China[2017YFC1309100] ; National Key Research and Development Program of China[2017YFA0205200] ; Chinese Academy of Sciences[GJJSTD20170004] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005]
WOS关键词	TEXTURE ANALYSIS ; GLEASON SCORES ; FUSION BIOPSY ; AGGRESSIVENESS ; DIAGNOSIS ; FEATURES
WOS研究方向	Radiology, Nuclear Medicine & Medical Imaging
语种	英语
出版者	WILEY
WOS记录号	WOS:000564229700001
资助机构	National Natural Science Foundation of China ; National Public Welfare Basic Scientific Research Project of Chinese Academy of Medical Sciences ; Clinical and Translational Research Project of Chinese Academy of Medical Sciences ; National Key Research and Development Program of China ; Chinese Academy of Sciences
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/40549]
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	Sun, Hao; Jin, Zheng-yu; Tian, Jie
作者单位	1.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing, Peoples R China 2.Peking Union Med Coll, Dept Radiol, Peking Union Med Coll Hosp, Beijing, Peoples R China 3.Chinese Acad Med Sci, Beijing, Peoples R China 4.Xidian Univ, Sch Life Sci & Technol, Xian, Peoples R China 5.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China 6.Peking Union Med Coll, Dept Urol, Peking Union Med Coll Hosp, Beijing, Peoples R China 7.Peking Union Med Coll, Dept Pathol, Peking Union Med Coll Hosp, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Gu-mu-yang,Han, Yu-qi,Wei, Jing-wei,et al. Radiomics Based on MRI as a Biomarker to Guide Therapy by Predicting Upgrading of Prostate Cancer From Biopsy to Radical Prostatectomy[J]. JOURNAL OF MAGNETIC RESONANCE IMAGING,2020:10.
APA	Zhang, Gu-mu-yang.,Han, Yu-qi.,Wei, Jing-wei.,Qi, Ya-fei.,Gu, Dong-sheng.,...&Tian, Jie.(2020).Radiomics Based on MRI as a Biomarker to Guide Therapy by Predicting Upgrading of Prostate Cancer From Biopsy to Radical Prostatectomy.JOURNAL OF MAGNETIC RESONANCE IMAGING,10.
MLA	Zhang, Gu-mu-yang,et al."Radiomics Based on MRI as a Biomarker to Guide Therapy by Predicting Upgrading of Prostate Cancer From Biopsy to Radical Prostatectomy".JOURNAL OF MAGNETIC RESONANCE IMAGING (2020):10.