Multiparametric MRI Radiomic Model for Preoperative Predicting WHO/ISUP Nuclear Grade of Clear Cell Renal Cell Carcinoma

doi:10.1002/jmri.27182

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

	Multiparametric MRI Radiomic Model for Preoperative Predicting WHO/ISUP Nuclear Grade of Clear Cell Renal Cell Carcinoma
	Li, Qiong 4,5; Liu, Yu-jia 1,6; Dong, Di 1,6; Bai, Xu 5; Huang, Qing-bo 7; Guo, Ai-tao 3; Ye, Hui-yi 5; Tian, Jie 1,2; Wang, Hai-yi 5
刊名	JOURNAL OF MAGNETIC RESONANCE IMAGING
	2020-05-28
页码	10
关键词	renal cell carcinoma nuclear grade radiomic magnetic resonance imaging
ISSN号	1053-1807
DOI	10.1002/jmri.27182
通讯作者	Tian, Jie(jie.tian@ia.ac.cn) ; Wang, Hai-yi(wanghaiyi301@outlook.com)
英文摘要	Background Nuclear grade is of importance for treatment selection and prognosis in patients with clear cell renal cell carcinoma (ccRCC). Purpose To develop and validate an MRI-based radiomic model for preoperative predicting WHO/ISUP nuclear grade in ccRCC. Study Type Retrospective. Population In all, 379 patients with histologically confirmed ccRCC. Training cohort (n = 252) and validation cohort (n = 127) were randomly assigned. Field Strength/Sequence Pretreatment 3.0T renal MRI. Imaging sequences were fat-suppressed T2WI, contrast-enhanced T1WI, and diffusion weighted imaging. Assessment Three prediction models were developed using selected radiomic features, radiomic and clinicoradiologic characteristics, and a model containing only clinicoradiologic characteristics. Receiver operating characteristic (ROC) curves and area under the curve (AUC) were used to assess the predictive performance of these models in predicting high-grade ccRCC. Statistical Tests The least absolute shrinkage and selection operator (LASSO) and minimum redundancy maximum relevance (mRMR) method were used for the selection of radiomic features and clinicoradiologic characteristics, respectively. Multivariable logistic regression analysis was used to develop the radiomic signature of radiomic features and clinicoradiologic model of clinicoradiologic characteristics. Results The radiomic signature showed good performance in discriminating high-grade (grades 3 and 4) from low-grade (grades 1 and 2) ccRCC, with sensitivity, specificity, and AUC of 77.3%, 80.0%, and 0.842, respectively, in the validation cohort. The radiomic model, combining radiomic signature and clinicoradiologic characteristics, displayed good predictive ability for high-grade with sensitivity, specificity, and accuracy of 63.6%, 93.3%, and 88.2%, respectively, in the validation cohort. The radiomic model showed a significantly better performance than the clinicoradiologic model (P < 0.05). Data Conclusion Multiparametric MRI-based radiomic model can predict WHO/ISUP grade in patients with ccRCC with satisfying performance, and thus could help the physician to improve treatment decisions. Level of Evidence 3 Technical Efficacy Stage 2
资助项目	National Natural Science Foundation of China[81971580] ; 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[81930053] ; National Key R&D Program of China[2017YFA0205200] ; National Key R&D Program of China[2017YFC1308700] ; National Key R&D Program of China[2017YFC1309100] ; Beijing Natural Science Foundation[L182061] ; Medical Big Data Research and Development Project of Chinese PLA General Hospital[2018MBD-023] ; Youth Innovation Promotion Association CAS[2017175]
WOS关键词	CLINICAL-PRACTICE GUIDELINES ; CANCER ; CT ; CLASSIFICATION ; DIAGNOSIS ; FEATURES ; OUTCOMES ; MASSES
WOS研究方向	Radiology, Nuclear Medicine & Medical Imaging
语种	英语
出版者	WILEY
WOS记录号	WOS:000535717500001
资助机构	National Natural Science Foundation of China ; National Key R&D Program of China ; Beijing Natural Science Foundation ; Medical Big Data Research and Development Project of Chinese PLA General Hospital ; Youth Innovation Promotion Association CAS
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/39546]
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	Tian, Jie; Wang, Hai-yi
作者单位	1.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, 95 Zhongguancun East Rd, Beijing 100191, Peoples R China 2.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing, Peoples R China 3.Chinese Peoples Liberat Army Gen Hosp, Med Ctr 1, Dept Pathol, Beijing, Peoples R China 4.Tianjin Nankai Hosp, Tianjin Hosp Integrated Tradit Chinese & Western, Dept Radiol, Tianjin, Peoples R China 5.Chinese Peoples Liberat Army Gen Hosp, Med Ctr 1, Dept Radiol, 28 Fuxing Rd, Beijing 100853, Peoples R China 6.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 7.Chinese Peoples Liberat Army Gen Hosp, Med Ctr 1, Dept Urol, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Li, Qiong,Liu, Yu-jia,Dong, Di,et al. Multiparametric MRI Radiomic Model for Preoperative Predicting WHO/ISUP Nuclear Grade of Clear Cell Renal Cell Carcinoma[J]. JOURNAL OF MAGNETIC RESONANCE IMAGING,2020:10.
APA	Li, Qiong.,Liu, Yu-jia.,Dong, Di.,Bai, Xu.,Huang, Qing-bo.,...&Wang, Hai-yi.(2020).Multiparametric MRI Radiomic Model for Preoperative Predicting WHO/ISUP Nuclear Grade of Clear Cell Renal Cell Carcinoma.JOURNAL OF MAGNETIC RESONANCE IMAGING,10.
MLA	Li, Qiong,et al."Multiparametric MRI Radiomic Model for Preoperative Predicting WHO/ISUP Nuclear Grade of Clear Cell Renal Cell Carcinoma".JOURNAL OF MAGNETIC RESONANCE IMAGING (2020):10.