The potential of prostate gland radiomic features in identifying the Gleason score

doi:10.1016/j.compbiomed.2022.105318

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

	The potential of prostate gland radiomic features in identifying the Gleason score
	Gong, Lixin 5,6; Xu, Min 4; Fang, Mengjie 3,5; He, Bingxi 2,5; Li, Hailin 2,5; Fang, Xiangming 4; Dong, Di 3,5; Tian, Jie 1,2,5,6
刊名	COMPUTERS IN BIOLOGY AND MEDICINE
	2022-05-01
卷号	144 页码:11
关键词	MRI Radiomics Prostate cancer Gleason score Gland segmentation
ISSN号	0010-4825
DOI	10.1016/j.compbiomed.2022.105318
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 one of the most critical predictors of diagnosing prostate cancer (PCa). The prostate gland, including both lesions and their microenvironment, may contain more comprehensive information about the PCa. We aimed to investigate the potential of prostate gland radiomic features in identifying Gleason scores (GS) < 7, = 7, and > 7. Methods: We retrospectively examined preoperative magnetic resonance imaging (MRI) results, clinical data, and postoperative pathological findings from 489 PCa patients. The three-dimensional (3D) and two-dimensional (2D) radiomic features were extracted from the manually segmented 3D prostate gland and its maximum 2D layer on MRI, respectively. Significant features were selected, and sequence signatures were then developed via multi-class linear regression (MLR) accordingly. Subsequently, 2D and 3D radiomic models were constructed by applying MLR to the combination of the sequence signatures, respectively. The stability of the significant features was discussed by their average ranking in the other 30 random cohorts. Based on our distance matrix algorithm, we generated different regions of interest to simulate the manual segmentation biases and discuss the model's tolerance to them. Results: Our 2D model reached a C-index of 0.728 and an average area under the receiver operating characteristic curve of 0.794 in the validation cohort. The corresponding key features were stable, with an average ranking of the top 8.352% in 30 random cohorts, and the model could tolerate a segmentation boundary deviation of 2 mm without significant performance degradation. Conclusion: 2D prostate-gland-MRI-based radiomic features showed stable potential in identifying GS.
资助项目	Strategic Priority Research Program of Chinese Academy of Sciences[XDB 38040200] ; National Natural Science Foundation of China[82022036] ; 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[62027901] ; National Natural Science Foundation of China[81930053] ; National Key R&D Program of China[2017YFA 0205200] ; Beijing Natural Science Foundation[L182061] ; Beijing Natural Science Foundation[Z20J00105] ; Chinese Academy of Sciences[GJJSTD2017 0004] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005] ; Project of High-Level Talents Team Introduction in Zhuhai City (Zhuhai)[HLHPTP201703] ; Youth Innovation Promotion Association CAS[2017175]
WOS关键词	CLINICALLY SIGNIFICANT ; MULTIPARAMETRIC MRI ; CANCER ; ANTIGEN ; GUIDELINES ; DIAGNOSIS ; SELECTION ; PATTERNS ; BIOPSY ; SYSTEM
WOS研究方向	Life Sciences & Biomedicine - Other Topics ; Computer Science ; Engineering ; Mathematical & Computational Biology
语种	英语
出版者	PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号	WOS:000806843400008
资助机构	Strategic Priority Research Program of Chinese Academy of Sciences ; National Natural Science Foundation of China ; National Key R&D Program of China ; Beijing Natural Science Foundation ; Chinese Academy of Sciences ; Project of High-Level Talents Team Introduction in Zhuhai City (Zhuhai) ; Youth Innovation Promotion Association CAS
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/49611]
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.Jinan Univ, Zhuhai Precis Med Ctr, Zhuhai Peoples Hosp, Zhuhai 519000, Peoples R China 2.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing 100191, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.Nanjing Med Univ, Wuxi Peoples Hosp, Imaging Ctr, Wuxi 214023, Jiangsu, Peoples R China 5.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 6.Northeastern Univ, Coll Med, Shenyang 110016, Peoples R China
推荐引用方式 GB/T 7714	Gong, Lixin,Xu, Min,Fang, Mengjie,et al. The potential of prostate gland radiomic features in identifying the Gleason score[J]. COMPUTERS IN BIOLOGY AND MEDICINE,2022,144:11.
APA	Gong, Lixin.,Xu, Min.,Fang, Mengjie.,He, Bingxi.,Li, Hailin.,...&Tian, Jie.(2022).The potential of prostate gland radiomic features in identifying the Gleason score.COMPUTERS IN BIOLOGY AND MEDICINE,144,11.
MLA	Gong, Lixin,et al."The potential of prostate gland radiomic features in identifying the Gleason score".COMPUTERS IN BIOLOGY AND MEDICINE 144(2022):11.