MRI-derived radiomics models for diagnosis, aggressiveness, and prognosis evaluation in prostate cancer

doi:10.1631/jzus.B2200619

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

	MRI-derived radiomics models for diagnosis, aggressiveness, and prognosis evaluation in prostate cancer
	Zhu, Xuehua 2; Shao, Lizhi 1; Liu, Zhenyu 1,3; Liu, Zenan 2; He, Jide 2; Liu, Jiangang 4,6; Ping, Hao 5; Lu, Jian 2
刊名	JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE B
	2023-08-01
卷号	24 期号:8 页码:663-681
关键词	Magnetic resonance imaging (MRI) Radiomics Prostate cancer Predictive model
ISSN号	1673-1581
DOI	10.1631/jzus.B2200619
通讯作者	Ping, Hao(pinghaotrh@ccmu.edu.cn) ; Lu, Jian(lujian@bjmu.edu.cn)
英文摘要	Prostate cancer (PCa) is a pernicious tumor with high heterogeneity, which creates a conundrum for making a precise diagnosis and choosing an optimal treatment approach. Multiparametric magnetic resonance imaging (mp-MRI) with anatomical and functional sequences has evolved as a routine and significant paradigm for the detection and characterization of PCa. Moreover, using radiomics to extract quantitative data has emerged as a promising field due to the rapid growth of artificial intelligence (AI) and image data processing. Radiomics acquires novel imaging biomarkers by extracting imaging signatures and establishes models for precise evaluation. Radiomics models provide a reliable and noninvasive alternative to aid in precision medicine, demonstrating advantages over traditional models based on clinicopathological parameters. The purpose of this review is to provide an overview of related studies of radiomics in PCa, specifically around the development and validation of radiomics models using MRI-derived image features. The current landscape of the literature, focusing mainly on PCa detection, aggressiveness, and prognosis evaluation, is reviewed and summarized. Rather than studies that exclusively focus on image biomarker identification and method optimization, models with high potential for universal clinical implementation are identified. Furthermore, we delve deeper into the critical concerns that can be addressed by different models and the obstacles that may arise in a clinical scenario. This review will encourage researchers to design models based on actual clinical needs, as well as assist urologists in gaining a better understanding of the promising results yielded by radiomics.
资助项目	Beijing Natural Science Foundation[Z200027] ; Beijing Natural Science Foundation[L212051] ; Cohort Construction Project of Peking University Third Hospital[BYSYDL2021012] ; Medicine-X Project of Peking University Health Science Center[BMU2022MX014] ; National Natural Science Foundation of China[61871004]
WOS关键词	RISK-ASSESSMENT SCORE ; RADICAL PROSTATECTOMY ; BIOCHEMICAL RECURRENCE ; CLINICALLY SIGNIFICANT ; PREOPERATIVE NOMOGRAM ; MULTIPARAMETRIC MRI ; EXTERNAL VALIDATION ; DISEASE RECURRENCE ; PATHOLOGICAL STAGE ; PREDICTION
WOS研究方向	Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Research & Experimental Medicine
语种	英语
出版者	ZHEJIANG UNIV PRESS
WOS记录号	WOS:001049245800001
资助机构	Beijing Natural Science Foundation ; Cohort Construction Project of Peking University Third Hospital ; Medicine-X Project of Peking University Health Science Center ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/54044]
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	Ping, Hao; Lu, Jian
作者单位	1.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China 2.Peking Univ Third Hosp, Dept Urol, Beijing 100191, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100080, Peoples R China 4.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Engn Med, Beijing 100191, Peoples R China 5.Capital Med Univ, Beijing Tongren Hosp, Dept Urol, Beijing 100730, Peoples R China 6.Beihang Univ, Key Lab Big Data Based Precis Med, Minist Ind & Informat Technol, Beijing 100191, Peoples R China
推荐引用方式 GB/T 7714	Zhu, Xuehua,Shao, Lizhi,Liu, Zhenyu,et al. MRI-derived radiomics models for diagnosis, aggressiveness, and prognosis evaluation in prostate cancer[J]. JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE B,2023,24(8):663-681.
APA	Zhu, Xuehua.,Shao, Lizhi.,Liu, Zhenyu.,Liu, Zenan.,He, Jide.,...&Lu, Jian.(2023).MRI-derived radiomics models for diagnosis, aggressiveness, and prognosis evaluation in prostate cancer.JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE B,24(8),663-681.
MLA	Zhu, Xuehua,et al."MRI-derived radiomics models for diagnosis, aggressiveness, and prognosis evaluation in prostate cancer".JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE B 24.8(2023):663-681.